Identifying patients at risk of increased health utilization following lumbar spine surgery

IF 1.9 4区医学 Q3 CLINICAL NEUROLOGY

Journal of Clinical Neuroscience Pub Date : 2025-06-11 DOI:10.1016/j.jocn.2025.111349

Rushmin Khazanchi , Divy Kumar , Anitesh Bajaj , Robert J. Oris , Austin R. Chen , Daniel E. Herrera , Rohan M. Shah , Shravan Asthana , Samuel G. Reyes , Pranav Bajaj , Wellington K. Hsu , Alpesh A. Patel , Srikanth N. Divi

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引用次数: 0

Abstract

Background

Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed mechanisms and risk factors for healthcare utilization, no studies have developed a prognostic machine learning model to quantify and functionalize predictions.

Methods

A cohort of lumbar fusion and lumbar decompression surgeries was queried from a tertiary academic medical center from 2002 to 2022. Patient and operative characteristics were systematically extracted for each surgery. Several machine learning algorithms were employed and optimized to predict high healthcare utilizers using an aggregate 90-day index. SHAP feature importance values were computed for the top performing model.

Results

A total of 10,128 unique lumbar decompression surgeries and 2,890 unique lumbar fusion surgeries were included. The Random Forest model had the highest performance of tested models (AUROC of 0.766 for lumbar decompression, 0.727 for lumbar fusion). Both models outperformed the ASA benchmark model. The top three predictors of high health utilization in the decompression cohort included preoperative lumbar stenosis, preoperative benzodiazepine use, and preoperative neuromodulator use. The top three predictors for the fusion cohort included preoperative opioid use, preoperative neuromodulator use, and preoperative benzodiazepine use. Other Key variables spanned several domains including preoperative medication usage, patient demographics, and operative indications and characteristics.

Discussion

This study demonstrates the successful creation of a prognostic machine learning model for prediction of high healthcare utilization within 90 days of lumbar spine surgery. These models, after external validation, have the potential to be instrumental aspects of a spine surgeon’s workflow.

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确定腰椎手术后有增加健康利用风险的患者

背景：术前充分识别术后有重大医疗保健利用风险的患者有助于指导术前决策和术后患者管理。虽然有几项研究提出了医疗保健利用的机制和风险因素，但没有研究开发出预测机器学习模型来量化和功能化预测。方法对2002 ~ 2022年某三级学术医疗中心腰椎融合术和腰椎减压术进行队列调查。系统地提取每次手术的患者和手术特征。使用并优化了几种机器学习算法，以使用总90天指数预测高医疗保健利用率。计算表现最好的模型的SHAP特征重要性值。结果共纳入10128例独特腰椎减压手术和2890例独特腰椎融合手术。随机森林模型在所有模型中表现最好（腰椎减压的AUROC为0.766，腰椎融合术的AUROC为0.727）。这两个模型都优于ASA基准模型。在减压队列中，高健康利用率的前三个预测因素包括术前腰椎管狭窄、术前苯二氮卓类药物使用和术前神经调节剂使用。融合队列的前三个预测因素包括术前阿片类药物使用、术前神经调节剂使用和术前苯二氮卓类药物使用。其他关键变量跨越几个领域，包括术前用药，患者人口统计学，手术指征和特征。本研究成功创建了一个预测机器学习模型，用于预测腰椎手术后90天内的高医疗利用率。这些模型，经过外部验证，有潜力成为脊柱外科医生工作流程的工具方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Clinical Neuroscience 医学-临床神经学

CiteScore

4.50

自引率

0.00%

发文量

402

审稿时长

40 days

期刊介绍： This International journal, Journal of Clinical Neuroscience, publishes articles on clinical neurosurgery and neurology and the related neurosciences such as neuro-pathology, neuro-radiology, neuro-ophthalmology and neuro-physiology. The journal has a broad International perspective, and emphasises the advances occurring in Asia, the Pacific Rim region, Europe and North America. The Journal acts as a focus for publication of major clinical and laboratory research, as well as publishing solicited manuscripts on specific subjects from experts, case reports and other information of interest to clinicians working in the clinical neurosciences.