Predicting outcome in XXXX : external validation of predictive risk models.

@entity1 @entity64 @entity16 @entity1188 @entity385
BACKGROUND: Six multivariable models predicting 3-month outcome of acute @entity385 have been developed and internally validated previously. The purpose of this study was to externally validate the previous models in an independent data set. SUMMARY OF REPORT: We predicted outcomes for 299 @entity1 with @entity385 who received placebo in the National Institute of @entity16 and @entity64 rt-PA trial. The model equations used 6 acute clinical variables and head CT @entity1188 volume at 1 week as independent variables and 3-month National Institutes of Health @entity64 Scale, Barthel Index, and Glasgow Outcome Scale as dependent variables. Previously developed model equations were used to forecast excellent and devastating outcome for subjects in the placebo tissue plasminogen activator data set. Area under the receiver operator characteristic curve was used to measure discrimination, and calibration charts were used to measure calibration. The validation data set @entity1 were more severely ill (National Institutes of Health @entity64 Scale and @entity1188 volume) than the model development subjects. Area under the receiver operator characteristic curves demonstrated remarkably little degradation in the validation data set and ranged from 0.75 to 0.89. Calibration curves showed fair to good calibration. CONCLUSIONS: Our models have demonstrated excellent discrimination and acceptable calibration in an external data set. Development and validation of improved models using variables that are all available acutely are necessary.
