Cargando…

Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single ho...

Descripción completa

Detalles Bibliográficos
Autores principales: Lambert, Ben, Stopard, Isaac J., Momeni-Boroujeni, Amir, Mendoza, Rachelle, Zuretti, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387798/
https://www.ncbi.nlm.nih.gov/pubmed/35981055
http://dx.doi.org/10.1371/journal.pone.0272442
_version_ 1784770081018871808
author Lambert, Ben
Stopard, Isaac J.
Momeni-Boroujeni, Amir
Mendoza, Rachelle
Zuretti, Alejandro
author_facet Lambert, Ben
Stopard, Isaac J.
Momeni-Boroujeni, Amir
Mendoza, Rachelle
Zuretti, Alejandro
author_sort Lambert, Ben
collection PubMed
description A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.
format Online
Article
Text
id pubmed-9387798
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-93877982022-08-19 Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals Lambert, Ben Stopard, Isaac J. Momeni-Boroujeni, Amir Mendoza, Rachelle Zuretti, Alejandro PLoS One Research Article A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients. Public Library of Science 2022-08-18 /pmc/articles/PMC9387798/ /pubmed/35981055 http://dx.doi.org/10.1371/journal.pone.0272442 Text en © 2022 Lambert et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lambert, Ben
Stopard, Isaac J.
Momeni-Boroujeni, Amir
Mendoza, Rachelle
Zuretti, Alejandro
Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title_full Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title_fullStr Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title_full_unstemmed Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title_short Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals
title_sort using patient biomarker time series to determine mortality risk in hospitalised covid-19 patients: a comparative analysis across two new york hospitals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387798/
https://www.ncbi.nlm.nih.gov/pubmed/35981055
http://dx.doi.org/10.1371/journal.pone.0272442
work_keys_str_mv AT lambertben usingpatientbiomarkertimeseriestodeterminemortalityriskinhospitalisedcovid19patientsacomparativeanalysisacrosstwonewyorkhospitals
AT stopardisaacj usingpatientbiomarkertimeseriestodeterminemortalityriskinhospitalisedcovid19patientsacomparativeanalysisacrosstwonewyorkhospitals
AT momeniboroujeniamir usingpatientbiomarkertimeseriestodeterminemortalityriskinhospitalisedcovid19patientsacomparativeanalysisacrosstwonewyorkhospitals
AT mendozarachelle usingpatientbiomarkertimeseriestodeterminemortalityriskinhospitalisedcovid19patientsacomparativeanalysisacrosstwonewyorkhospitals
AT zurettialejandro usingpatientbiomarkertimeseriestodeterminemortalityriskinhospitalisedcovid19patientsacomparativeanalysisacrosstwonewyorkhospitals