By Nidhi Saha, BDSJul 7 2022Reviewed by Danielle Ellis, B.Sc. In a recent article posted on medRxiv*, longitudinal clinical phenotypes were described based on respiratory ordinal scales. According to the study, demographics, clinical features, laboratory tests, and radiographic observations correlated with coronavirus disease 2019 trajectory.
To address these shortcomings, an effective method was developed that considered the entire disease course and the patient's problems. Longitudinal data integration is an effective method to identify disease severity considering the entirety of the disease course in terms of patient problems, the persistence of symptoms, and resource use.
The study IMPACC was a prospective observational cohort study conducted on 1,164 patients from 20 hospitals across the United States. Based on the severity of respiratory sickness, a seven-point ordinal scale was used to evaluate the disease severity. This study examined patient characteristics using unsupervised clustering of the respiratory ordinal score over time to capture the dynamics of disease progression.
Further, the patients were prospectively questioned quarterly for one year following discharge for PASC. Over 28 days, demographics, comorbidities, radiographic observations, clinical laboratory values, SARS-CoV-2 polymerase chain reaction , and serology were collected. A multivariable logistic regression analysis was conducted.
Hispanic/Latinx ethnicity was related to an elevated risk of more severe disease; however, neither race nor ethnicity was ultimately connected to mortality when analyzing the multivariable risk in the most severely ill groups. Conclusion High baseline viral load and its persistence were linked with COVID-19 disease severity, according to the present investigation. Whereas fatal SARS-CoV-2 cases were related to the lowest anti-RBD and S immunoglobulin G concentrations.
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