Development and Validation of an Electronic Postoperative Morbidity Score.
Stubbs, Daniel J
Bowen, Jessica L
Furness, Rachel C
Gilder, Fay J
Menon, David K
Ovid Technologies (Wolters Kluwer Health)
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Stubbs, D. J., Bowen, J. L., Furness, R. C., Gilder, F. J., Romero-Ortuno, R., Biram, R., Menon, D. K., & et al. (2019). Development and Validation of an Electronic Postoperative Morbidity Score.. Anesth Analg, 129 (4), 935-942. https://doi.org/10.1213/ANE.0000000000003953
BACKGROUND: Electronic health records are being adopted due to numerous potential benefits. This requires the development of objective metrics to characterize morbidity, comparable to studies performed in centers without an electronic health record. We outline the development of an electronic version of the postoperative morbidity score for integration into our electronic health record. METHODS: Twohundred and three frail patients who underwent elective surgery were reviewed. We retrospectively defined postoperative morbidity score on postoperative day 3. We also recorded potential electronic surrogates for morbidities that could not be easily extracted in an objective format. We compared discriminative capability (area under the receiver operator curve) for patients having prolonged length of stay or complex discharge requirements. RESULTS: One hundred thirty-nine patients (68%) had morbidity in ≥1 postoperative morbidity score domain. Initial electronic surrogates were overly sensitive, identifying 173 patients (84%) as having morbidity. We refined our definitions using backward logistic regression against "gold-standard" postoperative morbidity score. The final electronic postoperative morbidity score differed from the initial version in its definition of cardiac and neurological morbidity. There was no significant difference in the discriminative capability between electronic postoperative morbidity score and postoperative morbidity score for either outcome (area under the receiver operator curve: 0.66 vs 0.66 for complex discharge requirement, area under the receiver operator curve: 0.66 vs 0.67 for a prolonged length of stay; P> .05 for both). Patients with postoperative morbidity score or electronic postoperative morbidity score-defined morbidity on day 3 had increased risk of prolonged length of stay (P < .001 for both). CONCLUSIONS: We present a variant of postoperative morbidity score based on objective electronic metrics. Discriminative performance appeared comparable to gold-standard definitions for discharge outcomes. Electronic postoperative morbidity score may allow characterization of morbidity within our electronic health record, but further study is required to assess external validity.
Humans, Postoperative Complications, Treatment Outcome, Length of Stay, Risk Assessment, Risk Factors, Retrospective Studies, Reproducibility of Results, Predictive Value of Tests, Age Factors, Decision Support Techniques, Time Factors, Aged, Aged, 80 and over, Frail Elderly, Female, Male, Electronic Health Records, Elective Surgical Procedures, Frailty
Wellcome Trust (204017/Z/16/Z)
External DOI: https://doi.org/10.1213/ANE.0000000000003953
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286687