Leveraging electronic health records for predictive modeling of post-surgical complications

Statistical Methods in Medical Research
Grant B WellerMarianne Huebner

Abstract

Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health record data. However, these data provide opportunities to use probabilistic methods for early prediction of adverse health events, which may be more informative than deterministic algorithms. Electronic health record data from a set of 9598 colorectal surgery cases from 2010 to 2014 were used to predict the occurrence of selected complications including surgical site infection, ileus, and bleeding. Consistent with previous studies, we find a high rate of missing values for both covariates and complication information (4-90%). Several machine learning classification methods are trained on an 80% random sample of cases and tested on a remaining holdout set. Predictive performance varies by complication, although an area under the receiver operating characteristic curve as high as 0.86 on testing data was achieved for bleeding complications, and accuracy for all complications compares favorably to existing clinical decision rules. Our resul...Continue Reading

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Citations

Mar 14, 2020·The Journal of Craniofacial Surgery·Martin L MakJohn Phillips
Jul 7, 2020·Colorectal Disease : the Official Journal of the Association of Coloproctology of Great Britain and Ireland·UNKNOWN PelvEx Collaborative
Oct 23, 2019·American Journal of Surgery·Michael BronsertKathryn L Colborn
Oct 31, 2021·Langenbeck's Archives of Surgery·Jonas HennHanno Matthaei
Nov 1, 2021·Methods of Information in Medicine·Asa AdadeyLorraine B Possanza

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Software Mentioned

R package
randomForest
LIBSVM
grpreg
R

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