Clinical Analytics Prediction Engine

• Published in: PloS one Vol. 15; no. 8; p. e0238065
• Main Authors: Shah, Nirav, Konchak, Chad, Chertok, Daniel, Au, Loretta, Kozlov, Alex, Ravichandran, Urmila, McNulty, Patrick, Liao, Linning, Steele, Kate, Kharasch, Maureen, Boyle, Chris, Hensing, Tom, Lovinger, David, Birnberg, Jonathan, Solomonides, Anthony, Halasyamani, Lakshmi
• Format: Journal Article
• Language: English
• Published: Public Library of Science 27.08.2020
• Subjects: Electronic records; Hospital information systems; Hospitalization; Medical records; Mortality; United States; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0238065

Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.