Prediction models for COVID-19 disease outcomes.
Emerg Microbes Infect
; 13(1): 2361791, 2024 Dec.
Article
in En
| MEDLINE
| ID: mdl-38828796
ABSTRACT
SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
SARS-CoV-2
/
COVID-19
/
Hospitalization
Limits:
Adolescent
/
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Country/Region as subject:
America do norte
Language:
En
Journal:
Emerg Microbes Infect
/
Emerg. microbes & infect
/
Emerging microbes & infections
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: