Predicting patients at risk for 3-day postdischarge readmissions, ED Visits, and Deaths

Deepak Agrawal, Cheng Bang Chen, Ronald W. Dravenstott, Christopher T.B. Strömblad, John Andrew Schmid, Jonathan D. Darer, Priyantha Devapriya, Soundar Kumara

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Background: Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs. Objectives: To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge. Research Design: Retrospective cohort study of admissions between June 2012 and June 2014. Subjects: All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system. Measures: All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. Results: The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively. Conclusions: The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs.

Original languageEnglish (US)
Pages (from-to)1017-1023
Number of pages7
JournalMedical care
Issue number11
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health


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