Cargando…

LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT

INTRO: Evidence about the phenotypic profile of COVID-19 ICU patients in sub- Saharan Africa remains insufficient. Our study aimed to identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (...

Descripción completa

Detalles Bibliográficos
Autores principales: Nyasulu, P., Sigwadi, L., Zemlin, A., Tamuzi, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186906/
http://dx.doi.org/10.1016/j.ijid.2023.04.375
_version_ 1785042647139745792
author Nyasulu, P.
Sigwadi, L.
Zemlin, A.
Tamuzi, J.
author_facet Nyasulu, P.
Sigwadi, L.
Zemlin, A.
Tamuzi, J.
author_sort Nyasulu, P.
collection PubMed
description INTRO: Evidence about the phenotypic profile of COVID-19 ICU patients in sub- Saharan Africa remains insufficient. Our study aimed to identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (ICU) in South Africa. METHODS: We used a latent class analysis (LCA) model in a prospective cohort study of clinical and laboratory data collected from 343 COVID-19 patients between 27 March 2020 and 10 February 2021. A sub-analysis was performed to determine sub-phenotypes associated with clinical outcomes among COVID-19 patients and their impact on survival. FINDINGS: Data from 343 COVID-19 patients were analysed. Two distinct phenotypes 1 and 2, comprising 68.46 % and 31.54% patients respectively, were identified. The phenotype 2 patients were characterized by increased coagulopathy markers (D-dimer, median value 1.73 ng/L vs 0.94 ng/L, p <0.001), end-organ dysfunction (creatinine, median 79 umol/L vs 69.5 umol/L, p <0.003), under-perfusion marker (lactate, median value 1.60 mmol/L, vs 1.20 mmol/L, p <0.001), abnormal cardiac function markers (median N‐terminal pro‐brain natriuretic peptide (NT-proBNP) 314 pg/ml vs 63.5 pg/ml, p <0.001 and median high‐sensitivity cardiac troponin (Hs-TropT) 39 ng/l vs 12 ng/ l, p<0.001) and acute inflammatory syndrome (median neutrophil-to-lymphocyte ratio 15.08 vs value 8.68, p <0.001 and monocyte, median value 0.68 × 109/L vs 0.45 × 109/L, p <0.001). Females in class 2 had lower mean haemoglobin levels than females in class 1 (11.88g/dL vs 12.67g/dL, p = 0.014). In a sub-analysis, mortality and survival were characterized in two sub-phenotypes with increased acute inflammatory syndrome, under-perfusion, end-organ dysfunction, and cardiac function markers in sub-phenotype 2. CONCLUSION: The identification of COVID-19 phenotypes and sub-phenotypes among ICU patients could be used as prognostic markers in day-to-day management of patients. Future studies are required in sub-Sahara Africa to elucidate pathophysiological mechanisms underlying these phenotypes.
format Online
Article
Text
id pubmed-10186906
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Published by Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-101869062023-05-16 LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT Nyasulu, P. Sigwadi, L. Zemlin, A. Tamuzi, J. Int J Infect Dis Article INTRO: Evidence about the phenotypic profile of COVID-19 ICU patients in sub- Saharan Africa remains insufficient. Our study aimed to identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (ICU) in South Africa. METHODS: We used a latent class analysis (LCA) model in a prospective cohort study of clinical and laboratory data collected from 343 COVID-19 patients between 27 March 2020 and 10 February 2021. A sub-analysis was performed to determine sub-phenotypes associated with clinical outcomes among COVID-19 patients and their impact on survival. FINDINGS: Data from 343 COVID-19 patients were analysed. Two distinct phenotypes 1 and 2, comprising 68.46 % and 31.54% patients respectively, were identified. The phenotype 2 patients were characterized by increased coagulopathy markers (D-dimer, median value 1.73 ng/L vs 0.94 ng/L, p <0.001), end-organ dysfunction (creatinine, median 79 umol/L vs 69.5 umol/L, p <0.003), under-perfusion marker (lactate, median value 1.60 mmol/L, vs 1.20 mmol/L, p <0.001), abnormal cardiac function markers (median N‐terminal pro‐brain natriuretic peptide (NT-proBNP) 314 pg/ml vs 63.5 pg/ml, p <0.001 and median high‐sensitivity cardiac troponin (Hs-TropT) 39 ng/l vs 12 ng/ l, p<0.001) and acute inflammatory syndrome (median neutrophil-to-lymphocyte ratio 15.08 vs value 8.68, p <0.001 and monocyte, median value 0.68 × 109/L vs 0.45 × 109/L, p <0.001). Females in class 2 had lower mean haemoglobin levels than females in class 1 (11.88g/dL vs 12.67g/dL, p = 0.014). In a sub-analysis, mortality and survival were characterized in two sub-phenotypes with increased acute inflammatory syndrome, under-perfusion, end-organ dysfunction, and cardiac function markers in sub-phenotype 2. CONCLUSION: The identification of COVID-19 phenotypes and sub-phenotypes among ICU patients could be used as prognostic markers in day-to-day management of patients. Future studies are required in sub-Sahara Africa to elucidate pathophysiological mechanisms underlying these phenotypes. Published by Elsevier Ltd. 2023-05 2023-05-16 /pmc/articles/PMC10186906/ http://dx.doi.org/10.1016/j.ijid.2023.04.375 Text en Copyright © 2023 Published by Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Nyasulu, P.
Sigwadi, L.
Zemlin, A.
Tamuzi, J.
LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title_full LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title_fullStr LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title_full_unstemmed LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title_short LATENT CLASS ANALYSIS: AN INNOVATIVE APPROACH FOR IDENTIFICATION OF CLINICAL AND LABORATORY MARKERS OF DISEASE SEVERITY AMONG COVID-19 PATIENTS ADMITTED TO THE INTENSIVE CARE UNIT
title_sort latent class analysis: an innovative approach for identification of clinical and laboratory markers of disease severity among covid-19 patients admitted to the intensive care unit
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186906/
http://dx.doi.org/10.1016/j.ijid.2023.04.375
work_keys_str_mv AT nyasulup latentclassanalysisaninnovativeapproachforidentificationofclinicalandlaboratorymarkersofdiseaseseverityamongcovid19patientsadmittedtotheintensivecareunit
AT sigwadil latentclassanalysisaninnovativeapproachforidentificationofclinicalandlaboratorymarkersofdiseaseseverityamongcovid19patientsadmittedtotheintensivecareunit
AT zemlina latentclassanalysisaninnovativeapproachforidentificationofclinicalandlaboratorymarkersofdiseaseseverityamongcovid19patientsadmittedtotheintensivecareunit
AT tamuzij latentclassanalysisaninnovativeapproachforidentificationofclinicalandlaboratorymarkersofdiseaseseverityamongcovid19patientsadmittedtotheintensivecareunit