Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.

Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality traj...

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Autores principales:	Hyojung Paik, Jimin Kim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/128e62db365f4c6caa12e2950f1eb19a
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spelling	oai:doaj.org-article:128e62db365f4c6caa12e2950f1eb19a2021-12-02T20:07:57ZCondensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.1932-620310.1371/journal.pone.0257894https://doaj.org/article/128e62db365f4c6caa12e2950f1eb19a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257894https://doaj.org/toc/1932-6203Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.Hyojung PaikJimin KimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257894 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hyojung Paik Jimin Kim Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
description	Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.
format	article
author	Hyojung Paik Jimin Kim
author_facet	Hyojung Paik Jimin Kim
author_sort	Hyojung Paik
title	Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
title_short	Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
title_full	Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
title_fullStr	Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
title_full_unstemmed	Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
title_sort	condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/128e62db365f4c6caa12e2950f1eb19a
work_keys_str_mv	AT hyojungpaik condensedtrajectoryofthetemporalcorrelationofdiseasesandmortalityextractedfromover300000patientsinhospitals AT jiminkim condensedtrajectoryofthetemporalcorrelationofdiseasesandmortalityextractedfromover300000patientsinhospitals
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Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.

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