Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile
Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile
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DOI：
10.1007/s00586-021-06866-5
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发表时间：
2021-06-07
影响因子：
2.8
通讯作者：
An, Howard S.
中科院分区：
文献类型：
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作者：
Harada, Garrett K.;Siyaji, Zakariah K.;An, Howard S.
目的腰椎间盘突出症的外科治疗在世界范围内是一种常见的手术方式。然而，复发的突出髓核(Re-HNP)可能会发展，使预后和患者管理复杂化。这项研究的目的是利用机器学习(ML)分析来预测腰椎再发HNP，从而开发个性化的风险预测作为临床工具。方法采用单中心回顾性研究方法，对2630例接受腰椎小间盘摘除手术的患者进行研究，平均随访22个月。记录了各种术前患者的疼痛/残疾/功能特征、成像参数和拟人化/人口学指标。应用极端梯度增强(XGBoost)分类器来开发预测模型，以识别有再次发生HNP风险的患者。该模型被导出到用于临床实用的Web应用软件。结果男性1608例，女性1022例，其中114例发生Re-HNP。原发于中央型(65.8%)、旁中心型(17.6%)、远侧型(17.1%)。XGBoost算法识别了多个Re-HNP预测因素，并被整合到一个开放访问的网络应用软件中，识别出Re-HNP的低风险或高风险患者。术前VAS小腿、残疾、对齐参数、体重指数升高、症状持续时间和年龄是最强的预测因子。结论：据我们所知，我们通过大规模队列的ML方法建立的预测模型是第一个确定腰椎减压术后发生Re-HNP的重要危险因素的研究。我们开发了减压后再突出(RAD)概况指数，该指数已被转化为在线筛查工具，以识别再次发生HNP的低风险和高风险患者。潜在的全球实施还需要额外的验证。
Purpose Surgical treatment of herniated lumbar intervertebral disks is a common procedure worldwide. However, recurrent herniated nucleus pulposus (re-HNP) may develop, complicating outcomes and patient management. The purpose of this study was to utilize machine-learning (ML) analytics to predict lumbar re-HNP, whereby a personalized risk prediction can be developed as a clinical tool. Methods A retrospective, single center study was conducted of 2630 consecutive patients that underwent lumbar microdiscectomy (mean follow-up: 22-months). Various preoperative patient pain/disability/functional profiles, imaging parameters, and anthropomorphic/demographic metrics were noted. An Extreme Gradient Boost (XGBoost) classifier was implemented to develop a predictive model identifying patients at risk for re-HNP. The model was exported to a web application software for clinical utility. Results There were 1608 males and 1022 females, 114 of whom experienced re-HNP. Primary herniations were central (65.8%), paracentral (17.6%), and far lateral (17.1%). The XGBoost algorithm identified multiple re-HNP predictors and was incorporated into an open-access web application software, identifying patients at low or high risk for re-HNP. Preoperative VAS leg, disability, alignment parameters, elevated body mass index, symptom duration, and age were the strongest predictors. Conclusions Our predictive modeling via an ML approach of our large-scale cohort is the first study, to our knowledge, that has identified significant risk factors for the development of re-HNP after initial lumbar decompression. We developed the re-herniation after decompression (RAD) profile index that has been translated into an online screening tool to identify low-high risk patients for re-HNP. Additional validation is needed for potential global implementation.