Risk Prediction Models for Hospital Readmission

Abstract

Context Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-standardize readmission rates for purposes of hospital comparison. Objective To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. Data Sources MEDLINE, CINAHL, and Cochrane Library through March 2011, EMBASE through August 2011, and hand search of reference lists. Study Selection Dual review to identify English language studies of prediction models tested with medical patients, with both derivation and validation cohorts. Data Extraction Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Results Of 7,843 citations reviewed, 30 studies of 26 unique models met criteria. The most common outcome used was 30-day readmission; only one model specifically addressed preventable readmissions. Fourteen models relying on retrospective administrative data could be potentially used for standardization of readmission risk and hospital comparisons; of these, nine were tested in large US populations and had poor discriminative ability (c-statistics 0.55 – 0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c-statistics 0.56 – 0.72), and five could be used at hospital discharge (c-statistics 0.68 – 0.83). Six studies compared different models in the same population and two of these found that functional and social variables improved model discrimination. Though most models incorporated medical comorbidity and prior utilization variables, few examined variables associated with overall health and function, illness severity, or social determinants of health. Conclusions Most current readmission risk prediction models, whether designed for comparative or clinical purposes, perform poorly. Though in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.

Incidence and cost analysis of hospital admission and 30‐day readmission among patients with cirrhosis

“…Inability to accurately predict readmissions is consistent with a systematic review of risk prediction models for hospital readmission in general 14. Quality of care, patient satisfaction, coordination of care with the provider, postdischarge follow‐up, and individual patient factors, such as demographic characteristics, patient capacity for self‐care, cultural norms, and socioeconomic and health insurance status, are all associated with readmission risk but are not easily measurable.…”

Risk factors for hospital readmission of elderly patients

“…In other studies readmission was mostly related to age, comorbidities and the type of chronic illnesses [5,6,8,9], the role of age being explained by the fact that older people have more chronic diseases and a lower mean functional status [5].…”

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study

“…We had hypothesized that disability limitations measured as ADL limitations would be significant predictors of readmission for all the cohorts, supported by literature data. [10][11][12][13][14][15][16][17][18][19][39][40][41][42] Although ADL limitations were significant predictors of readmission for pneumonia using the HRS-CMS and ACS-HCUP data sets, this was not demonstrated for the heart failure or acute myocardial infarction cohorts. Cognitive impairment did not predict readmission in any cohort for either data set, perhaps because the diagnosis of dementia is part of the standard CMS risk adjustment already applied.…”

“…7 However, an increasing body of literature reveals that patient disability and social determinants of health impact readmission risk and vary across hospital populations, contributing to higher readmission penalties for safety-net hospitals-and generating increasing interest in studying risk adjustment for sociodemographic factors. 1,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] To provide a better understanding of the extent to which the addition of patient disability and social determinants of health would impact the current risk adjustment models used by CMS, the objective of our study was to assess how measures of disability and social determinants of health were associated Electronic supplementary material The online version of this article (doi:10.1007/s11606-016-3869-x) contains supplementary material, which is available to authorized users.…”