Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance.
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.



To synthesize the available literature on readmission risk prediction models for adults who are hospitalized because of pneumonia and describe their performance.



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.



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.



We found a limited number of validated pneumonia-specific 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.
1 Department of Internal Medicine.
Mark
M
Weinreich
1 Department of Internal Medicine.|2 Department of Clinical Sciences, and.
Oanh K
OK
Nguyen
1 Department of Internal Medicine.
David
D
Wang
3 Health Sciences Digital Library and Learning Center, University of Texas Southwestern Medical Center, Dallas, Texas; and.
Helen
H
Mayo
1 Department of Internal Medicine.|2 Department of Clinical Sciences, and.|4 Veterans Affairs North Texas Health Care System, Dallas, Texas.
Eric M
EM
Mortensen
1 Department of Internal Medicine.|2 Department of Clinical Sciences, and.
Ethan A
EA
Halm
1 Department of Internal Medicine.|2 Department of Clinical Sciences, and.
Anil N
0000-0001-7072-9946
AN
Makam
D014487:Centers for Medicare and Medicaid Services, U.S.; D006801:Humans; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data* / Q000639:trends; D011014:Pneumonia / Q000628:therapy*; D011379:Prognosis; D018570:Risk Assessment; D013997:Time Factors; D014481:United States
model; patient readmission; pneumonia; prediction; risk