Cargando…
Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study
Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time l...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116568/ https://www.ncbi.nlm.nih.gov/pubmed/33996864 http://dx.doi.org/10.3389/fmed.2021.661940 |
_version_ | 1783691422075977728 |
---|---|
author | Chen, Anne Zhao, Zirun Hou, Wei Singer, Adam J. Li, Haifang Duong, Tim Q. |
author_facet | Chen, Anne Zhao, Zirun Hou, Wei Singer, Adam J. Li, Haifang Duong, Tim Q. |
author_sort | Chen, Anne |
collection | PubMed |
description | Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration. |
format | Online Article Text |
id | pubmed-8116568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81165682021-05-14 Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study Chen, Anne Zhao, Zirun Hou, Wei Singer, Adam J. Li, Haifang Duong, Tim Q. Front Med (Lausanne) Medicine Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration. Frontiers Media S.A. 2021-04-29 /pmc/articles/PMC8116568/ /pubmed/33996864 http://dx.doi.org/10.3389/fmed.2021.661940 Text en Copyright © 2021 Chen, Zhao, Hou, Singer, Li and Duong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Chen, Anne Zhao, Zirun Hou, Wei Singer, Adam J. Li, Haifang Duong, Tim Q. Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title | Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title_full | Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title_fullStr | Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title_full_unstemmed | Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title_short | Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study |
title_sort | time-to-death longitudinal characterization of clinical variables and longitudinal prediction of mortality in covid-19 patients: a two-center study |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116568/ https://www.ncbi.nlm.nih.gov/pubmed/33996864 http://dx.doi.org/10.3389/fmed.2021.661940 |
work_keys_str_mv | AT chenanne timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy AT zhaozirun timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy AT houwei timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy AT singeradamj timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy AT lihaifang timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy AT duongtimq timetodeathlongitudinalcharacterizationofclinicalvariablesandlongitudinalpredictionofmortalityincovid19patientsatwocenterstudy |