Predicting the risk of readmission in pneumonia a systematic review of model performance

Mark Weinreich, Oanh K. Nguyen, David Wang, Helen Mayo, Eric M. Mortensen, Ethan A. Halm, Anil N. Makam

Research output: Contribution to journalReview articlepeer-review

32 Scopus citations


Rationale: Predicting which patients are at highest risk for readmission after hospitalization for pneumonia could enable hospitals to proactively reallocate scarce resources to reduce 30-day readmissions. Objectives: To synthesize the available literature on readmission risk prediction models for adults who are hospitalized because of pneumonia and describe their performance. Methods: We systematically searched Ovid MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases from inception through July 2015. We included studies of adults discharged with pneumonia that developed or validated a model that predicted hospital readmission. Two independent reviewers abstracted data and assessed the risk of bias. Measurements and Main Results: Of 992 citations reviewed, 7 studies met inclusion criteria, which included 11 unique risk prediction models. All-cause 30-day readmission rates ranged from 11.8 to 20.8% (median, 17.3%). Model discrimination (C statistic) ranged from 0.59 to 0.77 (median, 0.63) with the highest-quality, best-validated model, the Centers for Medicare and Medicaid Services Pneumonia Administrative Model performing modestly (C Statistic of 0.63 in 4 separate multicenter cohorts). The best performing model (C statistic of 0.77) was a single-site study that lacked internal validation. The models had adequate calibration, with patients predicted as high risk for readmission having a higher average observed readmission rate than those predicted to be low risk. None of the studies included pneumonia illness severity scores, and only one included measures of in-hospital clinical trajectory and stability on discharge, robust predictors of readmission. Conclusions:Wefound a limited number of validated pneumoniaspecific readmission models, and their predictive ability was modest. To improve predictive accuracy, future models should include measures of pneumonia illness severity, hospital complications, and stability on discharge.

Original languageEnglish (US)
Pages (from-to)1607-1614
Number of pages8
JournalAnnals of the American Thoracic Society
Issue number9
StatePublished - Sep 2016


  • Model
  • Patient readmission
  • Pneumonia
  • Prediction
  • Risk

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine


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