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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Medical Informatics and Decision Making 1/2018

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

BMC Medical Informatics and Decision Making > Ausgabe 1/2018
Yashar Maali, Oscar Perez-Concha, Enrico Coiera, David Roffe, Richard O. Day, Blanca Gallego
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Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12911-017-0580-8) contains supplementary material, which is available to authorized users.



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 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.


A 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.


The 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.


This 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.
Additional file 1: Table S1. Parameters of the Gradient Tree Boosting algorithm. In this study, we used the freely available gradient tree boosting algorithm implemented in the R package XGBoost with the following parameters chosen via manual tuning. Table S2. Conversion of continuous variables into categorical variables: cutting points for hospital length of stay (LOS), age (years), cumulative LOS (hours) in the previous year, days from last admission, number of pathology tests, number of pathology panels, hours since last surgery, hours since last panel and admission type. Table S3: Characteristics of patients and their hospital admissions for the study population. Main descriptive statistics. Table S4. Main categories of primary diagnosis (ICD10-AM) in our cohort. Table S5. Comorbidity groups in our cohort (Reference value = no comorbidity). Table S6. Pathology variables identified by the hospital laboratory in our cohort (Reference value = missing). Table S7. Calibration performance; Observed and expected rates for selected scores can be found in this table. Table S8. Sensitivity, specificity and PPV for different cut-off scores. (PDF 155 kb)
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