Abstract
BackgroundThe identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission.MethodsA combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia.ResultsThe scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year.ConclusionsThis study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.
Highlights
The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital
The models achieved a fair performance with Area under the receiver operating characteristic curve (AUC) for the gradient tree boosting models of 0.71, 0.74 and 0.76
Additional variables not currently contained in Electronic Health Record (EHR) data may be needed to improve performance
Summary
The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. Preventable factors under the control of the hospital include management errors, surgical complications, medication related errors, and poor discharge procedures that do not properly involve patients, their relatives, general practitioners or aged-care workers [15, 18, 19]. It makes sense to target those readmissions that hospitals are best able to prevent and to tailor the costliest interventions to patients most likely to benefit from them. This strategy requires methods to accurately, and in a timely manner, estimate risk
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