Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation

Area of Science:

• Geriatric Medicine
• Infectious Diseases
• Clinical Prediction Modeling

Background:

• Older adults, particularly those who are malnourished, face increased risks associated with COVID-19 (novel coronavirus) infection.
• Identifying individuals susceptible to re-infection is crucial for timely and effective clinical management.

Purpose of the Study:

• To develop and validate a clinical prediction model for COVID-19 re-positive cases specifically in malnourished older adults.
• To identify key factors influencing COVID-19 re-positivity in this demographic.

Main Methods:

• Retrospective collection of data from 347 malnourished older adults (January-May 2023).
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to identify predictive factors.
• Model performance assessed using Hosmer-Lemeshow test, Area Under the Curve (AUC), calibration curves, and decision curve analysis (DCA).

Main Results:

• A clinical prediction model was constructed, identifying significant risk and protective factors for COVID-19 re-positivity.
• The model demonstrated strong discrimination with an AUC of 0.881 and good calibration (Hosmer-Lemeshow test, P = 0.657).
• Decision curve analysis indicated clinical utility for predicting re-positivity at threshold probabilities >8%.

Conclusions:

• The developed model effectively identifies malnourished older adults at risk for COVID-19 re-positivity, enabling early intervention.
• This tool can guide clinical decision-making and potentially improve patient outcomes.
• Further validation through large-scale, multicenter studies is recommended to refine and update the model.