External validation of a prediction model for disability and pain after lumbar disc herniation surgery: a prospective international registry-based cohort study.

IF 2.5 2区 医学 Q1 ORTHOPEDICS
Allan Abbott, Casper Friis Pedersen, Henrik Hedevik, Catharina Parai, Martin A Gorosito, Mikkel Andersen, Tor Ingebrigtsen, Tore K Solberg, Margreth Grotle, Bjørnar Berg
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引用次数: 0

Abstract

Background and purpose:  We aimed to externally validate machine learning models developed in Norway by evaluating their predictive outcome of disability and pain 12 months after lumbar disc herniation surgery in a Swedish and Danish cohort.

Methods:  Data was extracted for patients undergoing microdiscectomy or open discectomy for lumbar disc herniation in the NORspine, SweSpine and DaneSpine national registries. Outcome of interest was changes in Oswestry disability index (ODI) (≥ 22 points), Numeric Rating Scale (NRS) for back pain (≥ 2 points), and NRS for leg pain (≥ 4 points). Model performance was evaluated by discrimination (C-statistic), calibration, overall fit, and net benefit.

Results:  For the ODI model, the NORspine cohort included 22,529 patients, the SweSpine cohort included 10,129 patients, and DaneSpine 5,670 patients. The ODI model's C-statistic varied between 0.76 and 0.81 and calibration slope point estimates varied between 0.84 and 0.99. The C-statistic for NRS back pain varied between 0.70 and 0.76, and calibration slopes varied between 0.79 and 1.03. The C-statistic for NRS leg pain varied between 0.71 and 0.74, and calibration slopes varied between 0.90 and 1.02. There was acceptable overall fit and calibration metrics with minor-modest but explainable heterogeneity observed in the calibration plots. Decision curve analyses displayed clear potential net benefit in treatment in accordance with the prediction models compared with treating all patients or none.

Conclusion:  Predictive performance of machine learning models for treatment success/non-success in disability and pain at 12 months post-surgery for lumbar disc herniation showed acceptable discrimination ability, calibration, overall fit, and net benefit reproducible in similar international contexts. Future clinical impact studies are required.

腰椎间盘突出术后残疾和疼痛预测模型的外部验证:一项前瞻性国际注册队列研究。
背景和目的:我们旨在外部验证挪威开发的机器学习模型,在瑞典和丹麦的队列中评估其对腰椎间盘突出症手术后12个月的残疾和疼痛的预测结果。方法:从NORspine、SweSpine和DaneSpine国家登记中心中提取接受显微椎间盘切除术或开放式椎间盘切除术治疗腰椎间盘突出症的患者的数据。关注的结果是Oswestry残疾指数(ODI)(≥22分)、背痛数值评定量表(NRS)(≥2分)和腿痛NRS(≥4分)的变化。通过判别(c统计)、校准、整体拟合和净效益来评估模型的性能。结果:对于ODI模型,NORspine队列包括22,529例患者,SweSpine队列包括10,129例患者,DaneSpine队列包括5,670例患者。ODI模型的c统计量在0.76 ~ 0.81之间,校正斜率点估计值在0.84 ~ 0.99之间。NRS背部疼痛的c统计量在0.70 ~ 0.76之间,校正斜率在0.79 ~ 1.03之间。NRS腿痛的c统计量在0.71 ~ 0.74之间,校正斜率在0.90 ~ 1.02之间。总体拟合和校准指标可接受,在校准图中观察到轻微但可解释的异质性。决策曲线分析显示,与治疗所有患者或不治疗相比,根据预测模型进行治疗的潜在净收益明显。结论:腰椎间盘突出症术后12个月治疗成功/不成功的残疾和疼痛的机器学习模型的预测性能显示出可接受的区分能力、校准、整体拟合和净效益,可在类似的国际环境中重现。需要进一步的临床影响研究。
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来源期刊
Acta Orthopaedica
Acta Orthopaedica 医学-整形外科
CiteScore
6.40
自引率
8.10%
发文量
105
审稿时长
4-8 weeks
期刊介绍: Acta Orthopaedica (previously Acta Orthopaedica Scandinavica) presents original articles of basic research interest, as well as clinical studies in the field of orthopedics and related sub disciplines. Ever since the journal was founded in 1930, by a group of Scandinavian orthopedic surgeons, the journal has been published for an international audience. Acta Orthopaedica is owned by the Nordic Orthopaedic Federation and is the official publication of this federation.
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