Suppr超能文献

正常体温的个体差异:患者记录的纵向大数据分析

Individual differences in normal body temperature: longitudinal big data analysis of patient records.

作者信息

Obermeyer Ziad, Samra Jasmeet K, Mullainathan Sendhil

机构信息

Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA

Department of Emergency Medicine and Health Care Policy, Harvard Medical School, Boston, MA, USA.

出版信息

BMJ. 2017 Dec 13;359:j5468. doi: 10.1136/bmj.j5468.

Abstract

OBJECTIVE

To estimate individual level body temperature and to correlate it with other measures of physiology and health.

DESIGN

Observational cohort study.

SETTING

Outpatient clinics of a large academic hospital, 2009-14.

PARTICIPANTS

35 488 patients who neither received a diagnosis for infections nor were prescribed antibiotics, in whom temperature was expected to be within normal limits.

MAIN OUTCOME MEASURES

Baseline temperatures at individual level, estimated using random effects regression and controlling for ambient conditions at the time of measurement, body site, and time factors. Baseline temperatures were correlated with demographics, medical comorbidities, vital signs, and subsequent one year mortality.

RESULTS

In a diverse cohort of 35 488 patients (mean age 52.9 years, 64% women, 41% non-white race) with 243 506 temperature measurements, mean temperature was 36.6°C (95% range 35.7-37.3°C, 99% range 35.3-37.7°C). Several demographic factors were linked to individual level temperature, with older people the coolest (-0.021°C for every decade, P<0.001) and African-American women the hottest (versus white men: 0.052°C, P<0.001). Several comorbidities were linked to lower temperature (eg, hypothyroidism: -0.013°C, P=0.01) or higher temperature (eg, cancer: 0.020, P<0.001), as were physiological measurements (eg, body mass index: 0.002 per m/kg, P<0.001). Overall, measured factors collectively explained only 8.2% of individual temperature variation. Despite this, unexplained temperature variation was a significant predictor of subsequent mortality: controlling for all measured factors, an increase of 0.149°C (1 SD of individual temperature in the data) was linked to 8.4% higher one year mortality (P=0.014).

CONCLUSIONS

Individuals' baseline temperatures showed meaningful variation that was not due solely to measurement error or environmental factors. Baseline temperatures correlated with demographics, comorbid conditions, and physiology, but these factors explained only a small part of individual temperature variation. Unexplained variation in baseline temperature, however, strongly predicted mortality.

摘要

目的

估计个体体温,并将其与其他生理和健康指标相关联。

设计

观察性队列研究。

地点

一家大型学术医院的门诊诊所,2009 - 2014年。

参与者

35488名既未被诊断为感染也未被开具抗生素处方的患者,预计其体温在正常范围内。

主要观察指标

个体水平的基线体温,采用随机效应回归估计,并控制测量时的环境条件、身体部位和时间因素。基线体温与人口统计学特征、合并症、生命体征以及随后一年的死亡率相关。

结果

在一个包含35488名患者(平均年龄52.9岁,64%为女性,41%为非白人种族)的多样化队列中,进行了243506次体温测量,平均体温为36.6°C(95%范围为35.7 - 37.3°C,99%范围为35.3 - 37.7°C)。几个人口统计学因素与个体水平的体温相关,老年人体温最低(每十年降低0.021°C,P<0.001),非裔美国女性体温最高(与白人男性相比:高0.052°C,P<0.001)。几种合并症与较低体温(如甲状腺功能减退:-0.013°C,P = 0.01)或较高体温(如癌症:0.020,P<0.001)相关,生理测量指标(如体重指数:每m²/kg为0.002,P<0.001)也是如此。总体而言,所测量的因素共同仅解释了个体体温变化的8.2%。尽管如此,无法解释的体温变化是随后死亡率的一个重要预测因素:在控制所有测量因素后,体温升高0.149°C(数据中个体体温的1个标准差)与一年死亡率升高8.4%相关(P = 0.014)。

结论

个体的基线体温显示出有意义的变化,这并非仅由测量误差或环境因素导致。基线体温与人口统计学特征、合并症情况和生理指标相关,但这些因素仅解释了个体体温变化的一小部分。然而,基线体温中无法解释的变化强烈预测了死亡率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08c8/5727437/792941f90ad2/obez041793.f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验