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预测局部性前列腺癌治疗后患者报告的结果:国际队列研究中的模型开发和评估.

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  • 1Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA; Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA.

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使用基线因素预测前列腺癌治疗后患者的结果在全球研究中显示出适度的准确性. 大量的国际数据突出显示了异质性,这表明需要针对特定区域的预测模型.

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临床决策的过程前列腺瘤/诊断成像检查前列腺瘤/流行病学前列腺瘤/治疗方法警的等待等待的等待

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科学领域:

  • 在瘤学瘤学.
  • 医疗保健服务研究 医疗服务研究
  • 生物统计学 生物统计学

背景情况:

  • 针对局部性前列腺癌 (PCa) 的共同决策需要了解预测患者报告结果 (PRO) 的因素.
  • 之前对PCa治疗结果的预测模型仅限于小型,单区域患者队列.
  • 移动真北全球注册表 (TNGR) 提供了一个大型的,跨国数据集,用于开发可靠的预测模型.

研究的目的:

  • 在局部PCa患者中开发和评估12个月PRO的预测模型.
  • 利用广泛的,多国TNGR数据集,以加强模型开发.
  • 为了确定基线因素,预测PCa治疗后的功能结果.

主要方法:

  • 分析了来自15个国家的27,499名男性局部PCa队列 (2016-2022年).
  • 患者被随机分配到培训 (n=18,332) 和验证 (n=9,167) 队列.
  • 多变量线性回归集成了基线PRO,人口统计,国家,治疗方式和瘤特征,以预测5个EPIC-26领域的变化.

主要成果:

  • 基线功能,治疗类型和瘤特征显著影响了12个月的功能变化.
  • 模型解释了验证队列中15% (尿失禁),14% (刺激性尿道),19% (肠道),32% (性) 和28% (荷尔蒙) 的差异.
  • 性能在不同的功能领域有所不同,表明差异性可预测性.

结论:

  • 对PCa治疗后功能性结果的首次全球分析显示,由于区域和实践的显著异质性,预测准确性适度.
  • 这些发现凸显了通用预测模型的局限性,以及大规模国际数据对结果基准分析的重要性.
  • 未来使用TNGR数据的研究将集中在特定区域的模型上,以支持个性化的患者咨询和质量改进.