human
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 quant 
    Output:
    
agent
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 quant 
    Output:
    