Cargando…
Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis
AIM: To develop an accurate lab score based on in-hospital patients’ potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission. METHODS: To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biolo...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462772/ https://www.ncbi.nlm.nih.gov/pubmed/36084080 http://dx.doi.org/10.1371/journal.pone.0273006 |
_version_ | 1784787263803097088 |
---|---|
author | Sarkar, Arnab Sanyal, Surojit Majumdar, Agniva Tewari, Devendra Nath Bhattacharjee, Uttaran Pal, Juhi Chakrabarti, Alok Kumar Dutta, Shanta |
author_facet | Sarkar, Arnab Sanyal, Surojit Majumdar, Agniva Tewari, Devendra Nath Bhattacharjee, Uttaran Pal, Juhi Chakrabarti, Alok Kumar Dutta, Shanta |
author_sort | Sarkar, Arnab |
collection | PubMed |
description | AIM: To develop an accurate lab score based on in-hospital patients’ potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission. METHODS: To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer’s test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022. RESULTS: Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883–0.945, 95% CI) and 0.873 (0.778–0.969, 95% CI) respectively. CONCLUSION: The development of proper lab scores, based on patients’ clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients’ survivability. |
format | Online Article Text |
id | pubmed-9462772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94627722022-09-10 Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis Sarkar, Arnab Sanyal, Surojit Majumdar, Agniva Tewari, Devendra Nath Bhattacharjee, Uttaran Pal, Juhi Chakrabarti, Alok Kumar Dutta, Shanta PLoS One Research Article AIM: To develop an accurate lab score based on in-hospital patients’ potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission. METHODS: To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer’s test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022. RESULTS: Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883–0.945, 95% CI) and 0.873 (0.778–0.969, 95% CI) respectively. CONCLUSION: The development of proper lab scores, based on patients’ clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients’ survivability. Public Library of Science 2022-09-09 /pmc/articles/PMC9462772/ /pubmed/36084080 http://dx.doi.org/10.1371/journal.pone.0273006 Text en © 2022 Sarkar 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 Sarkar, Arnab Sanyal, Surojit Majumdar, Agniva Tewari, Devendra Nath Bhattacharjee, Uttaran Pal, Juhi Chakrabarti, Alok Kumar Dutta, Shanta Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title | Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title_full | Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title_fullStr | Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title_full_unstemmed | Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title_short | Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis |
title_sort | development of lab score system for predicting covid-19 patient severity: a retrospective analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462772/ https://www.ncbi.nlm.nih.gov/pubmed/36084080 http://dx.doi.org/10.1371/journal.pone.0273006 |
work_keys_str_mv | AT sarkararnab developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT sanyalsurojit developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT majumdaragniva developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT tewaridevendranath developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT bhattacharjeeuttaran developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT paljuhi developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT chakrabartialokkumar developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis AT duttashanta developmentoflabscoresystemforpredictingcovid19patientseverityaretrospectiveanalysis |