AI/ML

Identification of postoperative complications using electronic health record data and machine learning.




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Identification of postoperative complications using electronic health record data and machine learning.

Am J Surg. 2020 07;220(1):114-119

Authors: Bronsert M, Singh AB, Henderson WG, Hammermeister K, Meguid RA, Colborn KL

Abstract
BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR).
METHODS: We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors.
RESULTS: Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93.
CONCLUSIONS: Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution.

PMID: 31635792 [PubMed - indexed for MEDLINE]

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