RT Journal
A1 Siontis GM, Tzoulaki I, Ioannidis JA
T1 Predicting death: An empirical evaluation of predictive tools for mortality
JF Archives of Internal Medicine
JO Archives of Internal Medicine
YR 2011
FD October 24
VO 171
IS 19
SP 1721
OP 1726
DO 10.1001/archinternmed.2011.334
UL http://dx.doi.org/10.1001/archinternmed.2011.334
AB Background 
                  The ability to predict death is crucial in medicine, and many relevant prognostic tools have been developed for application in diverse settings. We aimed to evaluate the discriminating performance of predictive tools for death and the variability in this performance across different clinical conditions and studies.Methods 
                  We used Medline to identify studies published in 2009 that assessed the accuracy (based on the area under the receiver operating characteristic curve [AUC]) of validated tools for predicting all-cause mortality. For tools where accuracy was reported in 4 or more assessments, we calculated summary accuracy measures. Characteristics of studies of the predictive tools were evaluated to determine if they were associated with the reported accuracy of the tool.Results 
                  A total of 94 eligible studies provided data on 240 assessments of 118 predictive tools. The AUC ranged from 0.43 to 0.98 (median [interquartile range], 0.77 [0.71-0.83]), with only 23 of the assessments reporting excellent discrimination (10%) (AUC, >0.90). For 10 tools, accuracy was reported in 4 or more assessments; only 1 tool had a summary AUC exceeding 0.80. Established tools showed large heterogeneity in their performance across different cohorts (I2 range, 68%-95%). Reported AUC was higher for tools published in journals with lower impact factor (P = .01), with larger sample size (P = .01), and for those that aimed to predict mortality among the highest-risk patients (P = .002) and among children (P < .001).Conclusions 
                  Most tools designed to predict mortality have only modest accuracy, and there is large variability across various diseases and populations. Most proposed tools do not have documented clinical utility.