Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. Driving pressure and survival in the acute respiratory distress syndrome. Effect of driving pressure on mortality in ARDS patients during lung protective mechanical ventilation in two randomized controlled trials Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration Incidence and outcomes of acute lung injury. Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. Potentially modifiable factors contributing to outcome from acute respiratory distress syndrome: the LUNG SAFE study
BACKGROUND Traditional approaches to mechanical ventilation use tidal volumes of 10 to 15 ml per kilogram of body weight and may cause stretch-induced lung injury in patients with acute lung injury and the acute respiratory distress syndrome . We therefore conducted a trial to determine whether ventilation with lower tidal volumes would improve the clinical outcomes in these patients . METHODS Patients with acute lung injury and the acute respiratory distress syndrome were enrolled in a multicenter , r and omized trial . The trial compared traditional ventilation treatment , which involved an initial tidal volume of 12 ml per kilogram of predicted body weight and an airway pressure measured after a 0.5-second pause at the end of inspiration ( plateau pressure ) of 50 cm of water or less , with ventilation with a lower tidal volume , which involved an initial tidal volume of 6 ml per kilogram of predicted body weight and a plateau pressure of 30 cm of water or less . The primary outcomes were death before a patient was discharged home and was breathing without assistance and the number of days without ventilator use from day 1 to day 28 . RESULTS The trial was stopped after the enrollment of 861 patients because  mortality was lower in the group treated with lower tidal volumes than in the group treated with traditional tidal volumes ( 31.0 percent vs. 39.8 percent , P=0.007 ) , and the number of days without ventilator use during the first 28 days after r and omization was greater in this group ( mean [ + /-SD ] , 12+/-11 vs. 10+/-11 ; P=0.007 ) . The mean tidal volumes on days 1 to 3 were 6.2+/-0.8 and 11.8+/-0.8 ml per kilogram of predicted body weight ( P<0.001 ) , respectively , and the mean plateau pressures were 25+/-6 and 33+/-8 cm of water ( P<0.001 ) , respectively . CONCLUSIONS In patients with acute lung injury and the acute respiratory distress syndrome , mechanical ventilation with a lower tidal volume than is traditionally used results in decreased mortality and increases the number of days without ventilator use BACKGROUND Mechanical-ventilation strategies that use lower end-inspiratory ( plateau ) airway pressures , lower tidal volumes ( VT ) , and higher positive end-expiratory pressures ( PEEPs ) can improve survival in patients with the acute respiratory distress syndrome ( ARDS ) , but the relative importance of each of these components is uncertain . Because respiratory-system compliance ( CRS ) is strongly related to the volume of aerated remaining functional lung during disease ( termed functional lung size ) , we hypothesized that driving pressure ( ΔP = VT/CRS ) , in which VT is intrinsically normalized to functional lung size ( instead of predicted lung size in healthy persons ) , would be an index more strongly associated with survival than VT or PEEP in patients who are not actively breathing . METHODS Using a statistical tool known as multilevel mediation analysis to analyze individual data from 3562 patients with ARDS enrolled in nine previously reported r and omized trials , we examined ΔP as an independent variable associated with survival . In the mediation analysis , we estimated the isolated effects of changes in ΔP result ing from r and omized ventilator setting s while minimizing confounding due to the baseline severity of lung disease . RESULTS Among ventilation variables , ΔP was most strongly associated with survival . A 1-SD increment in ΔP ( approximately 7 cm of water ) was associated with increased mortality ( relative risk , 1.41 ; 95 % confidence interval [ CI ] , 1.31 to 1.51 ; P<0.001 ) , even in patients receiving " protective " plateau pressures and VT ( relative risk , 1.36 ; 95 % CI , 1.17 to 1.58 ; P<0.001 ) . Individual changes in VT or PEEP after r and omization were not independently associated with survival ; they were associated only if they were among the changes that led to reductions in ΔP ( mediation effects of ΔP , P=0.004 and P=0.001 , respectively ) . CONCLUSIONS We found that ΔP was the ventilation variable that best stratified risk . Decreases in ΔP owing to changes in ventilator setting s were strongly associated with increased survival . ( Funded by Fundação de Amparo e Pesquisa do Estado de São Paulo and others . ) Background Driving pressure ( ΔPrs ) across the respiratory system is suggested as the strongest predictor of hospital mortality in patients with acute respiratory distress syndrome ( ARDS ) . We wonder whether this result is related to the range of tidal volume ( VT ) . Therefore , we investigated ΔPrs in two trials in which strict lung-protective mechanical ventilation was applied in ARDS . Our working hypothesis was that ΔPrs is a risk factor for mortality just like compliance ( Crs ) or plateau pressure ( Pplat , rs ) of the respiratory system . Methods We performed secondary analysis of data from 787 ARDS patients enrolled in two independent r and omized controlled trials evaluating distinct adjunctive techniques while they were ventilated as in the low VT arm of the ARDSnet trial . For this study , we used  VT , positive end-expiratory pressure ( PEEP ) , Pplat , rs , Crs , ΔPrs , and respiratory rate recorded 24 hours after r and omization , and compared them between survivors and nonsurvivors at day 90 . Patients were followed for 90 days after inclusion . Cox proportional hazard modeling was used for mortality at day 90 . If colinearity between ΔPrs , Crs , and Pplat , rs was verified , specific Cox models were used for each of them . Results Both trials enrolled 805 patients of whom 787 had day-1 data available , and 533 of these survived . In the univariate analysis , ΔPrs averaged 13.7 ± 3.7 and 12.8 ± 3.7 cmH2O ( P = 0.002 ) in nonsurvivors and survivors , respectively . Colinearity between ΔPrs , Crs and Pplat , rs , which was expected as these variables are mathematically coupled , was statistically significant . Hazard ratios from the Cox models for day-90 mortality were 1.05 ( 1.02–1.08 ) ( P = 0.005 ) , 1.05 ( 1.01–1.08 ) ( P = 0.008 ) and 0.985 ( 0.972–0.985 ) ( P = 0.029 ) for ΔPrs , Pplat , rs and Crs , respectively . PEEP and VT were not associated with death in any model . Conclusions When ventilating patients with low VT , ΔPrs is a risk factor for death in ARDS patients , as is Pplat , rs or Crs . As our data originated from trials from which most ARDS patients were excluded due to strict inclusion and exclusion criteria , these findings must be vali date d in independent observational studies in patients ventilated with a lung protective strategy .Trial registration Clinical trials.gov NCT00299650 . Registered 6 March 2006 for the Acurasys trial . Clinical trials.gov NCT00527813 . Registered 10 September 2007 for the Proseva trial In medicine , numerous decisions are made by care providers , often in shared decision making , on the basis of an estimated probability that a specific disease or condition is present ( diagnostic setting ) or a specific event will occur in the future ( prognostic setting ) in an individual . In the diagnostic setting , the probability that a particular disease is present can be used , for example , to inform the referral of patients for further testing , to initiate treatment directly , or to reassure patients that a serious cause for their symptoms is unlikely . In the prognostic context , predictions can be used for planning lifestyle or therapeutic decisions on the basis of the risk for developing a particular outcome or state of health within a specific period ( 13 ) . Such estimates of risk can also be used to risk-stratify participants in therapeutic intervention trials ( 47 ) . In both the diagnostic and prognostic setting , probability estimates are commonly based on combining information from multiple predictors observed or measured from an individual ( 1 , 2 , 810 ) . Information from a single predictor is often insufficient to provide reliable estimates of diagnostic or prognostic probabilities or risks ( 8 , 11 ) . In virtually all medical domains , diagnostic and prognostic multivariable ( risk ) prediction models are being developed , vali date d , up date d , and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making . A multivariable prediction model is a mathematical equation that relates multiple predictors for a particular individual to the probability of or risk for the presence ( diagnosis ) or future occurrence ( prognosis ) of a particular outcome ( 10 , 12 ) . Other names for a prediction model include risk prediction model , predictive model , prognostic ( or prediction ) index or rule , and risk score ( 9 ) . Predictors are also referred to as covariates , risk indicators , prognostic factors , determinants , test results , ormore statisticallyindependent variables . They may range from demographic characteristics ( for example , age and sex ) , medical historytaking , and physical examination results to results from imaging , electrophysiology , blood and urine measurements , pathologic examinations , and disease stages or characteristics , or results from genomics , proteomics , transcriptomics , pharmacogenomics , metabolomics , and other new biological measurement platforms that continuously emerge . Diagnostic and Prognostic Prediction Models Multivariable prediction models fall into 2 broad categories : diagnostic and prognostic prediction models ( Box A ) . In a diagnostic model , multiplethat is , 2 or morepredictors ( often referred to as diagnostic test results ) are combined to estimate the probability that a certain condition or disease is present ( or absent ) at the moment of prediction ( Box B ) . They are developed from and to be used for individuals suspected of having that condition . Box A. Schematic representation of diagnostic and prognostic prediction modeling studies . The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present ( or absent ) within an individual , at this point in timethat is , the moment of prediction ( T= 0 ) . In prognosis , the prediction is about whether an individual will experience a specific event or outcome within a certain time period . In other words , in diagnostic prediction the interest is in principle a cross-sectional relationship , whereas prognostic prediction involves a longitudinal relationship . Nevertheless , in diagnostic modeling studies , for logistical reasons , a time window between predictor ( index test ) measurement and the reference st and ard is often necessary . Ideally , this interval should be as short as possible without starting any treatment within this period . Box B. Similarities and differences between diagnostic and prognostic prediction models . In a prognostic model , multiple predictors are combined to estimate the probability of a particular outcome or event ( for example , mortality , disease recurrence , complication , or therapy response ) occurring in a certain period in the future . This period may range from hours ( for example , predicting postoperative complications [ 13 ] ) to weeks or months ( for example , predicting 30-day mortality after cardiac surgery [ 14 ] ) or years ( for example , predicting the 5-year risk for developing type 2 diabetes [ 15 ] ) . Prognostic models are developed and are to be used in individuals at risk for developing that outcome . They may be models for either ill or healthy individuals . For example , prognostic models include models to predict recurrence , complications , or death in a certain period after being diagnosed with a particular disease . But they may also include models for predicting the occurrence of an outcome in a certain period in individuals without a specific disease : for example , models to predict the risk for developing type 2 diabetes ( 16 ) or cardiovascular events in middle-aged nondiseased individuals ( 17 ) , or the risk for preeclampsia in pregnant women ( 18 ) . We thus use prognostic in the broad sense , referring to the prediction of an outcome in the future in individuals at risk for that outcome , rather than the narrower definition of predicting the course of patients who have a particular disease with or without treatment ( 1 ) . The main difference between a diagnostic and prognostic prediction model is the concept of time . Diagnostic modeling studies are usually cross-sectional , whereas prognostic modeling studies are usually longitudinal . In this document , we refer to both diagnostic and prognostic prediction models as prediction models , highlighting issues that are specific to either type of model . Development , Validation , and Updating of Prediction Models Prediction model studies may address the development of a new prediction model ( 10 ) , a model evaluation ( often referred to as model validation ) with or without updating of the model [ 1921 ] ) , or a combination of these ( Box C and Figure 1 ) . Box C. Types of prediction model studies . Figure 1 . Types of prediction model studies covered by the TRIPOD statement . D = development data ; V = validation data . Model development studies aim to derive a prediction model by selecting predictors and combining them into a multivariable model . Logistic regression is commonly used for cross-sectional ( diagnostic ) and short-term ( for example 30-day mortality ) prognostic outcomes and Cox regression for long-term ( for example , 10-year risk ) prognostic outcomes . Studies may also focus on quantifying the incremental or added predictive value of a specific predictor ( for example , newly discovered ) ( 22 ) to a prediction model . Quantifying the predictive ability of a model on the same data from which the model was developed ( often referred to as apparent performance [ Figure 1 ] ) will tend to give an optimistic estimate of performance , owing to overfitting ( too few outcome events relative to the number of c and i date predictors ) and the use of predictor selection strategies ( 2325 ) . Studies developing new prediction models should therefore always include some form of internal validation to quantify any optimism in the predictive performance ( for example , calibration and discrimination ) of the developed model and adjust the model for overfitting . Internal validation techniques use only the original study sample and include such methods as bootstrapping or cross-validation . Internal validation is a necessary part of model development ( 2 ) . After developing a prediction model , it is strongly recommended to evaluate the performance of the model in other participant data than was used for the model development . External validation ( Box C and Figure 1 ) ( 20 , 26 ) requires that for each individual in the new participant data set , outcome predictions are made using the original model ( that is , the published model or regression formula ) and compared with the observed outcomes . External validation may use participant data collected by the same investigators , typically using the same predictor and outcome definitions and measurements , but sample d from a later period ( temporal or narrow validation ) ; by other investigators in another hospital or country ( though disappointingly rare [ 27 ] ) , sometimes using different definitions and measurements ( geographic or broad validation ) ; in similar participants , but from an intentionally different setting ( for example , a model developed in secondary care and assessed in similar participants , but selected from primary care ) ; or even in other types of participants ( for example , model developed in adults and assessed in children , or developed for predicting fatal events and assessed for predicting nonfatal events ) ( 19 , 20 , 26 , 2830 ) . In case of poor performance ( for example , systematic miscalibration ) , when evaluated in an external validation data set , the model can be up date d or adjusted ( for example , recalibrating or adding a new predictor ) on the basis of the validation data set ( Box C ) ( 2 , 20 , 21 , 31 ) . R and omly splitting a single data set into model development and model validation data sets is frequently done to develop and vali date a prediction model ; this is often , yet erroneously , believed to be a form of external validation . However , this approach is a weak and inefficient form of internal validation , because not all available data are used to develop the model ( 23 , 32 ) . If the available development data set is sufficiently large , splitting by time and developing a model using data from one period and evaluating its performance using the data from the other period ( temporal validation ) is a stronger approach . With a single data set , temporal splitting and model validation can be considered intermediate between internal and external validation . Incomplete and Inaccurate Reporting Prediction models are becoming increasingly abundant in the medical literature ( 9 , 33 , 34 ) , and policymakers are BACKGROUND Acute lung injury is a critical illness syndrome consisting of acute hypoxemic respiratory failure with bilateral pulmonary infiltrates that are not attributed to left atrial hypertension . Despite recent advances in our underst and ing of the mechanism and treatment of acute lung injury , its incidence and outcomes in the United States have been unclear . METHODS We conducted a prospect i ve , population -based , cohort study in 21 hospitals in and around King County , Washington , from April 1999 through July 2000 , using a vali date d screening protocol to identify patients who met the consensus criteria for acute lung injury . RESULTS A total of 1113 King County residents undergoing mechanical ventilation met the criteria for acute lung injury and were 15 years of age or older . On the basis of this figure , the crude incidence of acute lung injury was 78.9 per 100,000 person-years and the age-adjusted incidence was 86.2 per 100,000 person-years . The in-hospital mortality rate was 38.5 percent . The incidence of acute lung injury increased with age from 16 per 100,000 person-years for those 15 through 19 years of age to 306 per 100,000 person-years for those 75 through 84 years of age . Mortality increased with age from 24 percent for patients 15 through 19 years of age to 60 percent for patients 85 years of age or older ( P<0.001 ) . We estimate that each year in the United States there are 190,600 cases of acute lung injury , which are associated with 74,500 deaths and 3.6 million hospital days . CONCLUSIONS Acute lung injury has a substantial impact on public health , with an incidence in the United States that is considerably higher than previous reports have suggested BACKGROUND In patients with the acute respiratory distress syndrome , massive alveolar collapse and cyclic lung reopening and overdistention during mechanical ventilation may perpetuate alveolar injury . We determined whether a ventilatory strategy design ed to minimize such lung injuries could reduce not only pulmonary complications but also mortality at 28 days in patients with the acute respiratory distress syndrome . METHODS We r and omly assigned 53 patients with early acute respiratory distress syndrome ( including 28 described previously ) , all of whom were receiving identical hemodynamic and general support , to conventional or protective mechanical ventilation . Conventional ventilation was based on the strategy of maintaining the lowest positive end-expiratory pressure ( PEEP ) for acceptable oxygenation , with a tidal volume of 12 ml per kilogram of body weight and normal arterial carbon dioxide levels ( 35 to 38 mm Hg ) . Protective ventilation involved end-expiratory pressures above the lower inflection point on the static pressure-volume curve , a tidal volume of less than 6 ml per kilogram , driving pressures of less than 20 cm of water above the PEEP value , permissive hypercapnia , and preferential use of pressure-limited ventilatory modes . RESULTS After 28 days , 11 of 29 patients ( 38 percent ) in the protective-ventilation group had died , as compared with 17 of 24 ( 71 percent ) in the conventional-ventilation group ( P<0.001 ) . The rates of weaning from mechanical ventilation were 66 percent in the protective-ventilation group and 29 percent in the conventional-ventilation group ( P=0.005 ) : the rates of clinical barotrauma were 7 percent and 42 percent , respectively ( P=0.02 ) , despite the use of higher PEEP and mean airway pressures in the protective-ventilation group . The difference in survival to hospital discharge was not significant ; 13 of 29 patients ( 45 percent ) in the protective-ventilation group died in the hospital , as compared with 17 of 24 in the conventional-ventilation group ( 71 percent , P=0.37 ) . CONCLUSIONS As compared with conventional ventilation , the protective strategy was associated with improved survival at 28 days , a higher rate of weaning from mechanical ventilation , and a lower rate of barotrauma in patients with the acute respiratory distress syndrome .  Protective ventilation was not associated with a higher rate of survival to hospital discharge IMPORTANCE Limited information exists about the epidemiology , recognition , management , and outcomes of patients with the acute respiratory distress syndrome ( ARDS ) . OBJECTIVES To evaluate intensive care unit ( ICU ) incidence and outcome of ARDS and to assess clinician recognition , ventilation management , and use of adjuncts-for example prone positioning-in routine clinical practice for patients fulfilling the ARDS Berlin Definition . DESIGN , SETTING , AND PARTICIPANTS The Large Observational Study to Underst and the Global Impact of Severe Acute Respiratory Failure ( LUNG SAFE ) was an international , multicenter , prospect i ve cohort study of patients undergoing invasive or noninvasive ventilation  , conducted during 4 consecutive weeks in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across 5 continents . EXPOSURES Acute respiratory distress syndrome . MAIN OUTCOMES AND MEASURES The primary outcome was ICU incidence of ARDS . Secondary outcomes included assessment of clinician recognition of ARDS , the application of ventilatory management , the use of adjunctive interventions in routine clinical practice , and clinical outcomes from ARDS . RESULTS Of 29,144 patients admitted to participating ICUs , 3022 ( 10.4 % ) fulfilled ARDS criteria . Of these , 2377 patients developed ARDS in the first 48 hours and whose respiratory failure was managed with  invasive mechanical ventilation . The period prevalence of mild ARDS was 30.0 % ( 95 % CI , 28.2%-31.9 % ) ; of moderate ARDS , 46.6 % ( 95 % CI , 44.5%-48.6 % ) ; and of severe ARDS , 23.4 % ( 95 % CI , 21.7%-25.2 % ) . ARDS represented 0.42 cases per ICU bed over 4 weeks and represented 10.4 % ( 95 % CI , 10.0%-10.7 % ) of ICU admissions and 23.4 % of patients requiring mechanical ventilation . Clinical recognition of ARDS ranged from 51.3 % ( 95 % CI , 47.5%-55.0 % ) in mild to 78.5 % ( 95 % CI , 74.8%-81.8 % ) in severe ARDS . Less than two-thirds of patients with ARDS received a tidal volume 8 of mL/kg or less of predicted body weight . Plateau pressure was measured in 40.1 % ( 95 % CI , 38.2 - 42.1 ) , whereas 82.6 % ( 95 % CI , 81.0%-84.1 % ) received a positive end-expository pressure ( PEEP ) of less than 12 cm H2O . Prone positioning was used in 16.3 % ( 95 % CI , 13.7%-19.2 % ) of patients with severe ARDS . Clinician recognition of ARDS was associated with higher PEEP , greater use of neuromuscular blockade , and prone positioning . Hospital mortality was 34.9 % ( 95 % CI , 31.4%-38.5 % ) for those with mild , 40.3 % ( 95 % CI , 37.4%-43.3 % ) for those with moderate , and 46.1 % ( 95 % CI , 41.9%-50.4 % ) for those with severe ARDS . CONCLUSIONS AND RELEVANCE Among ICUs in 50 countries , the period prevalence of ARDS was 10.4 % of ICU admissions . This syndrome appeared to be underrecognized and undertreated and associated with a high mortality rate . These findings indicate the potential for improvement in the management of patients with ARDS . TRIAL REGISTRATION clinical trials.gov Identifier : NCT02010073 Purpose To improve the outcome of the acute respiratory distress syndrome ( ARDS ) , one needs to identify potentially modifiable factors associated with mortality . Methods The large observational study to underst and the global impact of severe acute respiratory failure ( LUNG SAFE ) was an international , multicenter , prospect i ve cohort study of patients with severe respiratory failure , conducted in the winter of 2014 in a convenience sample of 459 ICUs from 50 countries across five continents . A pre-specified secondary aim was to examine the factors associated with outcome . Analyses were restricted to patients ( 93.1 % ) fulfilling ARDS criteria on day 1–2 who received invasive mechanical ventilation .  Results 2377 patients were included in the analysis . Potentially modifiable factors associated with increased hospital mortality in multivariable analyses include lower PEEP , higher peak inspiratory , plateau , and driving pressures , and increased respiratory rate . The impact of tidal volume on outcome was unclear . Having fewer ICU beds was also associated with higher hospital mortality . Non-modifiable factors associated with worsened outcome from ARDS included older age , active neoplasm , hematologic neoplasm , and chronic liver failure . Severity of illness indices including lower pH , lower PaO2/FiO2 ratio , and higher non-pulmonary SOFA score were associated with poorer outcome . Of the 578 ( 24.3 % ) patients with a limitation of life-sustaining therapies or measures decision , 498 ( 86.0 % ) died in hospital . Factors associated with increased likelihood of limitation of life-sustaining therapies or measures decision included older age , immunosuppression , neoplasia , lower pH and increased non-pulmonary SOFA scores . Conclusions Higher PEEP , lower peak , plateau , and driving pressures , and lower respiratory rate are associated with improved survival from ARDS.Trial Registration : Clinical Trials.gov NCT02010073
Objectives : A recent post hoc analysis suggested that driving pressure may be more important than traditional ventilatory variables in determining outcome in mechanically ventilated patients with acute respiratory distress syndrome .
We conducted a systematic review and meta- analysis to summarize the risk of mortality for higher versus lower driving pressure .