Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review

Abstract

ObjectiveTo update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.DesignSystematic review.Setting/data sourceCINAHL, Embase, MEDLINE from 2011 to 2015.ParticipantsAll studies of 28-day and 30-day readmission predictive model.Outcome measuresCharacteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.ResultsOf 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21–0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables ‘comorbidities’, ‘length of stay’ and ‘previous admissions’ were frequently cited across 73 models. The variables ‘laboratory tests’ and ‘medication’ had more weight in the models for cardiovascular disease and medical condition-related readmissions.ConclusionsThe predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.

Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk

“…Recent years have seen an explosion in these predictive models, which use patterns observed within large data sets to generate readmission risks for individual patients. In 2011, a systematic review found 26 models for readmissions,3 but an updated review that examined papers published up to 2015 found 68 more 4…”

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

“…[32] Zhou et al reviewed unplanned readmission risk models in detail in 2016. [51] The review emphasized classification of predictors and performance attributes of the 73 published models across 60 studies, and discrimination (AUROC) and calibration (Hosmer Lemeshow – only if reported) were compared. The state of the literature leaves a significant gap for more frequent inclusion of usefulness analyses as the decision-analytic framework prior to changing clinical workflow requires them.…”

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study (Preprint)

“…[8], [19] However, clinical adoption of risk prediction models remains quite low. [20], [21] Some of the barriers reported by physicians for not using risk prediction models are lack of time, lack of trust in its validity, and uncertainty about generalizability to the specific patient population seen by an individual physician.…”

“…[7] Hospital readmission risk prediction models have been traditionally developed using hypothesis-driven statistical methods since 1980s; and as of 2015, at least 94 unique models had been described in the published literature. [8], [9] Although these risk prediction models are helpful decision making tools, their utility is limited by considerations of generalizability, adaptability and absolute predictive performance. First, most of these models have been developed using high quality data from select patient cohorts, and therefore can have inconsistent external validity in other settings and patient populations, in the setting of missing data and over time.…”