Mert Karabacak, Alexander Schupper, Matthew Carr, Konstantinos Margetis
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Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes.</p><p><strong>Results: </strong>The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome.</p><p><strong>Conclusions: </strong>This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.</p>","PeriodicalId":8555,"journal":{"name":"Asian Spine Journal","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11366553/pdf/","citationCount":"0","resultStr":"{\"title\":\"A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.\",\"authors\":\"Mert Karabacak, Alexander Schupper, Matthew Carr, Konstantinos Margetis\",\"doi\":\"10.31616/asj.2024.0048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC).</p><p><strong>Purpose: </strong>To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose.</p><p><strong>Overview of literature: </strong>Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications.</p><p><strong>Methods: </strong>The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes.</p><p><strong>Results: </strong>The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. 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引用次数: 0
摘要
研究设计:目的:评估机器学习(ML)在预测颈椎前路椎体切除术(ACC)预后建模方面的有效性,并为此开发一种便于使用的用户友好型工具:根据我们的文献综述,还没有研究考察过 ML 算法预测 ACC 主要短期预后的能力,如住院时间(LOS)延长、非居家出院和主要并发症:方法: 使用美国外科学院的国家外科质量改进计划数据库来识别接受 ACC 的患者。延长的住院时间、非家庭出院和主要并发症被评估为相关结果。使用 TabPFN 算法开发了 ML 模型,并将其集成到一个开放访问的网站中,以预测这些结果:结果:预测住院时间延长、非居家出院和主要并发症的模型的接收者操作特征曲线下的平均面积(AUROC)分别为 0.802、0.816 和 0.702。这些结果凸显了这些模型的鉴别能力:在区分有重大并发症和无重大并发症的患者方面尚可(AUROC >0.7),在区分有无延长 LOS 和非家庭出院的患者方面良好(AUROC >0.8)。根据 SHapley Additive Explanations 分析,单层次手术与多层次手术、年龄、体重指数、术前血细胞比容和美国麻醉医师协会身体状况重复出现,是对每种结果最重要的变量:本研究通过采用先进的多变量模型技术,大大提高了对 ACC 手术术后结果的预测能力。其主要贡献在于创建了一个可访问的网络应用程序,突出了所开发模型的实用价值。我们的研究结果表明,ML 可以作为一种宝贵的辅助工具,对患者进行手术风险分层,并能预测各种术后不良后果。
A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.
Study design: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC).
Purpose: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose.
Overview of literature: Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications.
Methods: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes.
Results: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome.
Conclusions: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.