Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.
Background and purpose — Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to...
| Publicado en: | Acta Orthopaedica Vol. 96; pp. 521 - 529 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191461400&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191461400 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17453674 1BVL jtl: Acta Orthopaedica issn: 17453674 maglogo: N pubinfo: dt: 2025 vid: 96 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 191461400 191461400 191461400 10.2340/17453674.2025.44248 191461400 ppf: 521 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases. aug: au: DIJKSTRA, Hidde PARSONS, Cathleen S. VAN BREMEN 4-, Hanne-Eva WILLEMS, Hanna C. DE HOND, Anne A. H. VAN MUNSTER, Barbara C. DOORNBERG, Job N. OOSTERHOFF, Jacobien H. F. affil: Department of Orthopaedic Surgery, University Medical Centre Groningen, University of Groningen. sug: subj: Machine Learning Algorithms Prediction Algorithms Prediction Models Mortality Risk Factors Decision Making, Shared Hip Fractures Mortality Risk Assessment Decision Making, Computer Assisted Hip Fractures Surgery Fracture Fixation Frail Elderly Human Male Female Aged Aged, 80 and Over Audit Retrospective Design Hip Fractures Prognosis Netherlands Sensitivity and Specificity Calibration Malnutrition Dementia Patients Logistic Regression Prospective Studies Functional Status Boosting Machine Learning Algorithms Random Forest Support Vector Machine Clinical Assessment Tools Geriatric Functional Assessment Physical Mobility Data Analysis Software Descriptive Statistics Confidence Intervals Correlation Coefficient Funding Source Aged: 65+ years Aged, 80 & over Male Female ab: Background and purpose — Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short- and long-term mortality in a large cohort of patients with hip fractures. Methods — In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018–2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, including logistic regression, were trained with internal cross-validation and evaluated on discrimination (c-statistic), sensitivity, specificity, calibration, and interpretability. Results — 74,396 patients (median age 84, IQR 78–89; 68% female) were analyzed. Most patients lived at home (69%) and high malnutrition risk was seen in 10%. 18% had dementia. Mortality rates were 9.1% (30-day), 15% (90- day), and 26% (1-year). Logistic regression performed comparably to other algorithms, but was chosen as the preferred algorithm due to its superior interpretability (c-statistic: 30-day 0.82, 90-day 0.81, 1-year 0.80). Conclusion — We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. This information may inform SDM. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|