ArticleBMJ open2023
Development and validation of a tool to predict high-need, high-cost patients hospitalised with ischaemic heart disease.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study.Diagnostics (Basel, Switzerland) · 2026Article
- Risk stratification and determinant identification of high-need, high-cost ICU patients using machine learning: a large-scale retrospective study from a multi-specialty ICU in a tertiary hospital.Frontiers in public health · 2026Article
- Characteristics and related factors of high-need high-cost children in Shanghai, China: a retrospective cohort study in inpatient setting.BMC pediatrics · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo develop and validate a tool to predict patients with ischaemic heart disease (IHD) at risk of excessive healthcare resource utilisation.
designA retrospective cohort study.
settingWe identified patients through the State of Florida Agency for Health Care Administration (N=586 518) inpatient dataset.
participantsAdult patients (at least 40 years of age) admitted to the hospital with a diagnosis of IHD between 1 January 2007 and 31 December 2016. PRIMARY OUTCOME MEASURES: We identified patients whose healthcare utilisation is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individual as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalisation), cost and hospital length of stay. Discrimination power, prediction accuracy and model improvement for the binary logistic model were assessed using receiver operating characteristic statistic, the Brier score and the log-likelihood (LL)-based pseudo-R
resultsThe binary logistic model had good discrimination power (c-statistic=0.6496), an accuracy of probabilistic predictions (Brier score) of 0.0621 and an LL-based pseudo-R
conclusionsThe Excel-based HNHC risk predictive tool can accurately identify at-risk patients for HNHC based on three measures of healthcare expenditures.
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