Machine learning predictive model for lumbar disc reherniation following microsurgical discectomy.

IF 1.9 Q3 CLINICAL NEUROLOGY Brain & spine Pub Date : 2024-10-10 eCollection Date: 2024-01-01 DOI:10.1016/j.bas.2024.103918
Angel G Mehandzhiyski, Nikola A Yurukov, Petar L Ilkov, Dilyana P Mikova, Nikolay S Gabrovsky
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Abstract

Introduction: The integration of machine learning (ML) algorithms into the field of neurosurgery has the potential to facilitate the decision-making process for the surgeons, improve the surgical outcomes and the overall patient satisfaction rates. Reoperations for same level lumbar disc reherniation are associated with poorer outcomes and greater rate of complications.

Research question: Proper preoperative patient evaluation could reveal the individuals at higher risk of reherniation. A novel machine learning algorithm was used for the creation of a predictive scoring system for lumbar disc reherniation for patients requiring microdiscectomy without fusion.

Material and methods: Retrospective chart review was completed of all adult patients that underwent microdiscectomy without fusion for symptomatic single level LDH, in a single center, over the last 3 years. 230 patients met the inclusion criteria. 19 of them required a second surgical intervention due to same level reherniation.

Results: Utilizing the Risk-SLIM model, the Lumbar Reherniation Score (LRS) was created. The score's accuracy was tested against other model architectures, and a standard five-fold cross-validation was performed. The LRS has AUC of 0.87, confusion matrix accuracy of 0.74, Matthews correlation coefficient of 0.36 and informedness of 0.62. The LRS individual reherniation risk probability ranges from 0% to 88.1%.

Discussion and conclusion: The LRS is a novel, easy-to-use, patient-specific tool for preoperative prediction of the individual patient-specific risk of same level symptomatic reherniation following microdiscectomy. Further validation and testing of the model is needed before it can be used in real-life patient treatment.

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显微外科椎间盘切除术后腰椎间盘突出症的机器学习预测模型。
简介将机器学习(ML)算法融入神经外科领域有可能促进外科医生的决策过程,提高手术效果和患者的总体满意度。同一水平腰椎间盘突出症的再次手术与较差的疗效和较高的并发症发生率有关:研究问题:对患者进行适当的术前评估可以发现哪些人患腰椎间盘再突出症的风险较高。研究采用了一种新颖的机器学习算法,为需要进行不融合显微椎间盘切除术的患者建立腰椎间盘再突出预测评分系统:对过去 3 年中在一个中心因有症状的单水平 LDH 而接受不融合显微椎间盘切除术的所有成人患者进行了回顾性病历审查。230 名患者符合纳入标准。结果:利用风险-SLIM模型,创建了腰椎再疝评分(LRS)。该评分的准确性与其他模型架构进行了对比测试,并进行了标准的五倍交叉验证。LRS 的 AUC 为 0.87,混淆矩阵准确度为 0.74,马修斯相关系数为 0.36,知情度为 0.62。LRS 的个体再遗传风险概率范围为 0% 至 88.1%:LRS是一种新颖、易用、针对特定患者的工具,用于术前预测微椎间孔切除术后患者发生同水平症状性再疝的风险。在将该模型用于实际患者治疗之前,还需要对其进行进一步的验证和测试。
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来源期刊
Brain & spine
Brain & spine Surgery
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
71 days
期刊最新文献
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