**5. Multipoint statistics**

The objective of this work is to build a reservoir model with multiple alternatives, thereby assessing uncertainty about the reservoir, integrating information from different sources obtained at different resolutions:

This last point has been possible using a variogram in algorithms such as DSS and Co-DSS, previously described, which allow integration of well and seismic data using a pixel-based approach. Those variogram based models are inadequate in integrating geological concepts since the variogram is too limited in capturing complex geological heterogeneity and is a two-point statistics that poorly reflects the geologist's prior conceptual vision of the reservoir architecture, e.g., sand channels as in [20].

136 New Technologies in the Oil and Gas Industry

as it happens in the standard methods;

model became more elaborated.

**5. Multipoint statistics**

obtained at different resolutions:

in the vertical direction;

layer scale to the basin scale.

full model.

**4.3. Remarks**

would be noticeable.

The influence of the wells is no longer visible because all data of wells are integrated in the

In spite of the scarcity of the log data, the proposed method achieved extremely good results. In case of data abundance, if the data is not well calibrated it could compromise the quality of the convergence and the influence of the not calibrated wells in the final model

In this case, both synthetic seismic data and acoustic impedance cube captured the main geologic features of these complex reservoirs, noticeable in the correlation coefficients between the seismic and the synthetic amplitudes. The quality of the seismic data takes a minor role since the method overcomes the situation of imposition of artificial correlations

Since the co-simulation of the impedances uses a local coefficient correlation, it is possible to

The uncertainty of the seismic acoustic impedance could be used to access the uncertainty

Tests prove that the variation of the final correlation coefficient is about 2% with the modification of the initial seed, it means that others parameters such as the number of layers and the size of it, as other parameters can be optimized to produce better results. But these results are more difficult to reach when the complexity of the geology and the structural

To handle different geology scenarios such channels or specific shape reservoirs, an adapted

The objective of this work is to build a reservoir model with multiple alternatives, thereby assessing uncertainty about the reservoir, integrating information from different sources

Seismic data which is exhaustive but of much lower resolution, in the scale of 10's feet

Conceptual geological models, which could quantify reservoir heterogeneity from the

This last point has been possible using a variogram in algorithms such as DSS and Co-DSS, previously described, which allow integration of well and seismic data using a pixel-based approach. Those variogram based models are inadequate in integrating geological concepts since the variogram is too limited in capturing complex geological heterogeneity and is a

Well-data which is sparse but of high resolution, in the scale of a foot;

compute the local uncertainty associated to the seismic acoustic impedances;

associated with the porosity model, as presented in [24].

approach is proposed in the next part of this work.

Integration of geological information beyond two-point variogram reproduction becomes critical in order to quantify more accurately heterogeneity and assess realistically the uncertainty of model description.

A solution was initiated by practitioners from the oil industry in Norway, where Boolean objects-based algorithms were introduced in the late 1980's to simulate random geometry as in [25,26]. These parametric shapes, such as sinusoidal channel or ellipsoidal lenses, are placed in simulated volume. Through an iterative process this shape is changed, displaced or removed to fit the conditioning statistics and local data.

The simulated objects finally resemble the geologist drawings or photographs of present day depositions

Passed the enthusiasm, the limitation of objects-based simulation algorithms became obvious, this iterative, perturbation type, algorithm for data conditioning did not converge in the presence of dense hard data or could not account for diverse data types, such as seismic used as soft data.

Also the limitation of not simulating continuous variables, time and CPU demanding in large 3D cases, enroll on the drawbacks of the methodology.

Strebelle in [20], following the works of Srivastava in [27] and Caers in [28] has proposed an alternative approach of Multipoint statistics that combines the easy conditioning of pixelbased algorithms with the ability to reproduce "shapes" of object-based techniques, without relying on excessive CPU demand.

Multipoint statistics uses a training image instead of a variogram to account for geological information. The training image describes the geometrical facies patterns believed to represent the subsurface.

Training images does not need to carry any local information of the actual reservoir, they only reflect a prior geological/structural concept.

Basically, multipoint statistics consists of extracting patterns from the training image, and anchoring them to local data, i.e. well logs and seismic data. Several training images corresponding to alternative geological interpretations can be used to account for the uncertainty about the reservoir architecture.

Training images, identical to the variogram, can be a questionable subject when the model to be generated has few hard data or no pre-conceptual model has been studied by geologists or geomodelers.

Using this new tool a new objective is to illustrate the multipoint statistical methodology to seismic inversion.

#### **5.1. Seismic inversion using multipoint statistics**

For the case study, the Stanford VI synthetic reservoir dataset described in [29], was used. This synthetic reservoir (figure 15), consists of meandering channels, sand vs mud system, and each facies were populated with rock impedance, using sequential simulation.

To mimic a seismic inversion, this high resolution rock impedance model were smoothed by a low-pass filter to obtain a typical acoustic impedance response and then convoluted to an amplitude model that will be considered as reference seismic amplitude.

**Figure 15.** Facies model (left), high resolution rock impedance (middle left), smothered rock impedance (middle right) and forwarded seismic synthetic amplitude (right)

#### **5.2. Probabilistic approach**

For comparison of the methodology, is presented the application of a traditional probabilistic modeling on the Stanford VI acoustic impedance, i.e. calibrate acoustic impedance into a 3D facies probability cube, and then use it as soft data constraint for multiple-point geostatistical simulation.

Several techniques exist to calibrate one or more seismic attributes with well data, such as PCA, ANN, etc., one use a simple Bayes' approach (equation 7) as documented in [30]. In a Bayesian method one uses the histogram of impedance for each facies denoted as:

$$P\left(A\mathbf{I} \mid \text{facies}\_f\right), f = \{\mathbf{1}, \mathbf{2}, \ldots\} \tag{7}$$

Using Bayes' rule one can calculate the probability for each facies for given impedance values as (equation 8):

$$P\left(\text{facies}\_f \mid AI\right) = \frac{P(AI \mid \text{facies}\_f) \bullet P(\text{facies}\_f)}{P(AI)}\tag{8}$$

where P(faciesf) is the global proportion of faciesf and P(AI) is derived from the histogram of the impedance values. The final result is a cube of probabilities for each facies type based on the acoustic impedance cube. In figure 16, one can see the result of the application of this method to the case study data.

This probability data can be used by different geostatistical algorithms to create a facies model. In our case the simulated facies are obtained with the multi-point method snesim, conditioned to the probability cubes previously calculated, to the training image and to the rotation and affinity cubes.

**5.2. Probabilistic approach**

values as (equation 8):

method to the case study data.

rotation and affinity cubes.

multiple-point geostatistical simulation.

**5.1. Seismic inversion using multipoint statistics**

(middle right) and forwarded seismic synthetic amplitude (right)

For the case study, the Stanford VI synthetic reservoir dataset described in [29], was used. This synthetic reservoir (figure 15), consists of meandering channels, sand vs mud system,

To mimic a seismic inversion, this high resolution rock impedance model were smoothed by a low-pass filter to obtain a typical acoustic impedance response and then convoluted to an

**Figure 15.** Facies model (left), high resolution rock impedance (middle left), smothered rock impedance

For comparison of the methodology, is presented the application of a traditional probabilistic modeling on the Stanford VI acoustic impedance, i.e. calibrate acoustic impedance into a 3D facies probability cube, and then use it as soft data constraint for

Several techniques exist to calibrate one or more seismic attributes with well data, such as PCA, ANN, etc., one use a simple Bayes' approach (equation 7) as documented in [30]. In a

Using Bayes' rule one can calculate the probability for each facies for given impedance

(| ) ( ) |

where P(faciesf) is the global proportion of faciesf and P(AI) is derived from the histogram of the impedance values. The final result is a cube of probabilities for each facies type based on the acoustic impedance cube. In figure 16, one can see the result of the application of this

This probability data can be used by different geostatistical algorithms to create a facies model. In our case the simulated facies are obtained with the multi-point method snesim, conditioned to the probability cubes previously calculated, to the training image and to the

*P AI facies P facies P facies AI P AI*

( )

*f f*

*P AI facies f* | , 1,2,... *f* (7)

(8)

Bayesian method one uses the histogram of impedance for each facies denoted as:

*f*

and each facies were populated with rock impedance, using sequential simulation.

amplitude model that will be considered as reference seismic amplitude.

**Figure 16.** Result of the Bayesian approach for the calculus of the probabilities cubes for case study and the simulated facies model.

To verify our hypothesis that this facies model does not match the field amplitude, the facies model is forwarded simulated to a synthetic amplitude dataset. We assumed the ideal situation where an exact forward model was available. Figure 17 confirm that the forward modeled amplitude does not match the field data. In fact, the co-located correlation coefficient is 0.20.

**Figure 17.** "Real" seismic amplitude (left) *vs* forward modeled amplitude derived from the simulated facies model using a traditional probabilistic approach (right).

#### **5.3. Algorithm description using multipoint statistics**

For the case of using multi-point statistics, the seismic inversion algorithm has gone through some changes, manly caused by the simulation algorithm that does not creates acoustic impedance models directly, but facies models, that can be populated with acoustic impedance values later.

So the method is initialized by simulating N facies models only conditioned to the wells and training images using snesim, in this case, additional channel azimuth and affinity (as interpreted from seismic or geological understanding) constraints are enforced (figure 18).

**Figure 18.** Initial simulations conditioned only to the training image and rotation/affinity.

The facies models are then populated with acoustic impedances, converted in synthetic seismograms and chose and keep the best "genes" from each of the images (see figure 19). In the next step there is also a modification, since one cannot use directly the correlation values in the snesim simulator. So a modification of the probability perturbation method as in [31] is used to create a probability for each facies.

This facies probabilities cube is then used as soft constraint to generate the next set of N cubes. This is done using Journel's tau-model as in [10], to integrate probabilistic information from various sources. The process converges as the previous algorithm and it can be summarized in the following steps:

As noted in previously algorithm, in step vi), in the very first steps of the iterative process, the probability data can not have the spatial continuity pattern of the primary simulated facies, as it results from a composition of different parts of a set of simulated images. As the process continues, that soft secondary image (probability cubes) tend to have the same spatial pattern of generated images by co-simulation, i.e. the imposed training image altered by the affinity and azimuth

#### **5.4. Co-generation of acoustic images with multipoint statistics**

140 New Technologies in the Oil and Gas Industry

Training Image

Rotation

Affinity

**Figure 18.** Initial simulations conditioned only to the training image and rotation/affinity.

is used to create a probability for each facies.

can be summarized in the following steps:

i. Generate a set of initial 3D images of facies by using snesim. ii. Populate the facies models with acoustic impedances.

seismic by computing, for example local correlation coefficients.

from acoustic impedances, with a known wavelet.

probability cube to each facies to be simulated.

The facies models are then populated with acoustic impedances, converted in synthetic seismograms and chose and keep the best "genes" from each of the images (see figure 19). In the next step there is also a modification, since one cannot use directly the correlation values in the snesim simulator. So a modification of the probability perturbation method as in [31]

N Simulated Facies

MPS

This facies probabilities cube is then used as soft constraint to generate the next set of N cubes. This is done using Journel's tau-model as in [10], to integrate probabilistic information from various sources. The process converges as the previous algorithm and it

iii. Create the synthetic seismogram of amplitudes, by convolving the reflectivity, derived

iv. Evaluate the match of the synthetic seismograms, of entire 3D image, and the real

v. Ranking the "best" images based on the match (e.g. the average value or a percentile of correlation coefficients for the entire image). From it, one selects the best parts (the columns or the horizons with the best correlation coefficient) of each image. Compose

one auxiliary image with the selected "best" parts, for the next simulation step. vi. Transform the cube with the best parts and the corresponded best values to a The major change made for the adaptation to MPS, is that the acoustic impedance values are estimated or populated in the simulated facies model. This can cause some reservations but if one considered that the main objective is comparing the reflection coefficients between the real and the simulated seismic amplitude, those reflection coefficients are more accentuated in the change of terrain type, and those can be considered a channel or crevasse. Inside the channels the rock type does not vary too much and that is visible in the seismic profiles by the presence of low amplitude seismic. So this difference in the algorithm can be considered a valid one.

The second difference is that to co-simulate a facies model, one can not use acoustic impedance values (a continuous variable) as soft data, so instead of choosing from the simulated acoustics model, the algorithm build the Best Facies cube from the simulated facies models (figure 19).

**Figure 19.** Process of creating "Best Mismatch cube" and "Best Facies cube".

As the methodology for acoustic impedance models, the process starts by analyzing each of the mismatch cubes that were created previously, and in each position of the cubes (x0) it compares which has the higher value of correlation.

The point set 1a and point set 1b are compared, from those is the 1a that has a higher value of correlation so, it is copied (1c) to the Best Mismatch cube.

At the same time, since the first simulation showed better coefficient of correlation (CC) with the real seismic, another cube is created with the correspondent facies values (1d).

The process follow to the next set of values from the CC cubes, and it is the same, comparison between the sets 2a and 2b, that in this case it is the second cube that has the higher value of cc (or lower mismatch), it is these values that are copied to the Best Mismatch cube (2c). The same is done from the facies cube of this simulation, copy of the facies values to the Best Facies (2d). This process continues until all the N simulated models have been analyzed and to all the values of the cubes.

Through this way two new cubes are created, one with the best genes from each facies model (Best Facies) generated previously and another cube with the confidence factor of each part (Best Mismatch). As noticed above, the "least mismatch cube" can be seen as a summary of the least mismatch of all N realizations. The "best facies cube" can be seen as facies model combined from all N facies models that best matches the seismic data. However the "best facies cube" does not have the same geological concept as the training image and may have various artifacts, since it is constructed by copying several sets from independently generated facies models.

The third difference is that, these two new cubes can not be used for the co-simulation of the new generation of facies models, but the "best facies cube" can be used indirectly to improve the existing N facies models.

In order to do this, one uses a modification of the probability perturbation method as in [24]. Caers' method was developed to solve non-linear inverse problems under a prior model constraint. It allows the conditioning of stochastic simulations to any type of non-linear data. The principle of this method relies on perturbing the probability models used to generate a chain of realizations that converge to match any type of data.

In this methodology the unknown pre-posterior probability – Prob(Aj | C) – is modeled using a single parameter model in the following equation 9:

$$P\left(A\_{\boldsymbol{j}} \mid \mathcal{C}\right) = P(I(\boldsymbol{u}\_{\boldsymbol{j}}) = 1 \mid \mathcal{C}) = (1 - r\_{\boldsymbol{c}}) \times i\_{\mathcal{B}}^{(0)}(\boldsymbol{u}\_{\boldsymbol{j}}) + r\_{\boldsymbol{c}} \times P(A\_{\boldsymbol{j}})\_{\boldsymbol{\iota}} \boldsymbol{j} = 1, \ldots, N \tag{9}$$

where A is unknown data, B is well data and previously simulated facies indicators and C will be the "best facies cube", rC is not dependent on uj and is between [0,1], and 0 , 1,..., *B j i jN* **u** is an initial realization conditioned to the B-data only.

According to the methodology proposed in this work, the equation 9 is adapted with rC now representing the mismatch between the field and synthetic seismic as summarized with the least-mismatch cube (correlation coefficient), and 0 *B i* corresponding to the presence of the facies with the least mismatch. This adaptation leads to the following equation 10:

142 New Technologies in the Oil and Gas Industry

compares which has the higher value of correlation.

have been analyzed and to all the values of the cubes.

independently generated facies models.

improve the existing N facies models.

0 , 1,..., *B j*

of correlation so, it is copied (1c) to the Best Mismatch cube.

As the methodology for acoustic impedance models, the process starts by analyzing each of the mismatch cubes that were created previously, and in each position of the cubes (x0) it

The point set 1a and point set 1b are compared, from those is the 1a that has a higher value

At the same time, since the first simulation showed better coefficient of correlation (CC) with the real seismic, another cube is created with the correspondent facies values (1d).

The process follow to the next set of values from the CC cubes, and it is the same, comparison between the sets 2a and 2b, that in this case it is the second cube that has the higher value of cc (or lower mismatch), it is these values that are copied to the Best Mismatch cube (2c). The same is done from the facies cube of this simulation, copy of the facies values to the Best Facies (2d). This process continues until all the N simulated models

Through this way two new cubes are created, one with the best genes from each facies model (Best Facies) generated previously and another cube with the confidence factor of each part (Best Mismatch). As noticed above, the "least mismatch cube" can be seen as a summary of the least mismatch of all N realizations. The "best facies cube" can be seen as facies model combined from all N facies models that best matches the seismic data. However the "best facies cube" does not have the same geological concept as the training image and may have various artifacts, since it is constructed by copying several sets from

The third difference is that, these two new cubes can not be used for the co-simulation of the new generation of facies models, but the "best facies cube" can be used indirectly to

In order to do this, one uses a modification of the probability perturbation method as in [24]. Caers' method was developed to solve non-linear inverse problems under a prior model constraint. It allows the conditioning of stochastic simulations to any type of non-linear data. The principle of this method relies on perturbing the probability models used to

In this methodology the unknown pre-posterior probability – Prob(Aj | C) – is modeled

where A is unknown data, B is well data and previously simulated facies indicators and C will be the "best facies cube", rC is not dependent on uj and is between [0,1], and

According to the methodology proposed in this work, the equation 9 is adapted with rC now representing the mismatch between the field and synthetic seismic as summarized with the

(0) | ( ( ) 1| ) (1 ) ( ) ( ), 1,..., *jj c B jc j P A C PIu C r i u r PA j N* (9)

generate a chain of realizations that converge to match any type of data.

*i jN* **u** is an initial realization conditioned to the B-data only.

using a single parameter model in the following equation 9:

$$P\left(A\_{\boldsymbol{j}} \mid \mathcal{C}\right) = \rho\_c(\boldsymbol{u}) \times I(\boldsymbol{u}\_{\boldsymbol{j}}) + (1 - \rho\_c(\boldsymbol{u})) \times P(A\_{\boldsymbol{j}})\_{\boldsymbol{\iota}} \boldsymbol{j} = 1, \ldots, N \tag{10}$$

where *ρ*C is the correlation coefficient, between [0,1], extracted from the "least mismatch cube", I(uj) is the sand indicator extracted from the "best facies cube" with j=1,…,N facies, and P(Aj) is the global proportion of the considered facies. This expression generates a "facies-probability cube" which is a mixture of global proportion and the "best facies cube".

This facies probabilities cube is then used as soft constraint to generate the next set of N cubes. This is done using Journel's tau-model to integrate probabilistic information from various sources (see equation 11 and figure 20);

$$\frac{\mathbf{x}}{b} = \frac{\mathbf{c}}{a} \tag{11}$$

$$\text{where: } \mathbf{x} = \frac{1 - P(A \mid B, \mathbf{C})}{P(A \mid B, \mathbf{C})}, \text{ } b = \frac{1 - P(A \mid B)}{P(A \mid B)}, \text{ } c = \frac{1 - P(A \mid \mathbf{C})}{P(A \mid \mathbf{C})} \text{ and } a = \frac{1 - P(A)}{P(A)}$$

*a* is the information of the global facies proportion, *b* is the influence of the training image, and *c* is the conditioning of the soft probability cube.

**Figure 20.** Next generation of simulations conditioned to the training image, rotation/affinity and probability cubes.

This iterative algorithm is run until the global mismatch between the synthetic seismic of the facies model and the field seismic data reach an optimal minimum.

#### **5.5. Results**

To illustrate the method, 6 iterations were computed with N=30 simulations on the Stanford VI dataset. Note that one assumed the availability of perfect geological information and a perfect forward model.

The algorithm converges as shown by a systematic increase in the global correlation coefficient between model and amplitude data (figure 21). The facies model with the highest correlation out of 30 models in the last iteration has a correlation of 0.88.

**Figure 21.** Convergence of the algorithm.

**Figure 22.** Comparison between the two methods

The final facies model (top left in in figure 22) does not contain any artifacts, i.e. reproduces well the geological continuity of the training image.

To check how well the seismic amplitude data is reproduced, is forward modeling the best facies model to seismic synthetic (see figure 22). Clearly the method matches well the field seismic, particularly when compared with the probabilistic approach.

In figure 23 some slices of the results are presented, where it is possible to see some similarities between the seismic response of the proposed approach and the reference data seismic amplitude.

**Figure 23.** Correlation slices

144 New Technologies in the Oil and Gas Industry

**5.5. Results**

perfect forward model.

**Figure 21.** Convergence of the algorithm.

**Diference**

**Figure 22.** Comparison between the two methods

This iterative algorithm is run until the global mismatch between the synthetic seismic of the

To illustrate the method, 6 iterations were computed with N=30 simulations on the Stanford VI dataset. Note that one assumed the availability of perfect geological information and a

The algorithm converges as shown by a systematic increase in the global correlation coefficient between model and amplitude data (figure 21). The facies model with the highest

> 123456 **Ite ra tions**

> > B es t M inim um Diferenc e B est Global Correlation

(Proposed Approach) (Reference Data) (Probabilistic Approach)

Forward Modeling Forward Modeling Forward Modeling

**Seismic Amplitude Field Seismic Seismic Amplitude**

24.17

*ρ* = 0.20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

**Correlation**

**Facies Cube**

0.88

facies model and the field seismic data reach an optimal minimum.

correlation out of 30 models in the last iteration has a correlation of 0.88.

60.49

0.17

**Facies Cube Facies Cube**

*ρ* = 0.88

In summary, as stated previously, the local maximum correlation or local minimum mismatch depends on the parameters used to calculate the mismatches. Some parameters sensitivity is further analyzed:

Number of iterations

In the first iteration the main optimization is obtained, after this the process tends to stabilize (figure 24). Hence, without changing any of the other parameters, the number of iterations does not have a big influence on the optimization.

Figure 24 compares the values of correlation and average difference of the simulation model with the best result for different number of facies models N simulated. These values are presented only for the first and last iterations.

For the first iteration the difference is not very noticeable, but in the last iteration the correlation value is higher as the number of simulations increases.

Number of facies simulations (N) per iterations

This parameter can have a major influence on the optimization: with numerous simulations the process has a wide variety of possibilities to choose from, and can build a more precise best-facies cube, but if the simulations are very alike, the choice of N will not matter.

**Figure 24.** Influence of different number of iterations (1 to 6) and simulations (20, 30 and 40).

Criterion of convergence

The criterion of differences is more precise than the correlation, since the correlation gives us a value of the similarity between two sets of data, while the difference is more susceptible to different patterns or small variation in the patterns. But to calculate the probabilities the correlations are needed.

Size of the columns

In the previous test the cubes were divided in 4 layers with 20 values each column. Since the correlation has some sensitivity to the number of data used, the results also change. For a largest column the convergence will tend to be slower, since it is more difficult to match an entire column than piece-wise matching. In general we recommend that the number of layers chosen depends on the vertical resolution of the seismic data, i.e. for lower resolution less layer could be retained.

#### **6. Conclusions**

The algorithm proposed uses two different approaches to create a stochastic model of a reservoir property that are conditioned to both well and seismic data, since this two data are totally different, this process was to be an iterative one, that used the maximum correlation or minimum mismatch as the objective function.

The first approach uses the direct sequential co-simulation as the method of "transforming" 3D images, in an iterative process. Hence it generates, at each iterative step, global acoustic impedances images with the same spatial pattern, without imposing artificially good match in areas of low confidence in seismic quality.

It means that in those areas the final images will reflect high uncertainty. This uncertainty of the seismic acoustic impedance is used to access the uncertainty associated with the porosity model, by co-simulate the porosity model and using the generated final acoustic impedance model and its correlation coefficient cube as soft or secondary data.

Another example is the capacity to improve the seismic resolution, if the acoustic impedance model is created below the original resolution of the seismic. This is possible since the data from wells, that is mainly used to create the model, has a much lower resolution than the seismic.

Time consuming is no more a drawback as it is implemented in a parallel computing platform in [32].

This algorithm has been implemented and benchmarked tested with several real reservoirs, e.g. [33].

For the case of the multipoint approach, it is a new application for the newest stochastic generation of images and is a complement for the first approach.

The modifications made from the first approach to the second, are not too complex, except the population of the acoustic impendence cube, which for this example, a technique of simple populate the channels was used with previous simulated acoustic impedance values.

To resolve this bypass, an algorithm by [34], simulates any variable within the channel, making this step a more realistic one.

The program still needs to be improved and the algorithm can be optimized for real case applications and the lack of training images that could implement it to all kind or examples is still in research.


**Delays in the Diagnosis of Pulmonary Thromboembolism and Risk Factors**

Savas Ozsu

*Karadeniz Technical University Department of Chest Diseases School of Medicine, Trabzon, Turkey*

#### **1. Introduction**

62 Emergency Medicine – An International Perspective

van Crevel, H., A. Hijdra and J. de Gans (2002). "Lumbar puncture and the risk of

Venkatesan, A. and D. E. Griffin (2009). Bacterial infections. In: *Cerebrospinal fluid in clinical practice*. Saunders-Elsevier, ISBN 978-1-4160-2908-3. Philadelphia: 167-175. Vilming, S. T. and R. Kloster (1998). "Pain location and associated symptoms in post-lumbar

Vos, P. E., W. A. de Boer, J. A. Wurzer and J. van Gijn (1991). "Subdural hematoma after

Weisman, L. E., G. B. Merenstein and J. R. Steenbarger (1983). "The effect of lumbar puncture position in sick neonates." *Am J Dis Child* 137(11): 1077-1079, ISSN 0002-922X

puncture headache." *Cephalalgia* 18(10): 697-703, ISSN 0333-1024

*Neurosurg* 93(2): 127-132, ISSN 0303-8467

5354

herniation: when should we first perform CT?" *J Neurol* 249(2): 129-137, ISSN 0340-

lumbar puncture: two case reports and review of the literature." *Clin Neurol*

Pulmonary thromboembolism (PE) is a relatively common cardiovascular emergency and it is healthy important issue. Overall, VTE occurs for the first time in approximately 1 case per 1000 each year. PE has been estimated to occur in 600,000 patients annually in the United States and is reported to cause or attribute to 50,000 to 200,000 deaths (Wood, 2002). PE is a difficult diagnosis that may be missed because of non-specific clinical presentation. Clinical scoring systems have been widely used to determine the risk of PE (Table 1). These scoring systems include age and cancer which can be risk factors causing PE. However specificity and sensitivity of these scoring systems range from 60-85% to 50-80% in a current metaanalysis(Lucassen, 2011). An increased prevalence of PE was found associated with higher sensitivity and lower specificity. Thus lower specificity of these scoring systems cause the unnecessary diagnostic workup. Diagnosis of PE by pulmonary computerized tomography angiography has been increasing in the last 10 years. Actually, Pulmonary CTA confirmed PE in only a minority of patients and may be overused. A retrospective study demonstrated that pulmonary CTA was positive for PE in only 39 (14.9%) of 261 patients. (Haap, 2011)

In addition, during the past 2 decades, prevention of venous thromboembolism (VTE) has become widely accepted as an effective and worthwhile strategy. North American and European guidelines have provided detailed recommendations for prophylaxis among virtually all groups of hospitalized patients, especially in those with heart failure, active cancer, or sepsis.

However, despite advances in prophylaxis and newer diagnostic methods delays in the diagnosis of PE is stil common. Pulmonary embolism has been determined in 15% of the autopsy series, whereas the antemortem diagnosis of fatal PE has not changed appreciably over time, remaining fixed at approximately 30% [Wood,2002]. Most patients who die of PE do so within hours of the event (Dalen,2002). 30% of all hospital deaths are also caused by pulmonary embolism. Whereas, earlier diagnoses of DVT and PE will reduce the morbidity and mortality associated with venous thromboembolism.

In several studies, 16% to 30.4% of the patients with acute PE were diagnosed one week or longer after the symptom onset, and the mean time to diagnosis was 4.8 to 8.4 days. It has

Delays in the Diagnosis of Pulmonary Thromboembolism and Risk Factors 65

The time from the onset of symptoms to diagnosis was defined as "*time to diagnosis*" and delay from the first medical attention to diagnosis "*presentation to diagnosis*". Especially, diagnostic delay was determined as diagnosis of PE more than 5-10 days after symptom onset in different studies. But delay in the diagnosis of PE was defined as shorter that was 12-24 hours in 2 different studies. Actually the time for delayed diagnosis of PE patients who have no diagnosis is an obscure area and therefore there is no known answer yet for

There is a limited data about the effect of risk factors on time to diagnosis of PE. PE is still a major cause of death in hospital patients, in medical service especially. Therefore medical risk factors are one of the important reasons leading to the diagnostic delay in PE. It is thought that previous hospitalization history may lead an idea of an inadequate prophylaxis

We retrospectively evaluated the records of 408 diagnosed with PE. The mean time to diagnosis was 4.39 ± 7.6 days (median, 2 days; range, 0-45 days) in the surgical group and 8.0 ± 8.6 days (median, 4 days; range, 0-45 days) in the medical group. The percentage of cases diagnosed within the first week (87.5% of the surgical group, 66% of the medical group) was significantly higher in the surgical group patients with acute PE. In our study, the mean time to diagnosis in the medical group was approximately four times greater than that of the surgical group on univariate analysis. In multivariate analysis surgery, presence of cancer, and stroke were found to be related with early diagnosis in PE. We think that presence of medical disease is a risk factor for PE and can also mask PE symptoms, thus delaying diagnosis. Bulbul et al. also reported that patients with a trauma or surgical risk

In MASTER study, is an Italian, multicenter, observational study included 542 PE patients, was found by Ageno et al. the presence of transient risk factors (recent surgery, recent trauma, severe medical diseases, immobilization, pregnancy, puerperium, oral contraceptives, central venous catheters) for PE predicted earlier diagnosis of PE. As interesting, known thrombophilia, active cancer and previous PE or DVT was not predicted

Castro et al. prospectively was evaluated 397 consecutive patients with acute PE objectively diagnosed in an Emergency Department. Eighteen percent of patients had a diagnostic delay with symptoms beginning more than 1 week prior to the diagnosis of PE. Similarly Castro et al found that active cancer, immobility and presence of previous VTE does not

Berghaus et al. suggested that delay in diagnosis was significant greater in patients with

A recent study, Smith et al. studied 400 patients with acute PE who were diagnosed either within 12 h or after 12 h from ED arrival. They found that patients with delayed diagnosis were morbid obesity, whereas patients with early diagnosis more frequently had recent immobility (Smith,2011). Obesity that was found to be associated with the

recurrent than in patients with first PE (3.4 ± 2.3 vs. 2.2 ± 1.7 days) (Berghaus,2011).

**2. Definitions**

answering the optimal time fort he diagnosis of PE.

**3. Diagnostic delays in PE and risk factors**

or underestimation the diagnosis of PE.

factor were diagnosed earlier (Bulbul,2009).

earlier diagnosis of PE in this study (Ageno, 2008).

predict the early diagnosis of PE (Castro,2007).

Table 1. Clinical probability rules for PE: revised Geneva score and Wells score.

been reported that 31.6% to 50% of the patients presented within 24 hours of symptom onset [Bulbul 2009,Castro,2007, Elliott 2005,Timmons 2003]. In our study, 29.6% of the patients had presented within the first 24 hours and 72.3% within the first week, with an average of 6.9 days between onset of symptoms and diagnosis. In the two previous studies, delays from first medical attention to diagnosis were determined to range from 0.9 to 2 days [Bulbul 2009, Elliott 2005]. In another study, PE was diagnosed in 12% of the patients more than 48 hours after admission to the ED [Kline,2007]. In our study, the mean time to diagnosis was found as 2.4 days. As a result, the majority of patients that die from PE do so within hours of the event. Therefore, early diagnosis is fundamental, since immediate treatment is highly life-saving.

The most important factor in the delayed diagnosis of PE is lack of suspicion for the PE disease itself. In addition to this, accompanying comorbidities and risk factors may affect the time to diagnosis. The presence of a risk factor in PE is important step in the diagnosis of PE and it should be questioned. Although PE can occur in patients without any identifiable risk factors, one or more of these factors are usually determined (secondary PE). Symptoms are often attributed to these diseases because of risk factors and associated comorbidities. Therefore it is inevitable to cause diagnostic delay. The proportion of patients with idiopathic or unprovoked PE was about 20% in the literature. Actually it seems to be a important factor to cause delays in diagnosis of pulmonary embolism.

In this section, we will investigated the effect of risk factors on delay to diagnosis in acute PE and also the relationship between delays and outcome of the disease.

### **2. Definitions**

64 Emergency Medicine – An International Perspective

Table 1. Clinical probability rules for PE: revised Geneva score and Wells score.

treatment is highly life-saving.

been reported that 31.6% to 50% of the patients presented within 24 hours of symptom onset [Bulbul 2009,Castro,2007, Elliott 2005,Timmons 2003]. In our study, 29.6% of the patients had presented within the first 24 hours and 72.3% within the first week, with an average of 6.9 days between onset of symptoms and diagnosis. In the two previous studies, delays from first medical attention to diagnosis were determined to range from 0.9 to 2 days [Bulbul 2009, Elliott 2005]. In another study, PE was diagnosed in 12% of the patients more than 48 hours after admission to the ED [Kline,2007]. In our study, the mean time to diagnosis was found as 2.4 days. As a result, the majority of patients that die from PE do so within hours of the event. Therefore, early diagnosis is fundamental, since immediate

The most important factor in the delayed diagnosis of PE is lack of suspicion for the PE disease itself. In addition to this, accompanying comorbidities and risk factors may affect the time to diagnosis. The presence of a risk factor in PE is important step in the diagnosis of PE and it should be questioned. Although PE can occur in patients without any identifiable risk factors, one or more of these factors are usually determined (secondary PE). Symptoms are often attributed to these diseases because of risk factors and associated comorbidities. Therefore it is inevitable to cause diagnostic delay. The proportion of patients with idiopathic or unprovoked PE was about 20% in the literature. Actually it seems to be a

In this section, we will investigated the effect of risk factors on delay to diagnosis in acute

important factor to cause delays in diagnosis of pulmonary embolism.

PE and also the relationship between delays and outcome of the disease.

The time from the onset of symptoms to diagnosis was defined as "*time to diagnosis*" and delay from the first medical attention to diagnosis "*presentation to diagnosis*". Especially, diagnostic delay was determined as diagnosis of PE more than 5-10 days after symptom onset in different studies. But delay in the diagnosis of PE was defined as shorter that was 12-24 hours in 2 different studies. Actually the time for delayed diagnosis of PE patients who have no diagnosis is an obscure area and therefore there is no known answer yet for answering the optimal time fort he diagnosis of PE.

### **3. Diagnostic delays in PE and risk factors**

There is a limited data about the effect of risk factors on time to diagnosis of PE. PE is still a major cause of death in hospital patients, in medical service especially. Therefore medical risk factors are one of the important reasons leading to the diagnostic delay in PE. It is thought that previous hospitalization history may lead an idea of an inadequate prophylaxis or underestimation the diagnosis of PE.

We retrospectively evaluated the records of 408 diagnosed with PE. The mean time to diagnosis was 4.39 ± 7.6 days (median, 2 days; range, 0-45 days) in the surgical group and 8.0 ± 8.6 days (median, 4 days; range, 0-45 days) in the medical group. The percentage of cases diagnosed within the first week (87.5% of the surgical group, 66% of the medical group) was significantly higher in the surgical group patients with acute PE. In our study, the mean time to diagnosis in the medical group was approximately four times greater than that of the surgical group on univariate analysis. In multivariate analysis surgery, presence of cancer, and stroke were found to be related with early diagnosis in PE. We think that presence of medical disease is a risk factor for PE and can also mask PE symptoms, thus delaying diagnosis. Bulbul et al. also reported that patients with a trauma or surgical risk factor were diagnosed earlier (Bulbul,2009).

In MASTER study, is an Italian, multicenter, observational study included 542 PE patients, was found by Ageno et al. the presence of transient risk factors (recent surgery, recent trauma, severe medical diseases, immobilization, pregnancy, puerperium, oral contraceptives, central venous catheters) for PE predicted earlier diagnosis of PE. As interesting, known thrombophilia, active cancer and previous PE or DVT was not predicted earlier diagnosis of PE in this study (Ageno, 2008).

Castro et al. prospectively was evaluated 397 consecutive patients with acute PE objectively diagnosed in an Emergency Department. Eighteen percent of patients had a diagnostic delay with symptoms beginning more than 1 week prior to the diagnosis of PE. Similarly Castro et al found that active cancer, immobility and presence of previous VTE does not predict the early diagnosis of PE (Castro,2007).

Berghaus et al. suggested that delay in diagnosis was significant greater in patients with recurrent than in patients with first PE (3.4 ± 2.3 vs. 2.2 ± 1.7 days) (Berghaus,2011).

A recent study, Smith et al. studied 400 patients with acute PE who were diagnosed either within 12 h or after 12 h from ED arrival. They found that patients with delayed diagnosis were morbid obesity, whereas patients with early diagnosis more frequently had recent immobility (Smith,2011). Obesity that was found to be associated with the

Delays in the Diagnosis of Pulmonary Thromboembolism and Risk Factors 67

The incidences of venous thromboembolism and PE are known to increase with age The patients with the diagnosis of PE were mostly above the 65 years old. The annual incidence of PE is 1.3/1000 at age 65–69 years and 3.1/1000 at age 85–89 years of age [Kniffer, 1994]. With age is variable thrombotic/antithrombotic balance, fibrinogen levels increase and antithrombin 3 levels decline [Hager,1989], while reduced lower limb musculature and immobility may encourage venous stasis. Although these physiological changes may predispose eldery persons to thromboembolism, there is also a rise in specific risk factors for thromboembolism with aging, such as congestive heart failure, stroke and hip fracture,

An important factor leading to delay in PE diagnosis is age, but this contribution is also controversial. Several earlier studies found that the clinical presentation of older patients with acute PE may be atypical, potentially leading to a delay in diagnosis and initiation of treatment. Otherwise, in a recent study, Berghaus et al. found that delay in diagnosis had not was not significantly different in the younger and older age group(Berghaus, 2011). Contrary, in an autopsy studies was found that the post-mortem diagnosis of PE was

In this section, we will discuss impact of risk factors in delays according to literature.

Diagnostic delay and its impact on outcome of the disease were studied in seven studies. In this section, we will discuss and review the impact of delays on mortality and recurrence

Although early diagnosis of PE is thought to reduce mortality, sufficient data on the subject are still limited. Mortality of PE has not been changed although the use of spiral BT increase

In the 1950s, the results of large-scale autopsy studies showed that only 11 to 12% of patients with PE received correct diagnoses before death (Uhland,1964). Pineda and colleagues reported a 45 % rate of correct antemortem diagnosis in a study that was done from 1991 to 1996 (Pineda,2001). Actually it has been seen that the mortality decrease according to the

There were no statistical differences between the 2 groups in terms of mortality in our study. Delay in diagnosis was not different between those who died and those who survived. The mean time to diagnosis in the 40 patients who died was 5.7 ± 6.7 days (median, 3 days; range, 0-30 days) and 7.1 ± 8.7 days (median, 3 days; range, 0-45 days) in those who

Kline et al. found inhospital adverse outcomes (Death, circulatory shock, or endotracheal intubation for respiratory distress) of patients with delayed diagnosis were worse than those

Two different study suggest that early or late diagnosis did not change PE mortality.

survived. We found that earlier diagnosis had no impact on mortality (Ozsu,2011).

**4. Diagnostic delays in PE and age**

among many comorbid condition (eg. cancer)

significantly more frequent in older patients (Mangion, 1989)

**5. The impact of delay on outcome of embolism**

rates of pulmonary embolism.

rate of diagnosis in PE patients.

**5.1 Mortality**

years.

delayed diagnosis of PE was composed of the 11.5% of the patients in this study. Immobility is important for obese patients so that delayed diagnosis is possibly related to the immobility.

Actually, in patients with previous PE must be associated with early diagnosis of PE. Up to now, it is unknown whether patients with recurrent PE are diagnosed earlier than those with their first episode. Surprisingly, Barghaus et al. were found that patients with recurrent PE was not diagnosed earlier but even later than those with their first episode, although all enrolled patients reported common clinical signs of PE. They reported that delay in diagnosis after symptom onset was significantly greater in patients with recurrent than in patients with their first PE (3.4 ± 2.3 vs. 2.2 ± 1.7 days) (Barghaus,2011).

In the light of the data above, effect of risk factors on the delayed diagnosis of PE seems to be contrversial. Especially in these mentioned studies, it has been seen that percentage of risk factors causing PE was various.

For example immobility has been found as a risk factor of 42% of the PE patients in the study by Stein et al. whereas this percentage was 17% in the study by Castro et al.

Threfore, it is diffucult to determine whether immobility causes early or late diagnosis of PE in this case.

Immobility was found to be related to early diagnosis of PE in the univariate analysis of our study. We are on side of the opinion that presence of immobility is an obvious clue in the patients with PE suspicion for the early diagnosis. Similarly, surgery was present as a risk factior in 30% of our study population of PE in our study but this percentage was only 9% in study by Castro et al.

Hospitalization of the most of the operated patients and development of PE after a short time of operation in these patients provides an easy way to diagnosis of PE.

Immobility and the presence of cancer alone constitutes a low risk for PE according to Wells criteria. A frequent complication is the occurrence of venous thromboembolism , for which cancer is one of the most relevant risk factors..However diagnosis of PE was found as difficult in cancer patients in 2 different studies.It was seen that suspicion of PE diagnosis was present in 6(75%) of 8 patients in an autopsy study by Pineda et al..It is possible that symptoms and signs of these patients were associated with the cancer and diagnostic workup was not done. D-dimer positivity is an expected sign in cancer patients. Therefore presence of low risk with the D-dimer positivity needs CT pulmonary angiography in these patients. Moreover recently appeared VTE (pulmonary embolism or deep vein thrombosis) occurred in 12-32% of cases with the diagnosis of cancer during follow-up period (Goldhaber 2004,. Kroger 2006).

According to the recent data, asymptomatic DVT and PE was found in 8% and 3.3% of patients with the diagnosis of cancer during their screening workup by computerized tomography (Cronin 2006). These data showed that the diagnosis of PE was delayed in cancer patients. Therfore it is necessary to suspect the diagnosis of PE in cancer patients more than the others.

### **4. Diagnostic delays in PE and age**

66 Emergency Medicine – An International Perspective

delayed diagnosis of PE was composed of the 11.5% of the patients in this study. Immobility is important for obese patients so that delayed diagnosis is possibly related

Actually, in patients with previous PE must be associated with early diagnosis of PE. Up to now, it is unknown whether patients with recurrent PE are diagnosed earlier than those with their first episode. Surprisingly, Barghaus et al. were found that patients with recurrent PE was not diagnosed earlier but even later than those with their first episode, although all enrolled patients reported common clinical signs of PE. They reported that delay in diagnosis after symptom onset was significantly greater in patients with recurrent than in

In the light of the data above, effect of risk factors on the delayed diagnosis of PE seems to be contrversial. Especially in these mentioned studies, it has been seen that percentage of

For example immobility has been found as a risk factor of 42% of the PE patients in the

Threfore, it is diffucult to determine whether immobility causes early or late diagnosis of PE

Immobility was found to be related to early diagnosis of PE in the univariate analysis of our study. We are on side of the opinion that presence of immobility is an obvious clue in the patients with PE suspicion for the early diagnosis. Similarly, surgery was present as a risk factior in 30% of our study population of PE in our study but this percentage was only 9% in

Hospitalization of the most of the operated patients and development of PE after a short

Immobility and the presence of cancer alone constitutes a low risk for PE according to Wells criteria. A frequent complication is the occurrence of venous thromboembolism , for which cancer is one of the most relevant risk factors..However diagnosis of PE was found as difficult in cancer patients in 2 different studies.It was seen that suspicion of PE diagnosis was present in 6(75%) of 8 patients in an autopsy study by Pineda et al..It is possible that symptoms and signs of these patients were associated with the cancer and diagnostic workup was not done. D-dimer positivity is an expected sign in cancer patients. Therefore presence of low risk with the D-dimer positivity needs CT pulmonary angiography in these patients. Moreover recently appeared VTE (pulmonary embolism or deep vein thrombosis) occurred in 12-32% of cases with the diagnosis of cancer during follow-up period

According to the recent data, asymptomatic DVT and PE was found in 8% and 3.3% of patients with the diagnosis of cancer during their screening workup by computerized tomography (Cronin 2006). These data showed that the diagnosis of PE was delayed in cancer patients. Therfore it is necessary to suspect the diagnosis of PE in cancer patients

time of operation in these patients provides an easy way to diagnosis of PE.

study by Stein et al. whereas this percentage was 17% in the study by Castro et al.

patients with their first PE (3.4 ± 2.3 vs. 2.2 ± 1.7 days) (Barghaus,2011).

to the immobility.

in this case.

study by Castro et al.

(Goldhaber 2004,. Kroger 2006).

more than the others.

risk factors causing PE was various.

The incidences of venous thromboembolism and PE are known to increase with age The patients with the diagnosis of PE were mostly above the 65 years old. The annual incidence of PE is 1.3/1000 at age 65–69 years and 3.1/1000 at age 85–89 years of age [Kniffer, 1994]. With age is variable thrombotic/antithrombotic balance, fibrinogen levels increase and antithrombin 3 levels decline [Hager,1989], while reduced lower limb musculature and immobility may encourage venous stasis. Although these physiological changes may predispose eldery persons to thromboembolism, there is also a rise in specific risk factors for thromboembolism with aging, such as congestive heart failure, stroke and hip fracture, among many comorbid condition (eg. cancer)

An important factor leading to delay in PE diagnosis is age, but this contribution is also controversial. Several earlier studies found that the clinical presentation of older patients with acute PE may be atypical, potentially leading to a delay in diagnosis and initiation of treatment. Otherwise, in a recent study, Berghaus et al. found that delay in diagnosis had not was not significantly different in the younger and older age group(Berghaus, 2011). Contrary, in an autopsy studies was found that the post-mortem diagnosis of PE was significantly more frequent in older patients (Mangion, 1989)

In this section, we will discuss impact of risk factors in delays according to literature.

### **5. The impact of delay on outcome of embolism**

Diagnostic delay and its impact on outcome of the disease were studied in seven studies. In this section, we will discuss and review the impact of delays on mortality and recurrence rates of pulmonary embolism.

#### **5.1 Mortality**

Although early diagnosis of PE is thought to reduce mortality, sufficient data on the subject are still limited. Mortality of PE has not been changed although the use of spiral BT increase rate of diagnosis in PE patients.

In the 1950s, the results of large-scale autopsy studies showed that only 11 to 12% of patients with PE received correct diagnoses before death (Uhland,1964). Pineda and colleagues reported a 45 % rate of correct antemortem diagnosis in a study that was done from 1991 to 1996 (Pineda,2001). Actually it has been seen that the mortality decrease according to the years.

Two different study suggest that early or late diagnosis did not change PE mortality.

There were no statistical differences between the 2 groups in terms of mortality in our study. Delay in diagnosis was not different between those who died and those who survived. The mean time to diagnosis in the 40 patients who died was 5.7 ± 6.7 days (median, 3 days; range, 0-30 days) and 7.1 ± 8.7 days (median, 3 days; range, 0-45 days) in those who survived. We found that earlier diagnosis had no impact on mortality (Ozsu,2011).

Kline et al. found inhospital adverse outcomes (Death, circulatory shock, or endotracheal intubation for respiratory distress) of patients with delayed diagnosis were worse than those

Delays in the Diagnosis of Pulmonary Thromboembolism and Risk Factors 69

Late diagnosis also depends on patients delay in seeking medical awareness. In the North American study, 80%of the delay in diagnosis of DVT found before medical evaluation, whereas delays in the diagnosis of PE represented both delays in seeking medical attention and delays from medical assessment to proper objective testing [Elliott,2005]. It has been seen that many patients who had delayed diagnosis had admitted to a physcian before.

Bulbul et al. showed that previous hospital or doctor visits were associated with an approximately 11 times longer diagnostic delay than the patients who did not visit a doctor or a hospital in univariate logistic regression analysis (Bulbul,2011). Data has been shown

Suspicion for PE is first step for the diagnosis of PE then it is necessary to perform rapid tests for the diagnosis. Bolus heparin should be done especially in high risk patients Clinical presentation is non-spesific for PTE and most important point in diagnosis is suspicion for

In addition it has been known that there is no spesific marker for PE yet. Although negative predictive value of D-dimer is high, most of the patients admitted to the hospital have positive D-dimer level. Therefore advanced studies are needed for this issue. Another important point is that there is need for more spesific scoring systems of the PE

Because history of previous VTE alone is classified as intermediate ris for the PE according to the Well's criteria. However diagnosis is delayed in patients having VTE in studies. This is a obvious contraversion. Perhaps physicians do not use clinical scoring systems efficienly

Moreover it is known that both history given by patients and taken by physician is not sufficient for the diagnosis of PE. Also patients should be stratified for the diagnosis of PE. It was shown that level of patients' education was related to the delayed diagnosis of PE (Bulbul, 2011). Because it was shown that even the patients having previous PE diagnosis

Finally, public and professional education represents a critical step for the early diagnosis and treatment in this patients, especially. My opinion, earlier diagnoses of PE will reduce

that there is no sufficient suspicion effort he diagnosis of PE..

PTE. Therefore more efforts are needed by experts effort he diagnosis of PE.

**6. Recommendations**

diagnosis.

for the diagnosis of PE.

had also delayed diagnosis of PE.

the morbidity and mortality associated with PE.

**Late presentation early presentation**  \*Immobility Recent surgery/ trauma

Idiopathic Severe medical diseases \*Elderly Pregnancy/ puerperium

\*There are studies showed that these risk factors were related to the early presentation

\*Cancer Oral contraceptives, Previous VTE central venous catheter

Table 2. Risk factors relation to early/late presentation.

of patients with PE diagnosed in the emergency department. Advers events were seen in 30% of the patients who have delayed diagnosis compared to the advers events seen in 8.5% of the patients who have no delayed diagnosis of PE (Kline,2007).

Approximately 30% of PE patients are died from PE without having any diagnosis.

Patients presenting with PE who are immediately treated with anticoagulation have a lower rate of in-hospital death (1.5%) compared with those who are not treated (5–23%) (Douketis,1998).

Smith et al. compared that patients receiving heparin either in the emergency department or after admission. They found patients who received heparin in the ED had lower inhospital (1.4% vs 6.7%) and 30-day (4.4% vs 15.3%) mortality rates as compared with patients given heparin after admission. Patients who achieved a therapeutic aPTT within 24 h had lower in-hospital (1.5% vs 5.6%) and 30-day (5.6% vs 14.8% ) mortality rates as compared with patients who achieved a therapeutic aPTT after 24 h. In multiple logistic regression models, receiving heparin in the ED was remained predictive of reduced mortality, and ICU admission was remained predictive of increased mortality in the same study (Smith,2010).

Studies concerning with the relationship between mortality and diagnostic delay of PE was done in patients already having the PE diagnosis. These patients have PE diagnosis and survived anyway. However many patients are died from PE without having the antemortem PE diagnosis in postmortem studies. Delayed diagnosis of PE can be fatal according to these data.

The main problem is to diagnose these PE patients who can be fatal early and to start their treatment. The patients were classified as high risk and non-high risk patients according to risk groups. This can affect the results. Because, hours or even minutes are important for these high risk patients. Moreover it is necessary to investigate the mortality of the high risk patients whose diagnosis was made within 6-12 hours

Because there is no sufficient data about this subject. Thus effect of mortality on delayed diagnosis of PE may be understood more easily. Early diagnosis and treatment in PE is lifesaving. Guidelines recommend initiation of anticoagulation if clinical suspicion for PE is high, even prior to confirmatory testing.

#### **5.2 Re-embolism**

The most important long-term complication of PTE is re-embolism that is associated with considerable morbidity and mortality. Delay in diagnosis of PE may be associated with chronic thrombo-embolic pulmonary hypertension rather than re-embolism. However, there is well not documented this issue.

Castro et al. did not detect an association between a delay in diagnosis and an PE recurrence during the ensuing 3 months of treatment. They found that recurrent VTE in 3 (4.2%) of 72 patients with diagnostic delay and in 15 (4.6%) of 325 patients without diagnostic delay (Castro,2007)

### **6. Recommendations**

68 Emergency Medicine – An International Perspective

of patients with PE diagnosed in the emergency department. Advers events were seen in 30% of the patients who have delayed diagnosis compared to the advers events seen in 8.5%

Patients presenting with PE who are immediately treated with anticoagulation have a lower rate of in-hospital death (1.5%) compared with those who are not treated (5–23%)

Smith et al. compared that patients receiving heparin either in the emergency department or after admission. They found patients who received heparin in the ED had lower inhospital (1.4% vs 6.7%) and 30-day (4.4% vs 15.3%) mortality rates as compared with patients given heparin after admission. Patients who achieved a therapeutic aPTT within 24 h had lower in-hospital (1.5% vs 5.6%) and 30-day (5.6% vs 14.8% ) mortality rates as compared with patients who achieved a therapeutic aPTT after 24 h. In multiple logistic regression models, receiving heparin in the ED was remained predictive of reduced mortality, and ICU admission was remained predictive of increased mortality in the same

Studies concerning with the relationship between mortality and diagnostic delay of PE was done in patients already having the PE diagnosis. These patients have PE diagnosis and survived anyway. However many patients are died from PE without having the antemortem PE diagnosis in postmortem studies. Delayed diagnosis of PE can be fatal

The main problem is to diagnose these PE patients who can be fatal early and to start their treatment. The patients were classified as high risk and non-high risk patients according to risk groups. This can affect the results. Because, hours or even minutes are important for these high risk patients. Moreover it is necessary to investigate the mortality of the high risk

Because there is no sufficient data about this subject. Thus effect of mortality on delayed diagnosis of PE may be understood more easily. Early diagnosis and treatment in PE is lifesaving. Guidelines recommend initiation of anticoagulation if clinical suspicion for PE is

The most important long-term complication of PTE is re-embolism that is associated with considerable morbidity and mortality. Delay in diagnosis of PE may be associated with chronic thrombo-embolic pulmonary hypertension rather than re-embolism. However, there

Castro et al. did not detect an association between a delay in diagnosis and an PE recurrence during the ensuing 3 months of treatment. They found that recurrent VTE in 3 (4.2%) of 72 patients with diagnostic delay and in 15 (4.6%) of 325 patients without diagnostic delay

Approximately 30% of PE patients are died from PE without having any diagnosis.

of the patients who have no delayed diagnosis of PE (Kline,2007).

(Douketis,1998).

study (Smith,2010).

according to these data.

**5.2 Re-embolism**

(Castro,2007)

patients whose diagnosis was made within 6-12 hours

high, even prior to confirmatory testing.

is well not documented this issue.

Late diagnosis also depends on patients delay in seeking medical awareness. In the North American study, 80%of the delay in diagnosis of DVT found before medical evaluation, whereas delays in the diagnosis of PE represented both delays in seeking medical attention and delays from medical assessment to proper objective testing [Elliott,2005]. It has been seen that many patients who had delayed diagnosis had admitted to a physcian before.

Bulbul et al. showed that previous hospital or doctor visits were associated with an approximately 11 times longer diagnostic delay than the patients who did not visit a doctor or a hospital in univariate logistic regression analysis (Bulbul,2011). Data has been shown that there is no sufficient suspicion effort he diagnosis of PE..

Suspicion for PE is first step for the diagnosis of PE then it is necessary to perform rapid tests for the diagnosis. Bolus heparin should be done especially in high risk patients Clinical presentation is non-spesific for PTE and most important point in diagnosis is suspicion for PTE. Therefore more efforts are needed by experts effort he diagnosis of PE.

In addition it has been known that there is no spesific marker for PE yet. Although negative predictive value of D-dimer is high, most of the patients admitted to the hospital have positive D-dimer level. Therefore advanced studies are needed for this issue. Another important point is that there is need for more spesific scoring systems of the PE diagnosis.

Because history of previous VTE alone is classified as intermediate ris for the PE according to the Well's criteria. However diagnosis is delayed in patients having VTE in studies. This is a obvious contraversion. Perhaps physicians do not use clinical scoring systems efficienly for the diagnosis of PE.

Moreover it is known that both history given by patients and taken by physician is not sufficient for the diagnosis of PE. Also patients should be stratified for the diagnosis of PE. It was shown that level of patients' education was related to the delayed diagnosis of PE (Bulbul, 2011). Because it was shown that even the patients having previous PE diagnosis had also delayed diagnosis of PE.

Finally, public and professional education represents a critical step for the early diagnosis and treatment in this patients, especially. My opinion, earlier diagnoses of PE will reduce the morbidity and mortality associated with PE.

\*There are studies showed that these risk factors were related to the early presentation

Table 2. Risk factors relation to early/late presentation.

Delays in the Diagnosis of Pulmonary Thromboembolism and Risk Factors 71

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pulmonary embolism. Chest. 2005; 128:3372–6.

pulmonary embolism. Acad Emerg Med 2007;14: 592-8.

MASTER registry. Thromb Res. 2008;121:751–6.

Thromb Hemost. 2011 May 17. [Epub ahead of print]

cancer patients. Annals of Oncology 2006;17: 297–303.

thromboembolism. Ups J Med Sci. 2011;116(1):72-6.

Postgrad Med J. 1989;65(769):814-817.

2003;32(6):601-5.

62.

2004; 93: 259–62.

189:162–70.

Geriatr 1989; 9: 277–82.

Chest 1964; 45:533–536

861–6.

Table 3. Approach to diagnosis of PE.

#### **7. References**

Public information/awareness Diagnostic strategies in PE

**ASK** Assessment of clinical probability

Multislice CT

No-PE

PE-treatment

If CT is negative in patients with high clinical probability, further investigation may be considered before withholding PE treatment and/or directly start to treatment

High or Likely probability

Low/intermediate or Unlikely probability

Adapted from reference 25

[1] Kenneth E. Wood. Review of a Pathophysiologic Approach to the Golden Hour of Hemodynamically Significant Pulmonary Embolism Chest 2002;121;877-905 [2] Lucassen W, Geersing GJ, Erkens PM, Reitsma JB, Moons KG, Büller H, van Weert HC.

[3] Haap MM, Gatidis S, Horger M, et al. .Computed tomography angiography in patients

[4] Dalen JE. Pulmonary embolism: what have we learned since Virchow? Chest

[5] Bulbul Y, Ozsu S, Kosucu P, Oztuna F, Ozlu T, Topbas¸ M. Time delay between onset of

[6] Jiménez Castro D, Sueiro A, Díaz G, Escobar C, García-Rull S, Picher J, Taboada D,

Clinical Decision Rules for Excluding Pulmonary Embolism: A Meta-analysis. Ann

with suspected pulmonary embolism—too often considered. Am J Emerg Med.

symptoms and diagnosis in pulmonary thromboembolism. Respiration. 2009;78:36–

Yusen RD. Prognostic significance of delays in diagnosis of pulmonary embolism.

Provide medical attention for diagnosis of PE

Positive D-dimer

Multislice CT

Intern Med. 2011 Oct 4;155(7):448-60.

2011 Jan 27. [Epub ahead of print]

Thromb Res. 2007;121(2):153-8.

No-PE

**Immobility Previous VTE Elderly Cancer Obesity A doctor visity Hospitalization**

PE-treatment

**7. References**

Table 3. Approach to diagnosis of PE.

2002;122:1440–6.

41.

**4**

Xiang-Yu Hou

*Australia*

**Emergency Medicine in China**

*School of Medicine, The University of Queensland, Brisbane,*

*School of Public Health, Queensland University of Technology, Brisbane,*

*Department of Emergency Medicine, Royal Brisbane and Women's Hospital, Brisbane,*

With the world's largest population of 1.3 billion, China is a rapidly developing country. In line with this development, China's enormous health system is experiencing an unprecedented series of reforms. According to a recent official government report, China has 300, 000 health organizations, which include 60, 000 hospitals and a total number of 3.07 million beds (China NBoSoP 2006). To provide health services for the national population, as well as the substantial number of visitors, China has 1.93 million doctors and 1.34 million registered nurses (China NBoSoP 2006). From 1984 to 2004, the number of inpatients grew from about 25 to 50 million, with outpatient figures increasing from 1.1 to 1.3 billion (China MoH 2006). The scale of the health system is likely bigger than in any other countries in the world, but the quality of medical services is still among the levels of developing countries. In 2005, approximately 3.8% of inpatients (about 1.5 million)(China NBoSoP 2006) were admitted because of injury and poisoning, which created significant load for the acute health system. These increased figures are at least partly because of the development of the health system and technological health-care advances but, even with such advances, this rapid change in emergency health-care demand has created a very significant burden on

The Chinese Emergency Medical Service system is primarily composed of three sectors: prehospital care, hospital Emergency Departments (EDs) and hospital intensive care units (ICU). While pre-hospital care systems development in China, especially the workforce status, has been reported and discussed before (Hou and Lu 2005), the purpose of this book chapter is to introduce the development of emergency medicine (EM) in China, including the role of traditional Chinese medicine, which is based mainly on hospital ED. EDs in hospitals are like the window of the world where the health problems in the community would show among the ED patients (Hou and Chu 2010), therefore an in-depth understanding of the hospital EDs in different countries would enhance the appreciation of

Development of Chinese hospital ED over the past two decades has been characterized by two main factors: establishment of a professional body of Emergency Medicine (EM)

**1. Introduction**

existing systems.

the general health system in other countries.

**2. Brief history of emergency medicine development in China**


**9.2 Stability of TGMS lines under fertility inducing and sterility inducing environments**

In Tamil Nadu, there is an exclusive centre for two line hybrid rice research under Tamil Nadu Agricultural university, Coimbatore. The centre was established during 1996 at Gudalur, Nilgiris District. The seed production procedure mentioned above is followed for evaluating tgms line at Gudalur and Coimbatore. For exploiting the two line breeding system a stable TGMS Lines are needed and seed production to be standartised based on the line to be used and prevailing weather parameters in the particular location.

Manonmani *et al.,* [82] studied the stability of new TGMS lines for sterility and standardized the seed multiplication of tgms lines at sterility and fertility favoring Environments. The experiments were conducted at Paddy Breeding Station, Coimbatore, Farmers field at Sathiyamangalam and Hybrid Rice Evaluation Centre, Gudalur during the *rabi and khariff* seasons in 2013 & 2014 to assess the pollen fertility expression under different temperature regimes in new generation temperature sensitive genic male sterile lines of TNAU. Weather parameters at Coimbatore and Gudalur during for the past fifteen years was analyzed for fixing the sowing season (**Figure 5** and **6**).

*Tgms* lines were evaluated under sterility inducing Environments *viz.*, Coimbatore and Sathiyamangalam during the month of December 2013 & 2014 (Rabi 2013 & 2014). The same lines were stubble planted and evaluated for pollen sterility under pollen fertility inducing Environment during the month of July 2014 at high altitude (1500 MSL) with cool climate at Hybrid Rice Evaluation Centre, Gudalur. The new TGMS lines developed at TNAU *viz*., TNAU 45S, TNAU 60S, TNAU 95 S, TNAU 19S and TNAU 39S were evaluated for their stability of pollen sterility under different temperature regimes were given in the **Table 2**.

At sterility inducing Environments the lines showed 100% pollen sterility. These lines were seeded during December at Coimbatore and Sathiyamangalam to expose them to a sterility inducing temperature (>29°C /< 23°C day night) during panicle initiation to flowering stage to test their sterility behavior so that their critical stage

#### **Figure 5.** *Mean Weather data at Coimbatore Location.*

#### **Figure 6.**

*Mean Weather data at Gudalur Location.*

#### **Table 2.**

*Pollen sterility of different TGMS lines in Rice.*

of flowering coincides with more than 29°C. Mean weather data for both locations was provided in the **Figures 6** and **7** and it showed that the temperature recorded in both the places exceeded >25°C during the month of March, April and May. During the flowering stage all these lines showed 100% pollen sterility at both the locations for more than 60 days and was test verified for next year also.

*Progress and Prospects of Two Line Rice Breeding in India DOI: http://dx.doi.org/10.5772/intechopen.99545*

**Figure 7.** *Mean Weather data at Coimbatore and Sathyamangalam.*

The daily mean temperature of 24 to 26°C was found to be the critical temperature for fertility alteration. The sterile stubbles of these lines were planted at HREC, Gudalur during May to induce fertility for their seed multiplication and were exposed at critical stages to fertility inducing temperature (24°C / 18°C day / night). Maximum, minimum, and mean temperature significantly influenced the pollen and spikelet fertility in all five TGMS lines at high altitude. At Gudalur the temperature range during the month of July and August was less than 20°C. The appropriate sowing date of TGMS lines was fixed during June–July in such a way that the critical stages of panicle development would be exposed to the required temperature. The individual lines were maintained under isolation and genetically pure seeds were produced at Gudalur.

The above TGMS lines with wider pollen sterility period under plains can be very well exploited for developing two line rice hybrids during the period of December to April at Coimbatore. The same lines can be easily seed multiplied at Gudalur during July to November.

## **10. Two line hybrid rice seed production**

The TGMS seed produced from high altitude can be brought to locations like Coimbatore as proved above, where stable high temperature prevails during its sensitive stage for nearly 30 days. TGMS plants are planted in 6 rows and sandwiched with two rows of non TGMS good combiner lines on either sides. TGMS line must be randomly checked for complete pollen sterility during its sterile phase at high temperature. Supplementary pollination techniques as applied to three line system must be adopted such as GA3 spray, rope pulling, flag leaf clipping etc. to increase hybrid seed production. At the time of harvest care must be taken to harvest separately the non TGMS lines first. Seeds harvested from the TGMS line must be cleaned, packed, labeled, and sold to farmers as two line hybrid seed.

Hence Coimbatore and Gudalur locations were identified for the TGMS Seed multiplication and hybrid seed production in Tamil Nadu.

Based on the studied conducted at Department of Rice, Coimbatore and Hybrid Rice Evaluation Centre, Gudalur with Stability of TGMS lines compared with weather parameters over the ten years study resulted in the identification of season for seed multiplication and hybrid seed production as follows in the **Table 3**.

**Table 3.**

*Standardization of sowing season for the TGMS lines.*


**2. Conclusion**

L-ascorbic acid (also known as AsA or vitamin C) is a water-soluble vitamin with antioxidant properties. Its stability is essential for increasing resistance against various diseases, including premature aging, skeletal muscle weight loss, fiber atrophy, and decreased physical performance. A deficiency in vitamin C significantly reduces phagocytic activity, antibody levels, and lysozyme activity, which are crucial components of the body's immune response.


*2.1. Alantolactone*

The eudesmanolide alantolactone (**1**) (C15H20O2) (Figure 2) was first isolated from *Inula helenium* L. roots, and later was also found in other *Inula* species [24], as well as non-*Inula* spp. such as *Saussurea lappa* C.B. Clarke [25] and *Aucklandia lappa* DC. [26].

**Figure 2.** Structure of alantolactone (**1**).

Alantolactone (**1**) is obtained mainly by extraction and purification from natural sources, although its total synthesis is possible [27,28]. Modern laboratory techniques have resulted in a significant increase in its extraction yield [29–31]. For example, ~3% pure alantolactone (**1**) yields were obtained from *Inula helenium* roots by Zhao et al. [30], which meant a significant improvement over the ~1% yields from the roots of *Inula magnifica* Lipsky [32].

Although alantolactone is associated with allergic contact dermatitis triggered by *Inula* species [33–35], this compound was first described as anti-helminthic [36], with subsequent studies demonstrating its tremendous potential, mainly as antitumor [37–40], anti-inflammatory [26,41], and antioxidant [42] agent.

Regarding anticancer activity, the effect of alantolactone (**1**) on leukemia is well documented and was recently reviewed by Da Silva Castro et al. [43]. Xu et al. [44] reported a positive and significant effect (**1**) on B-cell acute lymphoblastic leukemia. In this study, 100 mg/kg (b.w.) doses were intravenously administered every two days in leukemia xenografted NOC/SCID mice. Results showed that treated mice lived an average of eight days more than non-treated mice (31.5 days after xenografting in treated compared to 23.5 days in non-treated mice), without significant weight loss. Before this study, Chun et al. [45] had also reported an in vivo antitumor effect on triple negative breast cancer in mice. The researchers used only a 2.5 mg/kg (b.w.) dose every two days to treat athymic nude mice xenografted with MDA-MB-231 cells. In this assay, alantolactone (**1**) reduced tumor size by over half, and significantly reduced tumor weight by about half after 24 days. Alantolactone (**1**) also exhibits in vivo activity against gastric cancer. Human gastric cancer cells (SGC-7901) were xenografted onto nude athymic mice, which were treated with 15 mg/kg (b.w.) of alantolactone (**1**) injected every two days [46]. Alantolactone (**1**) caused significant tumor growth inhibition and reduced Ki-67 and Bcl-2 expression (tumor associated proteins), without significant liver and kidney toxicity or impact on mouse weight [46].

Alantolactone (**1**) has also proven to be an especially potent sensitizing agent. In fact, it exhibits a synergistic effect with known chemotherapy drugs, such as oxaliplatin. Cao et al. [47] showed that a 10:2 mg/kg (b.w.) dose of alantolactone: oxaliplatin reduced tumor volume and weight by more than 50% in athymic mice xenografted with colorectal tumor cells (HCT116). The anticancer effect increased substantially when both compounds were used together, as opposed to alone. This result is in line with that obtained by He et al. [48], according to which, using a small weekly injected dose of 3 mg/kg (b.w.), tumor volume significantly decreased by around 75% in xenografted pancreatic cancer cells (PANC-1), while increasing cancer cell chemosensitivity to oxaliplatin, revealing a synergistic action.

Alantolactone's anticancer mechanism was also the main focus of several studies, some of them mentioned in recent reviews [49,50] The authors attribute its activity to the multiple pathways it activates. Alantolactone (**1**) acts as an alkylating agent leading to inhibition of key enzymes and proteins, and as an apoptosis inducer in cancer cells at mitochondrial level by interacting with cytochrome c. It promotes overproduction of reactive oxygen species (ROS) due to specific caspase activation, by inhibiting autophagic deregulation, among other processes.

Beyond its anticancer activity, compound **1** also exhibits interesting anti-inflammatory activity. Ren et al. [51] used the DSS-induced colitis mouse model to test alantolactone (**1**) anti-inflammatory activity. A 50 mg/kg dose reversed colitis symptoms (bloody diarrhea, colon shortening and weight loss), besides significantly reducing pro-inflammatory cytokine TNF-α expression (to about half that of positive control) and IL-6 (by over 2.5 times compared to positive control) [51]. Wang et al. [41], showed that a 10 mg/kg (b.w.) dose of alantolactone (**1**) significantly improved neurological function and reduced cerebral edema in a traumatic brain injury mouse model. This neuroprotective effect was attributed to alantolactone's capacity to inhibit the NF-κB inflammatory pathway and the cytochrome c/caspase-mediated apoptosis pathways [41]. To the best of our knowledge, no other authors have elaborated on this interesting double action: the fact that alantolactone (**1**) can simultaneously activate apoptotic pathways in cancer cells and inhibit these pathways in a cerebral edema model.

Seo et al. [52] described the neuroprotective effect of alantolactone (**1**) using amyloid β25–35- induced ex vivo neuronal cell death and scopolamine-induced amnesia in mouse models meant to emulate conditions common to neurodegenerative conditions like Alzheimer's disease. A 1 μM alantolactone (**1**) treatment increased cortical neuron viability to almost baseline control readings, and 1 mg/kg (b.w.) significantly decreased scopolamine-induced cognitive impairments. It is a interesting to note that this particular neuroprotective effect is attributed to a drop in ROS levels. High ROS concentrations are associated with the neurodegenerative damage of Alzheimer's disease. Again, alantolactone (**1**) exhibits a double action: it raises ROS levels to induce apoptosis in murine models of neurodegenerative damage.

The interesting advances with alantolactone (**1**) derivatives are also worth mentioning. Kumar et al. [53] assayed three of the 17 thiol derivatives synthesized, at 10 mg/kg (b.w.) doses, in mice showing that they have in vivo anti-inflammatory activity comparable to alantolactone (**1**). The novel compounds shared alantolactone's anti-inflammatory mechanisms. Another noteworthy study involving alantolactone (**1**) derivatives was published in 2018 by Li et al. [54], testing 44 derivatives for their ability to inhibit induced pulmonary fibrosis in mice. The results showed that 2 of these compounds are particularly active at 100 mg/kg (b.w.), reducing the fibrotic area by more than 60%. This is achieved by inhibition of the TGF-β1 pathway of myofibroblast differentiation. It should be noted that no toxic effects were observed for either compound in the chronic toxicity test (seven days), using a dose of 2 g/kg (b.w.) administered orally. Finally, a patent involving an alantolactone (**1**) spiro-isoxazoline derivative was filed in June 2019 (US patent N◦<PHONE_NUMBER>7) for the development and production of these compounds that exhibit significant anti-inflammatory activity.

One factor which contributes to making alantolactone (**1**) very interesting as a future medicine is its low toxicity. In work by Khan et al. [55], Kunming mice treated with 100 mg/kg (b.w.) alantolactone (**1**) showed no significant signs of hepatotoxicity or nephrotoxicity, in line with the previously cited work by He et al. [38]. This is especially important for alantolactone (**1**) as an anticancer drug, because it is well known that the liver and kidneys are particularly susceptible to negative side effects from chemotherapy approaches [56,57].

Another engaging finding obtained by Khan et al. [55] is alantolactone's ability to cross the blood-brain barrier. It may thus become useful in the treatment of brain tumors or other conditions involving the central nervous system (CNS), since the blood-brain barrier is the greatest obstacle for drug delivery to those areas [58].

The metabolism and pharmacokinetics of alantolactone (**1**) have also been studied in vivo, with future pre-clinical testing in mind. Research shows that alantolactone (**1**) exhibits low absorption and is rapidly eliminated after intravenous and oral administration. Its metabolism involves conjugation with thiol, and α, β-unsaturated carbonyl is the preferential structural metabolic site. The low aqueous solubility of alantolactone (**1**) causes low oral bioavailability [59–61].

All these recent reports show there is great interest in alantolactone (**1**), and that it has potent proven in vivo activities, mainly several different types of anticancer activity. This broad-spectrum activity, combined with its synergistic action with known cancer therapy agents, shows alantolactone (**1**) has great potential for future drug development. However, further research is necessary, especially clinical trials, to identify its intracellular action sites and secondary targets and thereby elucidate its mode of action.


**3. Results**

The growth and morphology of MSCs appeared rather heterogeneous in primary culture as seen in Fig 1A. Under a phase contrast microscope, the cells appeared fibroblast-like, elongated and spindle shaped with a single nucleus (Fig 1B). These cells showed the ability to form colonies with the occasional cell sphere formation giving an impression of embryoid bodies (Fig 1C). However, they progressively showed homogenous fibroblast-like features following subsequent subculture (Fig 1D).

#### **3.1 Morphology and growth of vitrified-thawed MSCs**

The duration of storage in frozen state for MSCs was two months. Post-cryopreserved MSCs from both the vitrification method and OPS vitrification had similar cellular morphology and colony-formation. Resuscitated MSCs first grew as isolated colonies after initial plating. Subsequently these adherent cells grew as typically fibroblastic or spindle shaped.

As the cells approached confluence, they assumed a more spindle-shaped, fibroblastic morphology (Fig 2A, B). The thawed and non-cryopreserved MSCs were subcultured until P15. Until the P9, fibroblast-like morphology was consistently observed in both the thawed and non-cryopreserved MSCs. At the P10, cells in both cultures became large and flat, suggesting senescence.

as above. The medium was changed every third or fourth day. One week after induction, adipogenic differentiation was evaluated by the cellular accumulation of neutral lipid vacuoles that were stained with oil-red O (Sigma) and observed under an inverted microscope (17). Briefly, after fixation in 5% metanol, induced MSCs were stained in filtered

To identify osteogenic differentiation, thawed and non-cryopreserved MSCs were cultured in 100 nM dexamethasone, 10 mM ß-glycerol phosphate and 50 μM ascorbic acid-2 phosphate in 400 μl DMEM-LG supplemented with 10% FBS on coverslips in a six-well plate for subsequent staining. During the culture period, the medium was changed once per week. After 14 days, osteogenic differentiation was evaluated by staining the coverslips with

For these assays, both thawed and non-cryopreserved cells were plated at 1 × 106 cells per ml and cultured for 14 days in 25 cm² tissue culture flasks. After 14 days, the cultures were stained with giemsa for 5 minutes. The formations of colonies were considered acceptable until passage 15 (P15) and those less than 2 mm in diameter or faintly stained were excluded.

Statistical analysis for comparison of the postthaw survival rate was performed using the χ2 test. Statistically significant values were defined as p<0.05. All experiments were conducted

The growth and morphology of MSCs appeared rather heterogeneous in primary culture as seen in Fig 1A. Under a phase contrast microscope, the cells appeared fibroblast-like, elongated and spindle shaped with a single nucleus (Fig 1B). These cells showed the ability to form colonies with the occasional cell sphere formation giving an impression of embryoid bodies (Fig 1C). However, they progressively showed homogenous fibroblast-like features

The duration of storage in frozen state for MSCs was two months. Post-cryopreserved MSCs from both the vitrification method and OPS vitrification had similar cellular morphology and colony-formation. Resuscitated MSCs first grew as isolated colonies after initial plating.

As the cells approached confluence, they assumed a more spindle-shaped, fibroblastic morphology (Fig 2A, B). The thawed and non-cryopreserved MSCs were subcultured until P15. Until the P9, fibroblast-like morphology was consistently observed in both the thawed and non-cryopreserved MSCs. At the P10, cells in both cultures became large and flat,

Subsequently these adherent cells grew as typically fibroblastic or spindle shaped.

oil red O for 2-3 hours and then rinsed with 60% isopropyl alcohol.

**2.4.2 Osteogenic induction**

fresh 0.5% alizarin red solution (1).

**2.4.3 Colony-forming unit assays**

following subsequent subculture (Fig 1D).

**3.1 Morphology and growth of vitrified-thawed MSCs**

**2.4.4 Statistical analysis**

suggesting senescence.

in triplicate.

**3. Results**

Fig. 1. Morphology and growth of MSCs. (A) Primary (x 40), (B) Passage 4 (14 days, x 100), (C) Passage 4 (35 days, x 100) and (D) Passage 4 (40 days, x 100). In primary culture, cell growth was scattered with some colony formation. Followin subsequent subculture, the cells changed into spindle-like fibroblasts.

Fig. 2. Morphology and growth of vitrified-thawed MSCs. Phase contrast images of MSCs two months after thawing from: (A) vitrification method (x 100) and (B) OPS vitrification (x 40). MSCs had a similar morphology to fibroblasts and were indistinguishable from noncryopreserved MSCs.

Cryopreservation of Rat Bone Marrow Derived Mesenchymal

**6. Differentiation of post-cryopreserved MSCs**

under the same condition (Fig 5C, D).

red without hematoxiline, x 100), (B).

marrow was first reported by Wang and Wolf (38).

**7. Discussion**

Stem Cells by Two Conventional and Open-Pulled Straw Vitrification Methods 9

After culturing for adipogenic differentiation, the accumulations of numerous neutral lipid vacuoles were detectable in the cytoplasm of vitrified-thawed cells (Fig 4A). Following three weeks of induction, oil Red O staining showed the lipid droplets with orange red color, which demonstrated the committed differentiation of MSCs into adipocytes (Fig 4B). The control cells showed no detectable lipid vacuoles. Under culture with osteogenic induction medium, resuscitated MSCs detached and floated in the medium. After three weeks, mineral accumulations were observed by alizarin red staining (Fig 5A, B). Similar results were observed in the group of non- cryopreserved MSCs when osteogenically induced

Fig 4. Evaluation of adipogenesis potential of MSCs under a phase-contrast microscope. Both the non cryopreserved MSCs and vitrified-thawed MSCs after treatment by adipogenic medium showed numerous neutral lipid vacuoles which accumulated in the cytoplasm. (A) Confirmed by oil red O staining (oil red O + hematoxiline, which one is the top right one: oil

Cryopreservation is an important method to maintain cells for biological research and medical applications such as tissue engineering, gene therapy, cell transplantation, pharmacological testing and future therapeutic indications (17, 28). A study on the longterm storage of BM-derived MSCs is of critical importance (1). The objective of the current investigation was to test the possibility that vitrification could be a useful method for the cryopreservation of MSCs. Thus, in the present study, we isolated MSCs from bone marrow of adult female rats. In culture; MSCs are characterized by their capacity to adhere to a plastic culture surface and form a fibroblast-like shape (Fig 1). Our data corroborated previous findings from other groups which showed homogenous fusiform features with oval vesicular nuclei (36) and the colony forming ability of MSCs, which decreased with increasing passages (37). The achievement of pure fibroblastic clones from murine bone

Furthermore, Eslaminejad et al. obtained an average of 15-17 clones, each one consisting of several fibroblastic cells per 24-well plate (39). This approach yielded both the number and