ArticleGlobal spine journal2024
Utilization of Machine Learning to Model Important Features of 30-day Readmissions following Surgery for Metastatic Spinal Column Tumors: The Influence of Frailty.
Article in Global spine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Predictive value of frailty indices for postoperative outcomes of metastatic spine tumor: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2026Pooled it
- A systematic review on frailty indices utilized in oncologic surgery and radiotherapy for metastatic spine disease: A critical appraisal of components and performance.North American Spine Society journal · 2025Review
- Risk factors for and a prediction nomogram of physical frailty in older patients hospitalized with acute calculous cholecystitis.Global health & medicine · 2025Article
- Frailty in older adults: A systematic review of risk factors and early intervention pathways.Intractable & rare diseases research · 2025Review
- Application of machine learning algorithms to predict 30-day hospital readmission following cement augmentation for osteoporotic vertebral compression fractures.World neurosurgery: X · 2024Article
- Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.Brain sciences · 2023Review
- Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
Abstract
STUDY
designRetrospective cohort study.
objectiveThe aim of this study was to determine the relative importance and predicative power of the Hospital Frailty Risk Score (HFRS) on unplanned 30-day readmission after surgical intervention for metastatic spinal column tumors.
methodsAll adult patients undergoing surgery for metastatic spinal column tumor were identified in the Nationwide Readmission Database from the years 2016 to 2018. Patients were categorized into 3 cohorts based on the criteria of the HFRS: Low(<5), Intermediate(5-14.9), and High(≥ 15). Random Forest (RF) classification was used to construct predictive models for 30-day patient readmission. Model performance was examined using the area under the receiver operating curve (AUC), and the Mean Decrease Gini (MDG) metric was used to quantify and rank features by relative importance.
resultsThere were 4346 patients included. The proportion of patients who required any readmission were higher among the Intermediate and High frailty cohorts when compared to the Low frailty cohort (
conclusionOur study utilizes machine learning approaches and predictive modeling to identify frailty as a significant risk-factor that contributes to unplanned 30-day readmission after spine surgery for metastatic spinal column metastases.
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Registered trials
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