Original Research

Real‐time automated clinical deterioration alerts predict thirty‐day hospital readmission

Abstract

INTRODUCTION

Clinical deterioration alerts (CDAs) are increasingly employed to identify deteriorating patients.

METHODS

We performed a retrospective study to determine whether CDAs predict 30‐day readmission. Patients admitted to 8 general medicine units were assessed for all‐cause 30‐day readmission.

RESULTS

Among 3015 patients, 567 (18.8%) were readmitted within 30 days. Patients triggering a CDA (n = 1141; 34.4%) were more likely to have a 30‐day readmission (23.6% vs 15.9%; P < 0.001). Logistic regression identified triggering of a CDA to be independently associated with 30‐day readmission (odds ratio [OR]: 1.40; 95% confidence interval [CI]: 1.26‐1.55; P = 0.001). Other predictors were: an emergency department visit in the previous 6 months (OR: 1.23; 95% CI:, 1.20‐1.26; P < 0.001), increasing age (OR: 1.01; 95% CI: 1.01‐1.02; P = 0.003), presence of connective tissue disease (OR: 1.63; 95% CI: 1.34‐1.98; P = 0.012), diabetes mellitus with end‐organ complications (OR: 1.23; 95% CI: 1.13‐1.33; P = 0.010), chronic renal disease (OR: 1.16; 95% CI: 1.08‐1.24; P = 0.034), cirrhosis (OR: 1.25; 95% CI: 1.17‐1.33; P < 0.001), and metastatic cancer (OR: 1.12; 95% CI: 1.08‐1.17; P = 0.002). Addition of the CDA to the other predictors added only modest incremental value for the prediction of hospital readmission.

CONCLUSIONS

Readily identifiable clinical variables can be identified that predict 30‐day readmission. It may be important to include these variables in existing prediction tools if pay for performance and across‐institution comparisons are to be “fair” to institutions that care for more seriously ill patients. Journal of Hospital Medicine 2016;11:768–772. © 2016 Society of Hospital Medicine

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