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Predictive biosignatures for hospitalization in patients with virologically confirmed COVID-19
Kung-Hao Liang1,2,3,4, Yu-Chun Chen5,6,7, Chun-Yi Hsu1,6
1Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Insights
A new biosignature model using complete blood count (CBC) effectively predicts severe COVID-19 hospitalization risk. Genetic variants in IKZF1, ABLIM1, and MT-ND3 also correlate with hospitalization outcomes, offering insights for patient management.
Area of Science:
- Infectious Disease Epidemiology
- Genomics and Precision Medicine
- Clinical Diagnostics
Background:
- Coronavirus disease 2019 (COVID-19) severity varies due to viral and host factors.
- Accurate diagnosis of SARS-CoV-2 infection is crucial for patient management.
- Assessing patient condition via pulse oximetry, chest X-ray, and CBC aids in delivering appropriate medical care.
Purpose of the Study:
- To develop predictive biosignatures for severe COVID-19 requiring hospitalization.
- To differentiate patients needing intensive care from those manageable in less intensive settings.
- To identify genetic factors influencing COVID-19 severity and hospitalization risk.
Main Methods:
- Retrospective analysis of 7897 adult patients with confirmed SARS-CoV-2 infection.
- Comprehensive complete blood count (CBC) testing for all participants.
- Genome-wide genotyping of approximately 424,000 variants in a subset of 1867 patients.
Main Results:
- A validated biosignature model using CBC measurements predicted hospitalization events with high statistical significance (p < 10 -8).
- A "very high risk" group (>60% hospitalization rate) was identified, distinct from the general patient population (~30% rate).
- Genome-wide association study identified significant genetic variants in chromosomes 7, 10, and M (IKZF1, ABLIM1, MT-ND3) associated with hospitalization risk.
Conclusions:
- A robust biosignature model for predicting severe COVID-19 and hospitalization has been developed and validated.
- Identified genomic variants, including those in IKZF1, offer novel insights into infectious disease mechanisms.
- These findings contribute to advancing medical care and research in infectious diseases.
Background:
Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, presents with varying severity among individuals. Both viral and host factors can influence the severity of acute and chronic COVID-19, with chronic COVID-19 commonly referred to as long COVID. SARS-CoV-2 infection can be properly diagnosed by performing real-time reverse transcription polymerase chain reaction analysis of nasal swab samples. Pulse oximetry, chest X-ray, and complete blood count (CBC) analysis can be used to assess the condition of the patient to ensure that the appropriate medical care is delivered. This study aimed to develop biosignatures that can be used to distinguish between patients who are likely to develop severe disease and require hospitalization from patients who can be safely monitored in less intensive settings.
Methods:
A retrospective investigation was conducted on 7897 adult patients with virologically confirmed SARS-CoV-2 infection between January 26, 2020, and November 30, 2023; all patients underwent comprehensive CBC testing at Taipei Veterans General Hospital. Among them, 1867 patients were independently recruited for a population study involving genome-wide genotyping of approximately 424 000 genomic variants. Therefore, the participants were divided into two patient cohorts, one with genomic data (n = 1867) and one without (n = 6030) for model validation and training, respectively.
Results:
We constructed and validated a biosignature model by using a combination of CBC measurements to predict subsequent hospitalization events (hazard ratio = 3.38, 95% confidence interval: 3.07-3.73 for the training cohort and 3.03 [2.46-3.73] for the validation cohort; both p < 10 -8 ). The obtained scores were used to identify the top quartile of patients, who formed the "very high risk" group with a significantly higher cumulative incidence of hospitalization (log-rank p < 10 -8 in both the training and validation cohorts). The "very high risk" group exhibited a cumulative hospitalization rate of >60%, whereas the rate for the other patients was approximately 30% over a 1.5-year period, providing a binary classification of patients with distinct hospitalization risks. To investigate the genetic factors mediating this risk, we conducted a genome-wide association study. Specific regions in chromosomes 7 and 10 and the mitochondrial chromosome (M), harboring IKAROS family zinc finger 1 ( IKZF1 ), actin binding LIM protein 1 ( ABLIM1 ), and mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 3 ( MT-ND3 ), exhibited prominent associations with binary risk classification. The identified exonic variants of IKZF1 are linked to several autoimmune diseases. Notably, people with different genotypes of the leading variants (rs4132601, rs141492519, and Affx-120744614) exhibited varying cumulative hospitalization rates after infection.
Conclusion:
We successfully developed and validated a biosignature model of COVID-19 severe disease in virologically confirmed patients. The identified genomic variants provide new insights for infectious disease research and medical care.
