***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: OBJECTIVE Our purpose was to describe the incidence and effects of serious obstetric complications on maternal outcome in pregnancies complicated by HELLP syndrome . STUDY DESIGN A prospect i ve cohort study was performed on 442 pregnancies with HELLP syndrome managed at this center from August 1977 through July 1992 . RESULTS Of 437 women who had 442 pregnancies with HELLP syndrome ; 309 ( 70 % ) of the cases occurred ante partum and 133 ( 30 % ) post partum ; 149 ( 11 % ) developed at < 27 weeks and 80 ( 18 % ) at term . Maternal mortality was 1.1 % ( five patients ) . Serious maternal morbidity included disseminated intravascular coagulation ( 21 % ) , abruptio placentae ( 16 % ) , acute renal failure ( 7.7 % ) , pulmonary edema ( 6 % ) , subcapsular liver hematoma ( 0.9 % ) , and retinal detachment ( 0.9 % ) . Fifty-five percent of patients required  transfusions with blood or blood products  , and 2 % required laparotomies for major intraabdominal bleeding . Abruptio placentae was strongly correlated with the development of disseminated intravascular coagulation ( p < 0.0001 ) , acute renal failure ( p < 0.001 ) , and pulmonary edema ( p < 0.01 ) . Moreover , there was a strong association between pulmonary edema and acute renal failure ( p < 0.0001 ) . There were no differences in laboratory findings between HELLP syndrome before and after delivery ; however , women with postpartum HELLP syndrome had significantly higher incidences of pulmonary edema and renal failure . CONCLUSION HELLP syndrome is associated with serious maternal morbidity , especially when it arises in the postpartum period Background Unexpected clinical deterioration before 34 weeks gestation is an undesired course in early-onset pre-eclampsia . To safely prolong preterm gestation , accurate and timely prediction of complications is required . Method Women with confirmed early onset pre-eclampsia were recruited from 53 maternity units in the UK to a large prospect i ve cohort study ( PREP-946 ) for development of prognostic models for the overall risk of experiencing a complication using logistic regression ( PREP-L ) , and for predicting the time to adverse maternal outcome using a survival model ( PREP-S ) . External validation of the models were carried out in a multinational cohort ( PIERS-634 ) and another cohort from the Netherl and s ( PETRA-216 ) . Main outcome measures were C-statistics to summarise discrimination of the models and calibration plots and calibration slopes . Results A total of 169 mothers ( 18 % ) in the PREP data set had adverse outcomes by 48 hours , and 633 ( 67 % ) by discharge . The C-statistics of the models for predicting complications by 48 hours and by discharge were 0.84 ( 95 % CI , 0.81–0.87 ; PREP-S ) and 0.82 ( 0.80–0.84 ; PREP-L ) , respectively . The PREP-S model included maternal age , gestation , medical history , systolic blood pressure , deep tendon reflexes , urine protein creatinine ratio , platelets , serum alanine amino transaminase , urea , creatinine , oxygen saturation and treatment with antihypertensives or magnesium sulfate . The PREP-L model included the above except deep tendon reflexes , serum alanine amino transaminase and creatinine . On validation in the external PIERS data set , the reduced PREP-S model showed reasonable calibration ( slope 0.80 ) and discrimination ( C-statistic 0.75 ) for predicting adverse outcome by 48 hours . Reduced PREP-L model showed excellent calibration ( slope : 0.93 PIERS , 0.90 PETRA ) and discrimination ( 0.81 PIERS , 0.75 PETRA ) for predicting risk by discharge in the two external data sets . Conclusions PREP models can be used to obtain predictions of adverse maternal outcome risk , including early preterm delivery , by 48 hours ( PREP-S ) and by discharge ( PREP-L ) , in women with early onset pre-eclampsia in the context of current care . They have a potential role in triaging high-risk mothers who may need transfer to tertiary units for intensive maternal and neonatal care . Trial registration IS RCT N40384046 , retrospectively registered The analytical effect of the number of events per variable ( EPV ) in a proportional hazards regression analysis was evaluated using Monte Carlo simulation techniques for data from a r and omized trial containing 673 patients and 252 deaths , in which seven predictor variables had an original significance level of p < 0.10 . The 252 deaths and 7 variables correspond to 36 events per variable analyzed in the full data set . Five hundred simulated analyses were conducted for these seven variables at EPVs of 2 , 5 , 10 , 15 , 20 , and 25 . For each simulation , a r and om exponential survival time was generated for each of the 673 patients , and the simulated results were compared with their original counterparts . As EPV decreased , the regression coefficients became more biased relative to the true value ; the 90 % confidence limits about the simulated values did not have a coverage of 90 % for the original value ; large sample properties did not hold for variance estimates from the proportional hazards model , and the Z statistics used to test the significance of the regression coefficients lost validity under the null hypothesis . Although a single boundary level for avoiding problems is not easy to choose , the value of EPV = 10 seems most prudent . Below this value for EPV , the results of proportional hazards regression analyses should be interpreted with caution because the statistical model may not be valid 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 
    Output:
    
The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals .
The PREP-S model has the potential to be used as a triage tool for risk assessment .