脊柱转移性疾病患者的存活率,非手术治疗用放射治疗:SORG-ML算法是否相关?
Brian P Fenn1,2, Aditya V Karhade1,3, Olivier Q Groot1
1Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School.
Clinical spine surgery
|February 7, 2024
概括
脊柱转移性疾病的SORG-ML生存算法准确地预测了放射治疗患者的结果,显示了非手术管理的良好概括性. 这些发现支持在这个患者群体的临床决策中使用它们.
科学领域:
- 在瘤学瘤学.
- 神经外科 神经外科
- 医疗信息学 医疗信息学
背景情况:
- 脊髓转移性疾病是一个重大挑战,影响患者的生存和生活质量.
- 现有的SORG-ML算法用于生存预测,在手术管理的患者中得到了开发和验证.
- 在非手术治疗患者的外部验证对于更广泛的适用性至关重要.
研究的目的:
- 通过外部验证SORG-ML算法来预测脊髓转移性疾病患者的存活率.
- 为了评估算法的表现在一个队列管理非手术的放射治疗.
主要方法:
- 对2074名成年患者进行了回顾性队列研究,这些患者因脊髓转移性疾病接受了放射治疗.
- 使用歧视 (AUC) 的绩效评估,校准图,决策曲线分析和Brier分数.
- 主要结局包括90天和1年死亡率.
主要成果:
- SORG-ML算法表现出良好的预测性能,AUC在90天和1年死亡率中从0.76到0.87不等.
- 观察到公平的校准和积极的净益处,表明临床效用.
- 使用多重归算的验证 (n=2074) 与完整的病例分析 (n=415) 相比显示出更好的性能.
结论:
- 脊柱转移性疾病生存率的SORG-ML算法有效地对通过辐射进行非手术治疗的患者进行了概括.
- 这些经过验证的算法可以帮助对接受放射治疗的患者进行预后评估和治疗计划.
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