瘤学家在一般医院的常规实践中对临床预后的准确性有多高?通过特定的预后培训计划可以改善吗:前性干预研究研究
Irma Kupf1, Gabriele Thanner2, Michael Gerken3
1Department of Dermatology and Allergy, Ludwig-Maximilians-Universität München, München, Germany.
BMJ open
|June 18, 2024
概括
癌症患者的医生预测准确度随着培训而提高,特别是在短期预测方面. 长期预后和电子工具的益处有限,但培训对所有经验水平都有帮助.
科学领域:
- 在瘤学瘤学.
- 医学教育 医学教育
- 临床研究 临床研究
背景情况:
- 有效的临床预后对于瘤学家来说至关重要,以提供最佳的癌症护理.
- 以前关于预后的研究主要集中在息护理机构的患者身上.
- 这项研究针对更长寿命的更广泛的癌症患者队列的预后准确性.
研究的目的:
- 为了评估医生对多样化的癌症患者群体的预后准确性,这些癌症患者的平均预期寿命超过两年.
- 为了确定一个专业的预后培训计划是否可以提高医生的预后能力.
- 评估电子预后工具对医生准确性的影响.
主要方法:
- 一个前性,单中心的研究进行了三个一个月的阶段,涉及18名医生和736名癌症患者.
- 第1阶段评估了没有培训的基线预后. 第二阶段和第三阶段包括预后培训计划.
- 医生通过使用惊喜问题 (SQ) 提供预后估计,用于短期 (≤6个月) 和临床医生预测生存 (CPS) 提供长期预后,在第三阶段使用电子工具.
主要成果:
- 医生对短期预测的预后准确性 (SQ) 从第一阶段的72.6%显著提高到第三阶段的84.3% (p<0.001).
- 在第三阶段,概率学SQ也表现出高准确度 (83.1%).
- 临床预测生存率 (CPS) 的准确性仍然很低,为25.9%,并没有显著改善;单独使用电子工具的表现比医生要差,并且没有提高医生的表现.
结论:
- 一个有针对性的预后培训计划可以显著提高瘤学家的短期和中期预后准确性.
- 该培训使所有经验水平的医生受益,从住院医生到高级瘤学家.
- 通过这种培训,无法提高长期预后准确度,电子工具也无法提高医生的表现.
更多相关视频
相关概念视频
Cancer Survival Analysis
342
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
342
Comparing the Survival Analysis of Two or More Groups
177
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
177
Kaplan-Meier Approach
129
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
129


