Utility of ISARIC 4C Mortality Score, Vaccination History, and Anti-S Antibody Titre in Predicting Risk of Severe COVID-19

The ISARIC 4C Mortality score was developed to predict mortality risk among patients with COVID-19. Its performance among vaccinated individuals is understudied. This is a retrospective study of all patients with SARS-CoV-2 infection admitted to the National Centre for Infectious Diseases, Singapore, from January-2020 to December-2021. Demographic, clinical, and laboratory data were extracted, and multiple logistic regression (MLR) models were developed to predict the relationship between ISARIC score, vaccination status, anti-S antibody titre, and severe COVID-19. A total of 6377 patients were identified, of which 5329 met the study eligibility criteria. The median age of the patients was 47 years (IQR 35-71), 1264 (23.7%) were female, and 1239 (25.7%) were vaccinated. Severe disease occurred in 499 (9.4%) patients, including 133 (2.5%) deaths. After stratification, 3.0% of patients with low (0-4), 17.8% of patients with moderate (5-9), and 36.2% of patients with high (≥10) ISARIC scores developed severe COVID-19. Vaccination was associated with a reduced risk of progression to severe COVID-19 in the MLR model: aOR 0.88 (95% CI: 0.86-0.90), and the risk of severe COVID-19 decreased inversely to anti-S antibody titres. The anti-S antibody titre should be further investigated as an adjunct to the ISARIC score to triage COVID-19 patients for hospital admission and antiviral therapy.

Authors

Institutions

Publication Details

Journal
Viruses
Published
2024-10-12
DOI
https://doi.org/10.3390/v16101604
Citations
1
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
0.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utility of ISARIC 4C Mortality Score, Vaccination History, and Anti-S Antibody Titre in Predicting Risk of Severe COVID-19

Jean‐Marc Chavatte, Robert B. Hawkins, Barnaby Edward Young, Cher Heng Tan et al.
1 citations
Viruses
SARS-CoV-2 and COVID-19 Research
0.39
article

Utility of ISARIC 4C Mortality Score, Vaccination History, and Anti-S Antibody Titre in Predicting Risk of Severe COVID-19

Jean‐Marc Chavatte, Robert B. Hawkins, Barnaby Edward Young, Cher Heng Tan, Travis Ren Teen Chia, David Chien Lye, Wen Xiang Chen, Yong Han Ting, Lin Pin Koh, Samuel Sherng Young Wang, Jordan Sim, Kelvin Bryan Tan
article en
1 citations

Abstract

The ISARIC 4C Mortality score was developed to predict mortality risk among patients with COVID-19. Its performance among vaccinated individuals is understudied. This is a retrospective study of all patients with SARS-CoV-2 infection admitted to the National Centre for Infectious Diseases, Singapore, from January-2020 to December-2021. Demographic, clinical, and laboratory data were extracted, and multiple logistic regression (MLR) models were developed to predict the relationship between ISARIC score, vaccination status, anti-S antibody titre, and severe COVID-19. A total of 6377 patients were identified, of which 5329 met the study eligibility criteria. The median age of the patients was 47 years (IQR 35-71), 1264 (23.7%) were female, and 1239 (25.7%) were vaccinated. Severe disease occurred in 499 (9.4%) patients, including 133 (2.5%) deaths. After stratification, 3.0% of patients with low (0-4), 17.8% of patients with moderate (5-9), and 36.2% of patients with high (≥10) ISARIC scores developed severe COVID-19. Vaccination was associated with a reduced risk of progression to severe COVID-19 in the MLR model: aOR 0.88 (95% CI: 0.86-0.90), and the risk of severe COVID-19 decreased inversely to anti-S antibody titres. The anti-S antibody titre should be further investigated as an adjunct to the ISARIC score to triage COVID-19 patients for hospital admission and antiviral therapy.

VirusesVol. 16(10)
Ministry of Health (SG), National University of Singapore (SG), Nanyang Technological University (SG), Tan Tock Seng Hospital (SG), National Centre for Infectious Diseases (SG)
Good health and well-being
Openalex Percentile: Top 36%
SARS-CoV-2 and COVID-19 Research
0.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.