**4. Conclusions**

(1) The entire self-priming process of the self-priming centrifugal pump can be divided into three stages: gas-suction stage due to the impeller's rotating role in the initial self-priming stage, gas-suction stage due to the role of gas-water mixture and gas-water separation in the middle self-priming stage, and gas-suction stage due to the water flowing from the inlet section into the pump in the final

self-priming stage. Moreover, the self-priming time of the initial and final self-priming stages accounts for a small percentage of the entire self-priming process. The middle self-priming stage is the main stage in the self-priming process and determines the length of self-priming time.

(2) In the initial self-priming stage, the self-priming centrifugal pump is mainly based on drainage, and the gas is mixed with water before going outside the pump; it takes a certain amount of time for the gas to move from the inlet section to the outlet section. With the increase in the void fraction of the impeller, the drainage capacity of the self-priming pump is weakened, and the gas-exhausting rate is fast then slow. After entering the middle self-priming stage, due to the formation of a large negative pressure at the impeller inlet, the decreasing rate of the pressure continues to slow down on this basis, and the gas-suction rate of the self-priming centrifugal pump also slows down. In the final self-priming stage, the water in the inlet section goes into the impeller, whose power capability is enhanced obviously. The water mixed with the gas goes to the outlet section, so the gas-suction rate of the self-priming pump increased and reached the maximum value at a certain time. Afterward, the gas-suction rate decreased and finally approaches 0, due to the fact that the amount of gas in the pump decreases continuously.

(3) In the initial self-priming stage, the region near the suction surface of the impeller blade (low-pressure region) is easily occupied by gas. In the final self-priming stage, the region near the pressure surface of the impeller blade (high-pressure region) is easily occupied by water. In the middle self-priming stage, the impeller void fraction is high and a small amount of gas-water mixture exists in a small region close to the pressure surface of the impeller blade and impeller outlet. The key to successful self-priming is that the impeller's rotation forces a small amount of water mixed with some gas to flow along the pressure side of the impeller blade from the impeller inlet to the impeller outlet. Then, the mixture goes into the outward diffuser and flows along the pressure surface of the return diffuser, which shows that the gas-water mixture always flows along the high-pressure side of the pump in the self-priming process. The diffuser void fraction varies like the impeller void fraction, but its overall amplitude is lower.

(4) In the whole self-priming process, the backflow function of the backflow channel is the key to self-priming, and the pressure difference at both ends of the backflow channel is the main reason for the backflow function.

**Author Contributions:** B.H. and Y.Z. conceived and designed the experiments; X.W. performed the experiments; C.L. analyzed the data; C.W. and L.C. wrote the paper.

**Acknowledgments:** This research was funded by National Natural Science Foundation of China (Grant No.51609105), Jiangsu Province Science Foundation for Youth (Grant No. BK20170507), China Postdoctoral Science Foundation (Grant No.2016M601738 and 2018T110458). And the APC was funded by Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).

**Conflicts of Interest:** The authors declare no conflict of interest.


**3.1. Experimental BD in small animals**

A systemic inflammatory response is present in most patients at the diagnosis of BD [44]. The intense cellular and molecular activation that quickly follows the acute onset of BD involves acute transcriptional upregulation of inflammatory cytokines, both on a systemic and intraorgan level [16]. In liver, aside from microcirculatory changes, early effects of I/R injury and BD are mediated via Kupffer cells. When I/R injury occurs, Kupffer cells and neutrophils are activated, together with an increased expression of adhesion molecules and infiltration of monocytes and lymphocytes [9, 45]. Cell death by necrosis and apoptosis is initiated. In addition to mitochondrial damage, synthesis of oxygen-free radicals is boosted and accompanied by a decreasing activity of antioxidant enzymes. All of this results in immediate organ damage, contributing to hepatic failure after transplantation. In BD models many of these events have been also documented, exacerbating the hepatic damage that already occurs by I/R. In this sense, expression of adhesion molecules in endothelial and epithelial cells, initiating the release of proinflammatory cytokines and the infiltration of immune competent cells is induced by BD [16, 46–50]. VCAM-1 expression was found after 6 hours of BD in both hypotensive and normotensive donors. The pattern of VCAM-1 expression was similar to that described by others during periods of inflammation or rejection of the liver. Also, the ICAM-1 expression observed in all brain-dead rats was similar to expression patterns during episodes of inflammation [42, 51–55]. Leukocyte recruitment to the underlying parenchyma was facilitated, as expected, with upregulated VCAM-1 and ICAM-1 expression, with a significant increase in infiltrating leukocytes in the liver tissue of brain-dead rats. Also, naive as well as activated macrophages (i.e., ED1- and ED2-positive cells) were significantly increased in brain-dead donors versus controls [42]. The detrimental changes observed during experimental BD were confirmed in more limited clinical studies. Inflammatory changes were found in cadaveric donor livers that showed increased proportion of CD3+ lymphocytes, CD68+ monocytes, and macrophages relative to livers from living donors [33, 56, 57]. Clinical events after BD included more frequent acute rejection episodes and increased lymphocytic infiltrates [33, 56]. In human liver transplants, donor BD triggered upregulation of inflammatory cytokines IL-6, IL-10, TNF-α, TGFβ, IFN-γ, and MIP-1α. Those findings correlated with more pronounced cellular infiltrates, a higher incidence of primary graft dysfunction, and frequent acute rejection episodes [16, 58]. Findings in small animal models of BD parallel clinical observations [16], for instance upregulation of IL-10 and iNOS mRNA in liver has been demonstrated after 6 h of BD [39]. On the contrary, another study has not found iNOS induction in brain-dead animals. This contradictory result may be explained by the fact that the phase of

138 Organ Donation and Transplantation - Current Status and Future Challenges

BD in this later study was maintained for only 2–3 h [39].

**liver graft quality**

**3. Experimental models used to study the effect of brain death on**

Future research in experimental models of LT using BD donors is required to understand the pathophysiology of BD and elucidate the consequences of BD on graft function and survival [6]. The knowledge of the underlying mechanisms on the detrimental effects of BD is a critical question to promote better preservation of the organ and to improve outcome after LT [1]. Irreversible pontine ischemia is the essential hallmark of experimental BD. As hemodynamic instabilities related to cardiovascular collapse may bias experimental results, invasive monitoring is important for accurate determination of volume and maintenance of physiological blood pressure. The initial spike in blood pressure during brain stem herniation, apnea, and transient spontaneous reflexes with subsequent absence of spinal reflexes are characteristic criteria of the central catastrophe in experimental models of BD [16, 24, 28, 29, 31, 59]. To produce BD in small laboratory animals, a Fogarty balloon catheter (2–4 French) is inserted through a parietal bore hole into the subdural space and inflated. Reported inflation volumes for induction of the condition range from 200 to 500 μL in rats and 80–103 μL in mice. The inflated balloon catheter is left in place during the entire period of observation to avoid intracerebral hemorrhage and hemodynamic collapse. Computerized axial tomography or magnetic resonance imaging in brain-dead animals can document that the catheter inflation causes the hindbrain to herniate through the foramen magnum [16, 21, 28, 29, 60, 61]. Two types have been described: the so-called 'sudden onset' BD model and the 'gradual onset' model. In the gradual onset model described by [26], induction of BD was started by gradually increasing the intracranial pressure by inflating the balloon with 16 μL saline per minute. During balloon inflation, a hypotensive period occurred followed by a short peak and a subsequent drop in blood pressure. When the blood pressure returned to its basal level during an increasing peak, inflation of the balloon was stopped and anesthesia was withdrawn. The state of BD was confirmed 30 min after the onset of BD, by the absence of corneal reflexes and a positive apnea test, and such condition was maintained during 1 h or for 4 h [26]. In contrast to the sudden onset model, the gradual onset model simulates cerebral hemorrhage by slowly increasing the intracranial pressure, resulting in less hemodynamic instability and maintenance of normotension during BD for several hours. So far, only a few studies in abdominal organs have described BD induction using a gradual expansion of an intracranial balloon [24, 26].

Authors using a sudden onset model of BD [60] have demonstrated upregulations of proinflammatory markers in different organs, including the liver. BD was produced by rapid balloon inflation of a Fogarty arterial embolectomy catheter introduced into the subdural space through an occipital burr hole; this maneuver suddenly increases intracranial pressure and causes herniation of the brain stem within 20 min in all animals. The rats were tracheostomized and mechanically respirated for periods up to 6 h. [60]. Authors conclude that the experimental system described provides a potentially clinically relevant model in which to study the systemic effects of BD in detail and suggests means to prevent changes in peripheral organs [60]. Thus, it has been postulated that sudden onset BD reflects the situation of the hemodynamically instable donors [23]. In a sudden onset' BD model described by [23], through a frontolateral trepanation a balloon catheter was introduced in the subdural space with the tip pointing caudally. Inflating the balloon for 1 min increased the intracranial pressure thereby inducing rapid progressive brain injury leading to BD, which was based on the sharp rise and subsequent drop of blood pressure and heart rate. The state of BD was confirmed 30 min after induction by the absence of corneal reflexes and an apnea test and the effect of BD was studied at 1 h and 6 h after BD induction [23]. Hemodynamic changes in this model, mimicking acute isolated cerebral trauma, included first a sudden increase and subsequently a gradual drop of both heart rate and mean arterial pressure [23]. This BD model appears to be a reliable tool to mimic the type of fast occurring brain injury due to isolated cerebral trauma in man. In this BD model that reflects the situation of the hemodynamically instable donor, expression of immediate early genes was found in tissues of liver and kidney coinciding with progressive dysfunction of these organs and suggest a progressive detrimental effect of BD on donor organ quality, especially in livers from hemodynamically instable donors [23].

**Table 1** summarizes the experimental models used in small animals to study the effect of BD

Experimental Brain Death Models in Liver Transplantation

http://dx.doi.org/10.5772/intechopen.75438

141

In the baboon, BD is produced by creating intracranial hypertension. Under full inhalation anesthesia, a Foley catheter was introduced into the subdural space through a frontal burr hole in the skull and filled with 20–30 mL of saline, then BD occurred within 20 min [62, 63]. BD has been also induced in pigs by ligation of both brachiocephalic arteries, from which arise the carotid and vertebral arteries. Both experimental models of BD led to a series of major pathophysiological changes that may be collectively referred to as the autonomic storm. Though there was a brief initial period of excessive parasympathetic activity, evidenced by a marked bradycardia, most of the effects of this autonomic activity were brought about by the

Porcine BD models have been widely used in transplant surgery studies. A research group [64] performed a study with a lengthening of the induction phase to 60 min with gradual epidural balloon inflation up to 15 mL. Furthermore, they suggested that prolonged BD process might give the necessary preconditions to trigger a systemic inflammatory response that might contribute to organ dysfunction. Compared to the clinical situation, other authors [27] considered that 60 min was still a relatively short time. They therefore wanted to prolong the BD induction phase up to 200 min by stepwise increase of intracranial volume, followed by a 30-min observation period. Analysis of the monitoring results showed a classic intracranial pressure-volume relationship. Furthermore, intracranial compliance decreased gradually when intracranial pressure increased, which reflects a decreasing ability to compensate for added intracranial volume [27]. The described so-called Cushing response with arterial blood pressure increase, bradycardia, and respiratory irregularities was also demonstrated [27]. Authors believe that their model has the advantage of applying gradual progressive changes in intracranial pressure, cerebral perfusion pressure, and brain tissue oxygenation, leading to cellular injury in parenchymatous organs. Therefore, the model provides the possibility

**Article Type of BD Time of sustained BD Support during BD Cold ischemia**

Ref. [23] Sudden 1 or 6 h Ventilation Not realized

Ref. [31] Gradual and sudden 6 h Ventilation Not realized

Ref. [60] Sudden 6 h Ventilation Not realized

**Table 1.** Experimental models in small animals used to study the effect of BD on liver graft quality.

Inotropic support

Inotropic support

Not realized

Not realized

Ref. [1] Sudden 6 h Ventilation 6 h

Ref. [26] Gradual 1 or 4 h Ventilation

Ref. [42] Sudden 1 or 6 h Ventilation

on liver graft quality.

**3.2. Experimental BD in large animals**

sympathetic nervous system [63].

**Small animals**

In these experimental models described above, only the effect of induction of BD on liver tissue was evaluated. Unfortunately, the other conditions (ischemia and reperfusion) that are present in the clinical practice of transplantation were not included. It is important to evaluate such conditions, since they negatively affect liver grafts already damaged by BD when these grafts, especially steatotic ones, are obtained from cadaveric donors.

Given the prevalence of hepatic steatosis in the population, this represents a large potential pool of donors. However, the clinical problem is still unresolved as steatotic livers are more susceptible to I/R injury and, when used, have poorer outcome than non-steatotic livers [6]. To date, only one experimental study has evaluated the effect of BD on optimal and steatotic liver grafts, which were subsequently subjected to cold ischemia and transplantation. In such research [1], authors have tested the influence of hepatic steatosis and BD separately and in combination in an experimental model of LT [1]. A balloon catheter was introduced through the drill hole in the extradural space with the tip pointing caudally. The intracranial pressure was increased by inflating the balloon for 1 min. The increase in intracranial pressure induced rapid brain injury, leading to immediate BD, simulating a condition comparable to acute isolated cerebral trauma in humans. The state of BD was confirmed by the absence of corneal reflexes and an apnea test and liver grafts were extracted from donors at 6 h after the induction of BD [1]. Steatotic and non-steatotic liver grafts were preserved during 6 h and then were transplanted and submitted to reperfusion for 4 h. Authors report for the first time that the injurious effects of BD in LT are exacerbated in the presence of hepatic steatosis and occur before liver grafts are retrieved from donors [1]. In addition, the mechanisms responsible for the detrimental effects of BD were different depending of the type of the liver, which would interfere with protective pharmacological or surgical strategies applied in liver grafts, avoiding its potential benefits [6]. In such a study, BD-induced dysfunction in the cholinergic anti-inflammatory pathway and prevented the benefits induced by ischemic preconditioning, a surgical strategy that shows benefits when it is applied in non-BD clinical situations. In fact, the study indicated that the combination of acetylcholine and ischemic preconditioning could be considered as a feasible and easy-to-perform intervention to reduce the adverse effects of BD and improve the quality of liver grafts. So authors propose that the time frame between the declaration of BD and organ retrieval provides an important window for cytoprotective intervention, which may counteract the detrimental effects of BD [1].

**Table 1** summarizes the experimental models used in small animals to study the effect of BD on liver graft quality.


*Article* **3D Numerical Study of the Impact of Macro-Roughnesses on a Tidal Turbine, on Its Performance and Hydrodynamic Wake**

**Ilan Robin \*, Anne-Claire Bennis and Jean-Claude Dauvin**

Morphodynamique Continentale et Côtière (UMR CNRS 6143), Université de Caen Normandie, Rouen Normandie, 14000 Caen, France<EMAIL_ADDRESS>(A.-C.B<EMAIL_ADDRESS>(J.-C.D.)

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Biofouling is an important factor to consider when calculating the energetic efficiency of tidal farms. Despite the fact that biofouling effects have been widely investigated in the past for naval applications, very few studies concern tidal turbines. This paper proposes a numerical approach to assess the impact of biofouling on tidal turbines, which is efficient for testing many configurations. Two turbulence models are tested (RANS k-*ω* SST and LES Smagorinsky) for the motionless blade case to validate them. Then we chose to use the Smagorinsky model for the case of a complete tidal turbine rotor with realistically fouled blades. The pressure coefficient is strongly affected by the barnacle in the motionless blade case and the power coefficient is slightly degraded in the complete rotor case. Motionless blade cases do not represent the real biofouling behaviour for two reasons. First, sessile species settle in the down flow part of the chord where their impact is less important. Then, the surrounding turbulence provoked by the blades rotation in the rotor case reduces the impact of biofouling. In the wake, biofouling generates small vortexes that propagate into the larger ones, causing them to spread their energy.

**Keywords:** marine renewable energy; tidal energy; fluid–structure interaction; biofouling; turbulence; numerical modelling


**1.2. Geologic setting and lithostratigraphy**

The present Nile Delta lies on the north\_external margin of the African plate. Its structural evolution is largely contemporaneous with the break–up of the African plate margin conse‐ quent to the opening of the Red Sea and the northward transition of the Arabian Peninsula [1, 2]. The northern part of the Nile Delta is characterized by a series of major tectonic features with different orientations. Some of them are active during the deposition of Tertiary section while other flatten with depth, become horizontal and dies on top of the Rosetta Formation affecting only through the Kafr El-Sheikh Formation.

The Nile Delta is characterized by asymmetric fold, overthrust faults and diapers. Its dates back to the Syrian Arc system having an arcuate trend from northeast to southwest through the northern part of the Nile Delta to the Western Desert of Egypt (Figure 2).

The regional subsurface stratigraphic succession of the Nile Delta has been represented in Figure 3. This succession is discussed by many authors [3-11].

The lithostratigraphy of the Nile Delta is represented by Miocene sediments:

Moghra, Sidi Salem and Abu Madi formations (from bottom to top). In addition, the Pliocene– Pleistocene sequences are represented mainly by shale or clay with sandstone interbeds. The Miocene sequence is summarized as follows:

**Figure 2.** Regional structural setting, north Nile Delta from Mosconi et al. [38] and Hemdan et al. [39].


**5.2 Construction for cache demand matrix**

According to the parameters described in Section 5.1, we can construct the cache demand matrix, which is the total cache needing of all cache files, and as followings:

```
1. For CFi ( 1≤i≤m ) do
```
{Calculate the duplication width vector

DWVi (|SEGi1|, |SEGi2| … |SEGiw|) for CFi by Segli , where iw is the numbers of the segment of CFi, and |SEGij| is the duplication width of j th segment of CFi};

In CDM, the sum value Tcd= 1 i m, 1 j k *qij* is the total cache demand of all cache files, and

we hope the formula (1) is true. In fact, it is not possible.

$$\text{Tcd} \mathsf{STc} \tag{1}$$

Research and Implementation of Parallel Cache Model Through Grid Memory 145

**Strategy 1.** *d-density* **allocation strategy** means that the allocation ratio between the numbers of *CA* and the numbers of their *DSEG* for one segment in the same cache node is *d*. Namely,

Fig. 5. This is the compress storage in a cach node for the same duplication and it is the 3-

Compress

In order to use the storage of grid memory effectively, we hope *CDM*=*NCDM*. Through the *d-density* allocation strategy**,** we can make the formula (2) is true, where *Tcd*(*NCDM*) means

Tcd(NCDM)<Tc d (2)

For all cache nodes *CNk* (1≤*k*≤*p*) , we get a subset of *NCDM* elements SE to make the formula

is a small constant. For each *DSEG*s, we adopt the d-density allocation strategy

CN

DSEG1

CA

CA

CA

Cut-matrix

*CDM-NCDM*

Fig. 4. The relations between CDM, Cut-matrix, and NCDM

*NCDM*

CN

DSEG1

*CDM*

DSEG1

DSEG1

density allocation strategy.

**5.4 Cache allocation**

(3) is true*.*

to appoint CA.

that the total cache demand of *NCDM*.

one *DSEG* has *d CA*s in the same cache node. *d* is the average in PCMGM.

CA

CA

CA

When *Tcd*≥*Tc*, we can processes the *CDM* by the techniques of cut-matrix and compress in order to make the formula (1) is true.

#### **5.3 Cut-matrix and compress technique**

The *cut-matrix* is a special *m×k* matrix *CM=(cqij)* (1≤*i*≤*m*, 1≤*j*≤*k*), where *cqij=Cut-v* and *Cut-v* is a integer. The process is presented as follows:

The *NCDM* is the new cache demand matrix and all its *DSEG*s can be cached in grid memory. The demand of *CDM-NCDM* will be save the disk. The cut-matrix is presented in the figure 4.

According to the *CA* conception, the corresponding relation between a *CA* and a *DSEG* is one to one. But if a set of duplications of the same segment of be stored in the same cache node, the duplication will be stored one time in the cache node. The corresponding cache agent is established many times in the cache node, so this mechanism can improve the use rate of memory resources, but the computing ability of the cache node may be overload. The average ratio of the *CA* numbers and the *DSEG* numbers is called as the *cache agent density*  (CAD). The compress principle is presented in the figure 4.

144 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

DWVi (|SEGi1|, |SEGi2| … |SEGiw|) for CFi by Segli , where iw is the numbers of the

2. Look for the cache file which has the maximal segment number in all cache files, and

3. Through the vector set DWV1, DWV2 … DWVm and k, we construct the cache demand matrix: CDM= (qij) (1≤i≤m, 1≤j≤k), Where qij is the duplication width of the jth segment

When *Tcd*≥*Tc*, we can processes the *CDM* by the techniques of cut-matrix and compress in

The *cut-matrix* is a special *m×k* matrix *CM=(cqij)* (1≤*i*≤*m*, 1≤*j*≤*k*), where *cqij=Cut-v* and *Cut-v* is

*NCDM*=*CDM*; /\* *NCDM* is the cut *CDM* that can satisfy the formula (1)\*/

/\* - is the subtraction of Matrixes and if

Until the formula (1) is true;

*NCDM*=*0*, then *NCDM*-*CM*=*0, 0* is zero-matrix

Set all elements of *CM* as *Cut-v*;

Calculate the *Tcd by NCDM*;

*NCDM=NCDM*-*CM*;

The *NCDM* is the new cache demand matrix and all its *DSEG*s can be cached in grid memory. The demand of *CDM-NCDM* will be save the disk. The cut-matrix is presented in

According to the *CA* conception, the corresponding relation between a *CA* and a *DSEG* is one to one. But if a set of duplications of the same segment of be stored in the same cache node, the duplication will be stored one time in the cache node. The corresponding cache agent is established many times in the cache node, so this mechanism can improve the use rate of memory resources, but the computing ability of the cache node may be overload. The average ratio of the *CA* numbers and the *DSEG* numbers is called as the *cache agent density*

(CAD). The compress principle is presented in the figure 4.

is the total cache demand of all cache files, and

*Tcd*≤*Tc* (1)

segment of CFi, and |SEGij| is the duplication width of j th segment of CFi};

of CFi, and qij ≥0; If the jth segment of CFi is not existed, then qij=0.

*qij*

1 i m, 1 j k

we hope the formula (1) is true. In fact, it is not possible.

the maximal segment number is k;

In CDM, the sum value Tcd=

the figure 4.

order to make the formula (1) is true.

**5.3 Cut-matrix and compress technique**

a integer. The process is presented as follows:

*Cut-v*=0; Repeat do *Cut-v*++;

\*/

Fig. 4. The relations between CDM, Cut-matrix, and NCDM

**Strategy 1.** *d-density* **allocation strategy** means that the allocation ratio between the numbers of *CA* and the numbers of their *DSEG* for one segment in the same cache node is *d*. Namely, one *DSEG* has *d CA*s in the same cache node. *d* is the average in PCMGM.

Fig. 5. This is the compress storage in a cach node for the same duplication and it is the 3 density allocation strategy.

In order to use the storage of grid memory effectively, we hope *CDM*=*NCDM*. Through the *d-density* allocation strategy**,** we can make the formula (2) is true, where *Tcd*(*NCDM*) means that the total cache demand of *NCDM*.

$$\text{Tcd(NCDM)} \lhd \mathbf{Tc} \times \mathbf{d} \tag{2}$$

### **5.4 Cache allocation**

For all cache nodes *CNk* (1≤*k*≤*p*) , we get a subset of *NCDM* elements SE to make the formula (3) is true*.* is a small constant. For each *DSEG*s, we adopt the d-density allocation strategy to appoint CA.

$$\sum\_{1 \le i \le p,\atop 1 \le i \le p} \text{Mai}\_{i - \mathcal{E}} \le \sum\_{q \ne \mathcal{E} \mid \mathcal{E}} q\_{ij} \le \sum\_{1 \le i \le p,\atop 1 \le i \le p,\atop 2 \le i \le p} \text{Mai}\_{i} + \mathcal{E} \tag{3}$$

Research and Implementation of Parallel Cache Model Through Grid Memory 147

1. AA do the statistics work from user information and report them to GA; GA calculates the cache demand matrix CDM by the statistics information, and get a LCC (That is

2. All NAs, which LCC includes, calculate its memory storage ability Ma and its

3. GA receives the idle resource information from LCC and calculates the total computing

4. GA constructs the NCDM according to the cut-matrix and d-density allocation

6. All NAs of CN transfer the segments form the file server into grid memory in order to

8. All users commit its access sequences to AA, AA redirect the connections to build the

9. All CAs and users start the file operation in Parallel and PCMGM start the dynamic

In order to test this model, we built a *DNE* that is composed of 2 file servers and 8 cache nodes (personal computer), and 16 client computers, and they are connected by LAN. Each server has 4 disks, and the segments of large file are distributed in all disks in balance. There are 4 files and the length of all them is about 1GB. The segment length is 48MB. The cache nodes are classified into 2 types according to their types of cpu, memory, disk, and net adapter. The types of computers are *RSV* (2400MHZ, 768MB, 7200RPM, 100M) and *RSV* (1800MHZ, 512MB, 7200RPM, 100M). The operating systems of the computers are the LINUX. In order to test the peak access ability about the hot segment, we ignore the net factors. So, the test agent running on client end only sends the access command timely and the *CA* does the file operations and the data will be not transferred to the client end. The

**Experiment 1.** The experimentation includes two kinds: the one is the response time testing in the condition that all *DSEG*s are stored in the file server disks; the other is the response time testing in the condition that all *DSEG*s are stored in Grid memory. We calculate the average ratios of response time of this two kinds. The tests include 6 times according to the *DSEG* scales. The total number of *DSEGs* is the *Tc*, 2*Tc*… 6*Tc* during the 6 times testing. In each test, we adopt the three access method for the *DSEG*s: the random access, the sequence access, and the mix access composed of the random and sequence. The test results are shown in figure 6(a), and the results show that PCMGM is high efficiency compared with

**Experiment 2.** We had tested the variety of PCMGM performance during d is 1, 2, 3, 4, 5, and 6 (The total number of DSEGs is the Tc, 2Tc… 6Tc). The test results are shown in figure

**6. Process of PCMGM**

The PCMGM process is as follows:

computing ability Ca;

5. GA allots DSEGs for all CNs;

relations between the users and CAs;

adjusting mechanism by the migration.

average value of *d*-density allocation strategy is 6.

strategy;

**7. Experiments**

the disk cache method.

constructed by GA through fuzzy partition method);

ability Gcc and the total memory storage ability Gmc;

form the DSEGs; then, construct CAs for each DSEG; 7. All CAs in PCMGM start the hot segment service;

The *DSEG*s in *CDM-NCDM* will be written into the local disk of cache node.


**4.3 Capacitance measurements**

The depletion layer width and built-in potential of the GaN layer in the TCP/GaN heterojunction solar cell were estimated using a capacitance measurement setup. A solartron 1255B frequency response analyzer was utilized for the measurement. The sample was set on a sample stage, which was in a vacuum chamber to avoid any influences from light and humidity.


*Article Clostridioides difficile* **Infection among Cirrhotic Patients with Variceal Bleeding**

**Mirela Nicoleta Voicu 1 , Florica Popescu 1 , Dan Nicolae Florescu 2 , Ion Rogoveanu 2 , Adina Turcu-Stiolica 3,\* , Dan Ionut Gheonea 2 , Vlad Florin Iovanescu 2 , Sevastita Iordache 2 , Sergiu Marian Cazacu 2 and Bogdan Silviu Ungureanu 2**

**Abstract:** *Clostridioides difficile* infection (CDI) stands as the leading cause of nosocomial infection with high morbidity and mortality rates, causing a major burden on the healthcare system. Driven by antibiotics, it usually affects older patients with chronic disease or immunosuppressed or oncologic management. Variceal bleeding secondary to cirrhosis requires antibiotics to prevent bacterial translocation, and thus patients become susceptible to CDI. We aimed to investigate the risk factors for CDI in cirrhotic patients with variceal bleeding following ceftriaxone and the mortality risk in this patient's population. We retrospectively screened 367 cirrhotic patients with variceal bleeding, from which 25 patients were confirmed with CDI, from 1 January 2017 to 31 December 2019. We found MELD to be the only multivariate predictor for mortality (odds ratio, OR = 1.281, 95% confidence interval, CI: 0.098–1.643, *p* = 0.042). A model of four predictors (age, days of admission, Charlson index, Child–Pugh score) was generated (area under the receiver operating characteristics curve, AUC = 0.840, 95% CI: 0.758–0.921, *p* < 0.0001) to assess the risk of CDI exposure. Determining the probability of getting CDI for cirrhotic patients with variceal bleeding could be a tool for doctors in taking decisions, which could be integrated in sustainable public health programs.

**Keywords:** *Clostridioides difficile*; risk factors; Charlson comorbidity index; Child–Pugh score

#### **1. Introduction**

*Clostridioides difficile* infection (CDI) represents the leading cause of nosocomial infection and is associated with high morbidity and mortality as well as increased healthcare costs [1]. A European multicentre study involving 482 hospitals showed a rate of seven cases per 10,000 patients and also revealed a suboptimal laboratory diagnosis in the eastern European countries [2]. While it seems that CDI is shifting towards community spreading, there are still many hospital-acquired cases, especially in patients with comorbidities and if antibiotics are used.

Chronic disease, older age, immunosuppressive and oncologic medication are considered risk factors for patients acquiring hospital CDI [3]. Additionally, the increased use of proton pump inhibitors (PPI) [4] and nonsteroidal anti-inflammatory drugs (NSAID) [5] has been associated with an increase in CDI. Even though any antibiotic may be incriminated for inducing hospital-based infection by disrupting the gut barrier, clindamycin, cephalosporins and fluoroquinolones have been frequently linked to CDI [6]. Liver cirrhosis is the 13th cause of death worldwide, even though the viral aetiology seems to be

**Citation:** Voicu, M.N.; Popescu, F.; Florescu, D.N.; Rogoveanu, I.; Turcu-Stiolica, A.; Gheonea, D.I.; Iovanescu, V.F.; Iordache, S.; Cazacu, S.M.; Ungureanu, B.S. *Clostridioides difficile* Infection among Cirrhotic Patients with Variceal Bleeding. *Antibiotics* **2021**, *10*, 731. https://doi.org/10.3390/ antibiotics10060731

Academic Editor: Guido Granata

Received: 1 May 2021 Accepted: 15 June 2021 Published: 17 June 2021

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diminishing [7]. When portal hypertension is present, oesophageal varices will appear and approximately 20% will bleed in the first three years from diagnosis. Despite standard endoscopic therapy for oesophageal varices, some patients will rebleed and other complications may occur either due to portal hypertension or liver insufficiency [8].

Cirrhotic patients are vulnerable to developing CDI infection due to their frequent admission and infections, as well as dysbiosis and a low immune system [9]. Bacterial infection is frequently encountered in cirrhotic patients with variceal bleeding. Both the American Association for the Study of Liver Diseases (AASLD) and the European Association for the Study of the Liver (EASL) recommends the use of ceftriaxone as a prophylaxis for bacterial infection, rebleeding and reduced hospital stay [10,11]. Nonetheless, patients admitted with variceal bleeding have a higher mortality rate and are usually present in an advanced cirrhotic stage with additional possible complications. While the use of ceftriaxone is recommended for 5–7 days, this time, it might be enough for patients to acquire CDI depending on their status.

Additional methods might be required to prevent widespread CDI incidence in cirrhotic patients. Both local and global strategies should ensure new public health actions to consider sustainability in restraining the rate of CDI for vulnerable entities such as cirrhotic patients [12]. Despite the prevalence of CDI among hospitalized patients, few studies assessed the impact of CDI on patients with cirrhosis and variceal bleeding. The aim of our study is to provide an overview on patients with cirrhosis and variceal bleeding, which developed CDI after antibiotic prophylaxis.

#### **2. Materials and Methods**

#### *2.1. Study Design*

This was a retrospective, single-centre cohort study involving the assessment of information from cirrhotic patient records with or without CDI, who presented to the University County Hospital of Craiova, Romania, from 1 January 2017 to 31 December 2019. All patients signed an informed consent form at the hospital upon admission, conforming to the Declaration of Helsinki, 1967. The study was approved by the University of Medicine and Pharmacy of Craiova Ethics Commission no. 88/2020.

The patients-related variables included demographics, age, alcoholism, comorbidities, previous use of PPI, or antibiotics. Medical history was registered: cardiovascular and pulmonary diseases, diabetes mellitus, chronic renal failure, hepatocellular carcinoma (CHC). The variables related to admission were analysed: Child–Pugh score (total bilirubin, albumin, INR, ascites, encephalopathy), Atlas score (age, systemic antibiotics during CDI therapy, leukocyte count, albumin, creatinine), MELD (dialysis at least twice in the past week, creatinine, bilirubin, INR, sodium), Charlson comorbidity index (age, myocardial infarction, peripheral vascular disease, history of cerebrovascular accident, dementia, COPD, connective tissue disease, peptic ulcer disease, liver disease, diabetes mellitus, hemiplegia, chronic kidney disease, solid tumour, leukaemia, lymphoma, aids), albumin (g/dL), C-reactive protein (CRP, mg/dL), leukocytes (cells/µL), neutrophils (%), erythrocytes (cells/µL), haemoglobin (g/dL), haematocrit (%), platelet count (cells/µL), creatinine (mg/dL), urea (mg/mL), glomerular filtration rate (mL/min/1.73 m2 ), Na (mEq/L), K (mEq/L). Costs were also considered in the outcomes' analysis.

#### *2.2. Patient Admission Protocol*

We only included patients ≥ 18 years old, admitted with variceal bleeding secondary to liver cirrhosis at their first episode. We followed the European Society of Gastrointestinal Endoscopy (ESGE) and American Gastroenterological Association (AGA) guidelines for variceal bleeding with immediate resuscitation, if necessary, terlipressin prescription as well as antibiotic use to prevent bacterial dissemination. Endoscopy was performed as soon as possible, pointing out either oesophageal or gastric varices. Endoscopic signs such as active bleeding, oozing and white nipple are red signs requiring immediate therapy. Band ligation was performed for oesophageal varices and sclerotherapy with n-butyl-2-

cyanoacrylate for gastric varices. According to their status, patients were followed up for the next few days in the intensive care unit (ICU) or in our clinic with medical management. If rebleeding reappeared, another upper endoscopy was performed. All patients received antibiotics (ceftriaxone) according to the European Association for the Study of the Liver (EASL) recommendations [11] before or after endoscopy. Patients were also given lactulose to prevent hepatic encephalopathy.

Patients were excluded if they previously received treatment with metronidazole or vancomycin, had a recent positive test for CDI and had CDI treatment before testing. Additionally, if other antibiotics were used during their hospitalization before CDI positive results, patients were not included. Patients redirected from other hospitals that did not respect our upper gastrointestinal bleeding (UGIB) protocol were not included.

#### *2.3. CDI Diagnosis*

Patients developing symptoms 48 h after admission with more than 3 watery stools/per day for two consecutive days associated with abdominal pain were tested for CDI by A/B stool assay. Results were further confirmed by an enzyme immunoassay (EIA) for detecting glutamate dehydrogenase according to the European Society of Clinical Microbiology and Infectious Disease (ESCMID) [13]. We also checked the prescription records for all patients, as well as if other antibiotics were prescribed in the previous months, recent admissions for other conditions. Only one test result was used in this study. Patients received vancomycin or vancomycin +/− metronidazole depending on disease severity and therapeutic response.

#### *2.4. Statistical Analysis*

Continuous variables for the study sample were described as using mean and standard deviation or median (interquartile range). Percentages were used to describe the categorical variables. Comparison among groups was conducted with the Mann–Whitney U test for continuous variables and with the chi-square test for categorical variables. We created violin plots to visually compare measured parameters between two groups. Violin plots are like box plots, but also show the probability density of the data at different values. Univariate and multivariate analyses were performed to find the significant independent predictors of mortality. Variables that had a univariate association with overall mortality in CDI patients at *p*-value < 0.5 were included in the multivariate analysis. The same method was used to identify the risk factors of CDI. Correlogram with hierarchical clustering of covariates was drawn to reveal the best models for predictors of CDI. The Hosmer and Lemeshow test was used to assess how well the model fit with the data (the null hypothesis is that the model is an adequate fit). The 95% confidence interval for the odds ratio was assessed for every predictor. The area under the receiver operating characteristic (AUC) and its 95% CI was used to assess prognostic accuracy for models. A model is considered outstanding if AUC is bigger than 0.9, excellent if AUC is between 0.80 and 0.89, and acceptable if its AUC is between 0.7 and 0.8, and its 95% CI of AUC exceeds 0.7. All results were considered significant at a significance probability below 5%. All data were analysed using the GraphPad Prism 9.1.0 software (GraphPad Software, San Diego, CA, USA) and R *corrplot* and *pROC* packages (version 4.0.3).

#### **3. Results**

#### *3.1. Patient Characteristics*

A total of 367 patients with cirrhosis were identified during the study period, meeting inclusion criteria. From these patients, 25 patients were confirmed with CDI. The clinical features, comorbidities, and characteristics of patients with CDI are summarized in Table 1. The mean age of cirrhotic patients with CDI was 65.8 years (SD: 8.06 years), and 76% of the patients were male.

**Table 1.** Demographics and clinical characteristics of the study population.

PPI, proton pump inhibitor; ICU, intensive care unit; SBP, spontaneous bacterial peritonitis; CRP, C-reactive protein.

There were no significant differences in MELD value, the level of haemoglobin, platelets, and urea between patients with CDI and without CDI. The number of the days of admission was higher for patients with CDI (Table 2). The Child–Pugh score was significantly higher for the CDI group (*p* < 0.0001). Patients with cirrhosis and CDI had higher albumin and creatinine level than the patients with cirrhosis, but without CDI. The percentage of deaths was lower in the group of patients without CDI (*n* = 87, 25%) than in the group of patients with CDI (*n* = 11, 44%). Cirrhotic with CDI patients had a significantly higher costs (EUR 1502.45 vs. 998.31, *p*-value = 0.0006) compared with those without CDI (on an average were 1.5-fold greater). Cirrhotic patients with CDI had a higher Charlson index than cirrhotic patients without CDI (8.64 vs. 6.31, *p*-value = 0.001).

There was a modest trend for a higher rebleeding rate in patients without CDI at 7% compared with 4% for CDI patients; however, this did not reach the level of statistical significance (*p*-value = 0.53). There was a statistically significant trend for higher outpatients PPI usage in the patients with CDI at 92% compared with 48% without CDI.

The comparison of the violin plots demonstrated that patients with CDI tended to have a more skewed distribution of Charlson index, Child–Pugh score, costs, creatinine, urea or admission's days than patients without CDI, with their values toward higher values, as shown in Figure 1.

**Table 2.** Comparison of Cirr+CDI+ and Cirr+CDI−. Child–Pugh score 11.20 (2.14) 12 (9–13) 9 (7–10) <0.0001 \*

Death (yes) 11 (44%) 89 (25.6%) 0.0446 \* Rebleeding rate 1 (4%) 23 (6.73%) 0.5321

*Antibiotics* **2021**, *10*, x 5 of 13

**Cirr+CDI−**

8.08 (5.22)

7.9 (3.83)

*n* **= 342** *p***-Value**

7 (5–10) <0.0001 \*

**Table 2.** Comparison of Cirr+CDI+ and Cirr+CDI−.

*n* **= 25**

12 (9–19)

**Characteristics Cirr+CDI+**

Mean (S.D.) and median (interquartile range) for continuous variables; *n* (percentage) for categorical variables; MELD, Model for End-Stage Liver Disease; Cirr+CDI+, cirrhotic with CDI patients; Cirr+CDI−, cirrhotic without CDI patients; \*, *p*-value < 0.05. urea or admission's days than patients without CDI, with their values toward higher values, as shown in Figure 1.

**Figure 1.** Violin plots showing the expression distribution of values of variables between Cirr+CDI+ and Cirr+CDI− patients. \*, *p*-value < 0.05; \*\*, *p*-value < 0.01; \*\*\*, *p*-value < 0.001; \*\*\*\*, *p*-value < 0.0001.

*3.2. Factors Associated with Mortality in Cirrhotic Patients with CDI*

As shown in Table 3, univariate predictors of mortality for the cirrhosis and CDI patients included Child–Pugh score (*p*-value = 0.026), leukocytes (*p*-value = 0.023), CRP (*p*-value = 0.012), Atlas score (*p*-value = 0.011) and MELD (*p*-value *=* 0.022).

**Table 3.** Univariate regression to explore factors associated with mortality.

COR, crude odds ratio; PPI, proton pump inhibitor; ICU, intensive care unit; SBP, spontaneous bacterial peritonitis; MELD, model for end-stage liver disease; \*, *p* < 0.05, statistically significant.

According to the multivariate model, MELD is a predictor of mortality for patients with cirrhosis and CDI (Table 4). The logistic regression model was statistically significant, χ 2 (6) = 7.21, *p* = 0.032. The model explained 74.5% (Nagelkerke R2 ) of the variance in mortality and correct classified 96% of cases. Increasing MELD was associated with an increase in the likelihood of mortality.

**Table 4.** Multivariate regression to explore factors associated with mortality.

AOR, adjusted odds ratio; MELD, model for end-stage liver disease; \*, *p* < 0.05, statistically significant.

#### *3.3. Risk Factors Associated with CDI in Cirrhotic Patients with Variceal Bleeding*

Table 5 summarize the results of the univariate analysis to describe the risk factors associated with CDI. Higher age (OR = 1.062; 95% CI: 1.017–1.109), higher length of hospital admission (OR = 1.115; 95% CI: 1.062–1.171), liver cancer (OR = 3.173; 95% CI: 1.245–8.087), the use of proton pump inhibitor (OR = 12.902; 95% CI: 2.995–55.566), higher level of urea (OR = 1.013; 95% CI: 1.006–1.020) and a higher Charlson index (OR = 1.609; 95% CI: 1.33–1.947) were the risk factors for CDI during the hospitalization.

**Table 5.** Univariate regression to explore factors associated with CDI.

COR, crude odds ratio; \*, *p* < 0.05, statistically significant.

Multivariate analysis showed that a higher age (OR = 1.067; 95% CI: 1.004–1.134), longer hospital stay (OR = 1.159; 95% CI: 1.086–1.238), higher level of urea (OR = 1.013; 95% CI: 1.002–1.023), higher Charlson index (OR = 1.671; 95% CI: 1.326–2.106) and the use of proton pump inhibitor (OR = 23.015; 95% CI: 4.311–52.854) were risk factors for CDI in cirrhotic patients (Table 6).

**Table 6.** Multivariate regression to explore factors associated with CDI.

AOR, adjusted odds ratio; \*, *p* < 0.05; \*\*\*\*, *p*-value < 0.0001, statistically significant.

The correlation values in the Spearman correlation matrix ordered by hierarchical clustering and visualized by correlogram (Figure 2) identified two clusters, which will be assessed as Model 1 (age, days of admission, Charlson index, Child–Pugh score) and Model 2 (HCC, PPI, creatinine, urea). The highest positively correlated parameters seen in the correlogram were Charlson with age (r = 0.47, *p*-value < 0.05), Child–Pugh score with PPI (r = 0.55, *p*-value < 0.05), and creatinine with urea (r = 0.64, *p*-value < 0.05).

The accuracy of the two models, evaluated by AUC, as in Figure 3, suggested Model 1 (covariates age, days of submission, Charlson index, Child–Pugh score) being the best to predict CDI in cirrhotic patients with variceal bleeding. According to our results, the probability *p* of developing CDI after antibiotic prophylaxis could be assessed with the formula as in Equation (1):

$$\log(\frac{p}{1-p}) = -14.884 + 0.067 \times \text{Age} + 0.122 \times \text{DaysAdm} + 0.281 \times \text{Charkson} + 0.463 \times \text{Children} \tag{1}$$

log(

log(

*Antibiotics* **2021**, *10*, x 8 of 13

**Figure 2.** Correlogram with a hierarchical clustering of covariates included in the logistic regression analysis. Positive correlations are displayed in blue and negative correlations in red. Colour intensity and the size of the ellipse are proportional to the correlation coefficients. **Figure 2.** Correlogram with a hierarchical clustering of covariates included in the logistic regression analysis. Positive correlations are displayed in blue and negative correlations in red. Colour intensity and the size of the ellipse are proportional to the correlation coefficients. **Figure 2.** Correlogram with a hierarchical clustering of covariates included in the logistic regression analysis. Positive correlations are displayed in blue and negative correlations in red. Colour intensity and the size of the ellipse are proportional to the correlation coefficients.

ଵି) = −14.884 + 0.067 × Age + 0.122 × DaysAdm + 0.281 × Charlson + 0.463 × ChildScore (1)

ଵି) = −14.884 + 0.067 × Age + 0.122 × DaysAdm + 0.281 × Charlson + 0.463 × ChildScore (1)

**Figure 3.** The ROC curves for Model 1 and Model 2. The ROC of a perfect predictive model has sensibility (true positive rate) equal to 1 and 1-specificity (false positive rate) equal to 0. **Figure 3.** The ROC curves for Model 1 and Model 2. The ROC of a perfect predictive model has sensibility (true positive rate) equal to 1 and 1-specificity (false positive rate) equal to 0. **Figure 3.** The ROC curves for Model 1 and Model 2. The ROC of a perfect predictive model has sensibility (true positive rate) equal to 1 and 1-specificity (false positive rate) equal to 0.

#### **4. Discussion**

As the most frequent hospital-associated infection, CDI will presumably first target chronic patients, which also tend to be more susceptible to bacterial infection. However, the healthcare systems are all aware of this potential threat, as CDI should have been discussed

at a rather global scale since it has also shifted to community onset [14]. The environment stands as a high-ground factor in disease evolution, namely because of asymptomatic colonization as well as spore-resistant circumstances within medical care systems, which will eventually lead to new virulent strains [15]. Thus, first focusing on a targeted group of patients who require specific antibiotics might help restrain morbidity and health costs and enable new approaches to prevent widespread infection.

Antibiotic exposure represents the highest risk for CDI, with the most common agents represented by the second and third generation of cephalosporins [16]. Depending on the patient status, even a brief exposure may be incriminated for CDI appearance. Our study focused on a specific group of cirrhotic patients who presented with variceal bleeding, pointing out a 7.20% development of CDI after ceftriaxone administration in order to prevent bacterial translocation. In addition to antibiotic prophylaxis, patients suffering from advanced chronic liver disease are at risk of acquiring nosocomial infections due to their recurrent need for hospitalizations and the additional complications they develop over time [17]. Moreover, cirrhosis may lead to an impaired local gut immune response with a low motility level which may cause bacterial overgrowth, thus hampering the CDI exposure [18].

Variceal bleeding is a major complication of cirrhosis and portal hypertension with high mortality, ranging from 15 to 20% [19]. Available guidelines suggest the use of cephalosporines for antibiotic prophylaxis for patients admitted with variceal bleeding [10,11]. Cirrhotic patients have an increased risk for bacterial infection due to increased intestinal permeability, immune dysfunction and bacterial translocation [20]. The recommendation is to use antibiotics as soon as possible, since the bacterial infection may lead to a high mortality rate. The main choice remains ceftriaxone 1 g/24 h for up to seven days followed by norfloxacin 400 mg if patients do not have advanced liver disease [11]. Antibiotic use was proved to be efficient and to decrease early bacterial infection in cirrhotic patients [21]. However, the use of antibiotics on cirrhotic patients and the potential hospitalacquired infection is not extensively discussed. Only one study analysed the CDI relevance after antibiotic administration [22]. However, the results are rather doubtable since they used metronidazole, which is not actually indicated for variceal bleeding infection risk, but also included in the CDI management.

While all antibiotics may be incriminated for hospital-related CDI, some antimicrobial agents are known to associate a higher risk than others such as fluorochinolones, cephalosporins, carbapenems and clindamycin [23]. Ceftriaxone, a third generation cephalosporine, is commonly used for bacterial prevention in patients with variceal bleeding. However, since its elimination occurs through the biliary system, the bile concentration of antibiotic will be higher within the gut, which may cause a dysbiosis in the microbiota [24].

PPIs are frequently prescribed in patients with cirrhosis, even though their indication is limited to gastroduodenal ulcers or after band ligation. Their benefit outside these causes is controversial, and their long-term use has been linked to cirrhosis complications such as spontaneous bacterial peritonitis and hepatic encephalopathy [25]. PPI influence on CDI development has been well documented. Several meta-analyses concluded that their use may foster CDI appearance, thus promoting them as an independent risk factor [26–29]. Several theories were suggested, mainly based on the PPI's effect with acid suppression [30] and gene expression decrease, which maintain the colonocyte integrity [31] or by decreasing microbial diversity [32]. While histamine-2-receptor antagonists may be less harmful, the use of PPI along with antibiotics may enhance the risk of CDI [33]. Our study confirms that PPI might influence CDI development since patients were on a long-term therapy.

Our first objective was to highlight possible risk factors that may lead to CDI. We identified that older patients, longer admission, urea, higher Charlson index, liver cancer and PPI may influence CDI appearance. Thus, our results are similar with other studies which assessed CDI and cirrhosis. Furthermore, we tried to develop a patient profile to statistically identify a model that might help differentiate high risk patients. By hierar-

chical clustering, we identified two models that suggested what patients might be more predisposed to develop CDI. While Model 2 consisted of HCC, PPI, creatinine and urea, the best prediction model seemed to correspond to Model 1 which followed age, days of admission, Charlson index and the Child–Pugh score. Nevertheless, this might enhance awareness when antibiotics are used for cirrhotic patients with variceal bleeding and also help identify early additional complications. Hence, the medical system will also benefit, since this tool will help in early decision making and integrate it in sustainable public health programs through economic and environmental domains. The CDI risk tool will be helpful and might be embedded in the local protocols and decision-making process.

CDI is known to prolong admission, and consecutively raises the health costs [9]. This was also observed in our study, with patients with CDI having significantly higher costs than the ones without CDI. This provides an additional burden to the healthcare system increasing hospital charges. Bajaj et al. [34] reported that patients with cirrhosis and CDI have a 2.2-fold greater costs than those without CDI. A strategy was proposed by Saab et al. [35] who tried to implement a screening strategy to reduce healthcare costs. They concluded that patients with cirrhosis might require screening regarding their symptoms, and consecutively, the cost will be reduced at least four times compared with patients without CDI.

Mortality in CDI patients might be related to age, albumin levels, leukocytes count and renal failure [36]. Several studies identified risk factors associated with mortality in cirrhotic patients, which acquired CDI [37–39]. While ICU admission and albumin were recognized as predictors of short-term mortality, we identified some risk factors related to CDI severity, such as leukocytes, CRP and Atlas score, and to the liver disease—mainly Child–Pugh score and MELD. Malignancy has also been reported to increase death rate; however, our patients with HCC with associated CDI did not have a worse prognosis than other CDI patients. Hong et al. [40] obtained a hazard ratio of 1.06 ± 0.02 for the MELD score, suggesting a 21.5% increase in mortality in CDI patients and concluding that this is the only reliable short-term mortality predictor. Our analysis also recognized the MELD score as the most reliable predictor of mortality and its increase was associated with the likelihood of mortality.

A major concern for CDI exposure should now be acknowledged along with the COVID-19 pandemic. Its impact on the healthcare system should not be considered only in the short term by affecting patient's status, medical workers [41] and the fact that some diseases might be neglected but also in the long-run by the use of specific medication and their effect on patients [42]. However, at first, the CDI rate might be on a lower trend, due to increase hand hygiene and contact precautions, but most of all due to extensive cleaning and general disinfection, along with more patients which develop severe disease, as antibiotics will be used more frequently to prevent additional bacterial pulmonary infection [43,44].

Extensive measures should be considered for dedicated patients with known risk factors when antibiotics are used. Preventing a possible CDI infection might be challenging in cirrhotic patients which require antibiotics, however, if a better knowledge of hand hygiene for both patients and medical staff is instated within hospital policies, it might prevent disease spread [45,46]. Nevertheless, isolating patients, chemical disinfection agents as well as glove use on CDI patients should be considered as infection control measures to limit other cases appearance [47].

Thus, new CDI policies on awareness should be developed and identifying a patient profile might ease day-to-day practice and decrease morbidity and healthcare costs, at least in our group of patients.

Several limitations should be considered in our study. First of all, this was a singlecentre retrospective study, using a small number of CDI patients, thus, when compared to other dedicated institutions, results may differ. However, we tried to minimize potential errors by limiting to a specific set of patients and a specific antibiotic. Therapy consisted in vancomycin and vancomycin +/− metronidazole, but with no follow up in all patients

for 14 days and also with no data included regarding recurrence rate. We also did not include data about CDI strains and long-term mortality. Moreover, the recurrence rate of CDI was not taken into account since we focused on the first diagnosis, risk factors and early mortality.

#### **5. Conclusions**

CDI infection should be considered whenever ceftriaxone is used to prevent bacterial infection after variceal bleeding and identifying the patients with a higher risk will have an impact on morbidity and on the healthcare system. Our model consisting of age, days of admission, Child–Pugh score and Charlson index could predict CDI development in cirrhotic patients with variceal bleeding following ceftriaxone. Furthermore, multicentre studies should be implemented to validate our results.

**Author Contributions:** Conceptualization, M.N.V., I.R. and B.S.U.; methodology, I.R., D.I.G. and B.S.U.; software, A.T.-S.; validation, F.P., D.N.F., V.F.I., S.I. and S.M.C.; formal analysis, A.T.-S., D.I.G. and B.S.U.; data curation, D.N.F., V.F.I. and S.M.C.; writing—original draft preparation, M.N.V., A.T.-S. and B.S.U.; writing—review and editing, I.R.; supervision, B.S.U. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the University of Medicine and Pharmacy of Craiova Ethics Commission no. 88/2020.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data supporting the results of this study are available from the corresponding author on request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**