缓解护理需求复杂性的PALCOM尺度的验证研究:在晚期癌症患者的队列研究
Margarita Viladot1, Jose-Luís Gallardo-Martínez2, Fany Hernandez-Rodríguez3
1Unit of Supportive and Palliative Care in Cancer, Medical Oncology Department, Hospital Clínic de Barcelona, University of Barcelona, 08036 Barcelona, Spain.
Cancers
|August 26, 2023
概括
帕尔科姆尺度准确地识别了癌症患者复杂的息护理需求,帮助适当的转诊到早期息护理 (EPC) 团队. 这项验证证实了其在评估患者复杂性的准确性,以更好地管理护理.
科学领域:
- 在瘤学瘤学.
- 抚慰性护理是一种缓解性护理.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 以患者为中心的护理模式需要对预后和护理复杂性的准确评估,以便早期缓和护理 (EPC) 转诊.
- 帕尔科姆 (PALCOM) 规模是一个5个领域的工具,旨在量化癌症患者的息护理需求复杂性.
研究的目的:
- 验证PALCOM尺度,用于评估癌症患者息护理需求的复杂性.
- 为了比较PALCOM尺度的表现与专家实证评估 (EA).
主要方法:
- 一项前性队列研究,涉及283名晚期癌症患者.
- 使用肯德尔的tau和F1分数,PALCOM规模评级与专家实证评估 (EA) 的比较.
- 接收器操作特征 (ROC) 曲线分析,以评估PALCOM尺度的预测能力.
主要成果:
- 在PALCOM和EA之间,复杂度分布 (低,中,高) 没有显著差异.
- 在PALCOM和EA之间有很高的相关性 (肯德尔的tau=0.81) 和准确性 (F1-score=0.84).
- 帕尔科姆显示出强大的预测能力 (AUC=0.907对于高复杂度,0.902对于低复杂度).
结论:
- 帕尔科姆尺度表是一个精确的工具,用于确定息护理需求的复杂性水平.
- 准确的复杂性识别对于在以患者为中心的模型中与EPC团队进行适当的转诊和共享护理至关重要.
相关概念视频
01:21Cancer Survival Analysis
383
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...
383
02:56Treatment Resistant Cancers
3.3K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.3K
01:24Kaplan-Meier Approach
179
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,...
179
01:20Comparing the Survival Analysis of Two or More Groups
222
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...
222