使用医疗保健支出模式预测老年人跌倒后的死亡风险:基于人口的队列研究
Alexandros Katsiferis1,2, Laust Hvas Mortensen1,2, Mark P Khurana1
1Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Age and ageing
|August 31, 2023
概括
医疗保健支出模式可以预测急诊室跌倒后老年人的1年死亡率. 这种预后模型有助于对高风险患者的临床决策,改善患者的治疗结果.
科学领域:
- 老年学是一门学科.
- 医疗保健服务研究 医疗服务研究
- 预测分析是一种预测分析.
背景情况:
- 跌倒是老年人出现在急诊室 (ED) 的重大发病和死亡原因之一.
- 准确的预后模型对于指导临床决策和为这些脆弱患者分配资源至关重要.
研究的目的:
- 开发和验证65岁及以上的个人1年死亡率的预后模型,这些人因跌倒而出现在ED.
- 调查医疗保健支出模式作为预测因素在这个预后模型中的实用性.
主要方法:
- 一项基于人口的队列研究包括35997名65岁以上的个人,他们在2013年经历了跌倒,随访1年.
- 从入院前2年的医疗保健支出指标 (弹性动态指标,DIOR) 与临床和社会人口统计因素一起分析.
- 开发了多变量逻辑回归模型,并使用10倍交叉验证进行内部验证,通过AUC,Brier分数和校准评估性能.
主要成果:
- 纳入年龄,性别,药物数量和医疗保健支出DIORs的模型显示出高度歧视 (AUC: 81.1%).
- 与仅使用年龄和性别的模型相比,医疗保健支出模式显著提高了预测准确性 (AUC: 72.5%).
- 该模型显示了良好的校准和跨越各种概率值的潜在临床实用性.
结论:
- 医疗保健支出模式为老年人在ED下降后的1年死亡率提供了相当大的预测价值.
- 拟议的方法通过将纵向健康相关支出数据与传统预测指标相结合来增强预后评估.
- 这种方法可以帮助临床医生识别高风险失败者,并为改善结果量身定制干预措施.
相关概念视频
01:20Actuarial Approach
96
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,...
96
01:15Assumptions of Survival Analysis
153
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.
153
01:21Cancer Survival Analysis
381
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...
381
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
01:24Kaplan-Meier Approach
178
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,...
178