根据临终关怀条件的生存差异:回顾
Patrick D Crowley1, Leslie R Siegel2,3, Francis X Whalen3,4
1Division of Public Health, Infectious Disease, and Occupational Medicine, Mayo Clinic, Rochester, MN, USA.
The American journal of hospice & palliative care
|August 26, 2025
概括
住院患者
科学领域:
- 老年学
- 缓和护理
- 生物统计学
背景情况:
- 临终关怀服务不同患者的病情.
- 有限的近期数据存在于临终关怀后的生存时间.
- 影响生存的关键人口和诊断因素未得到充分研究.
研究的目的:
- 分析住院入院后的生存时间.
- 调查年龄,性别和临终关怀条件 (HQC) 对生存的影响.
- 确定患有较长住院存活时间的小组.
主要方法:
- 使用现有的患者数据进行分析.
- 在入学时计算的年龄和记录的性别.
- 患者被分为九个临终关怀条件的类别.
- 使用威尔科克森等级总和测试进行生存比较.
主要成果:
- 平均存活时间为17. 36天",传染性"HQC存活时间最短 (4. 69天),而"其他"存活时间最长 (28. 53天).
- 女性 (19. 34天) 和老年患者 (20. 51天) 的平均存活时间比男性 (15. 37天) 和年轻患者 (15. 03天) 长.
- 9. 47%的患者存活了至少6个月,女性 (10. 99%),老年人 (12. 44%) 和痴呆症患者 (16. 46%) 的比率更高.
结论:
- 住院生存时间因年龄,性别和诊断而异.
- 调查结果表明临终关怀的适用性评估可能存在偏见.
- 了解这些生存差异对于患者和家庭的期望和护理计划至关重要.
更多相关视频
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
376
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.6K
相关概念视频
Continuing Care
1.6K
Continuing care describes the variety of health, personal, and social services provided over a prolonged period. The need for continuing care is increasing because people are living longer. Many people do not have families or others to care for them. Continuing care is mainly for patients who are disabled, functionally dependent, or suffering from a terminal disease. It is available within institutional settings or in homes. Examples include nursing centers or facilities, assisted living,...
1.6K
Comparing the Survival Analysis of Two or More Groups
285
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...
285
Cancer Survival Analysis
453
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...
453
Assumptions of Survival Analysis
196
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196
Truncation in Survival Analysis
300
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
300
Actuarial Approach
133
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
133
