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Individual differences in normal body temperature: longitudinal big data analysis of patient records
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 infecti...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group Ltd.
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727437/ https://www.ncbi.nlm.nih.gov/pubmed/29237616 http://dx.doi.org/10.1136/bmj.j5468 |
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author | Obermeyer, Ziad Samra, Jasmeet K Mullainathan, Sendhil |
author_facet | Obermeyer, Ziad Samra, Jasmeet K Mullainathan, Sendhil |
author_sort | Obermeyer, Ziad |
collection | PubMed |
description | 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(2), 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. |
format | Online Article Text |
id | pubmed-5727437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BMJ Publishing Group Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-57274372017-12-19 Individual differences in normal body temperature: longitudinal big data analysis of patient records Obermeyer, Ziad Samra, Jasmeet K Mullainathan, Sendhil BMJ Research 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(2), 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. BMJ Publishing Group Ltd. 2017-12-13 /pmc/articles/PMC5727437/ /pubmed/29237616 http://dx.doi.org/10.1136/bmj.j5468 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Research Obermeyer, Ziad Samra, Jasmeet K Mullainathan, Sendhil Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title | Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title_full | Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title_fullStr | Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title_full_unstemmed | Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title_short | Individual differences in normal body temperature: longitudinal big data analysis of patient records |
title_sort | individual differences in normal body temperature: longitudinal big data analysis of patient records |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727437/ https://www.ncbi.nlm.nih.gov/pubmed/29237616 http://dx.doi.org/10.1136/bmj.j5468 |
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