在接受治疗时,老年患者的血清25(OH) D水平和一年内死亡率
Nadav Abel1, Felicia Stern2, Sari Tal3
1Geriatric Department, Hartzfeld Hospital at Kaplan Medical Center, Affiliated with the Hebrew University of Jerusalem, Rehovot, Israel.
BMC geriatrics
|August 8, 2024
概括
维生素D缺乏症 (维生素D缺乏症) 在老年病房的老年人中很常见,并预测一年内死亡率. 对这些易受伤害的患者进行检测和早期的维生素D干预可能会改善结果.
科学领域:
- 老年医学 老年医学
- 营养科学 营养科学
- 临床流行病学 临床流行病学
背景情况:
- 维生素D缺乏与不良健康结果越来越相关,包括老年人死亡率.
- 与年龄相关的疾病和脆弱性有助于与低维生素D水平相关的更高风险.
研究的目的:
- 调查80岁及以上患者中维生素D缺乏症的流行情况.
- 研究维生素D水平与住院一年内死亡率之间的关联.
- 为了确定这个患者队列中死亡率的预测因素.
主要方法:
- 对830名老年患者 (≥80岁) 电子健康记录的回顾性分析.
- 收集的数据包括人口统计,诊断,实验室结果 (包括25-氧维生素D),药物和一年内死亡率.
- 幸存者和已故患者之间的特征比较.
主要成果:
- 超过一半的患者 (53.6%) 的维生素D水平低于50nmol/L;32%的患者的维生素D水平≤35nmol/L.
- 死亡率预测因素包括老年,男性性别,心力衰竭 (CHF),较高的药物计数,较低的白蛋白,较高的维生素B12,维生素D<50nmol/L,以及抗精神病药物/抗凝剂的使用.
- 已故的患者患有更多的并发病,住院时间更长,住院时间更频繁.
结论:
- 低维他命症D是住院老年人一年死亡率的重要预测因素.
- 应考虑对急性疾病的老年患者进行维生素D测试和早期干预.
- 需要进一步的研究来确定这一群体中全面的死亡预测因素.
相关概念视频
01:20Actuarial Approach
69
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,...
69
01:21Cancer Survival Analysis
334
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...
334
01:29Bone Disorders
3.5K
Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
3.5K
01:15Assumptions of Survival Analysis
114
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.
114
01:20Comparing the Survival Analysis of Two or More Groups
166
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...
166
01:24Kaplan-Meier Approach
115
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,...
115