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

Abstract

Purpose

Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults.

Patients and Methods

Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a “non-re-positive” group and a “re-positive” group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model’s goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively.

Results

We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ² =5.916, P =0.657; the AUC was 0.881; when the threshold probability was >8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was >80%, the positive estimated value was closer to the actual number of cases.

Conclusion

This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies to further validate, refine, and update this model.

Keywords:

• malnutrition
• COVID-19
• re-positive
• clinical prediction model

Abbreviations

COVID-19, novel coronavirus;GNRI, Geriatric Nutritional Risk Index; LASSO, least absolute shrinkage and selection operator; AUC, area under the curve; DCA, decision curve analysis; CIC, clinical impact curve analysis; BMI, body mass index; WBC, white blood cell; CRP, c-reactive protein; Hb, hemoglobin; ALB, albumin; ALT, alanine aminotransferase, AST, aspartate aminotransferase; Scr, serum creatinine.

Data Sharing Statement

The dataset generated and analyzed during the current study is available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was conducted at the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine and has been approved by the Ethics Committee of our hospital in accordance with the STROBE criteria and the Declaration of Helsinki. As all included data were anonymized and retrospective, informed consent from patients was not required.

Acknowledgments

The authors wish to express gratitude to all respondents and their families for their enthusiastic participation in this study.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.