A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection.
ADVANCES IN LABORATORY MEDICINE 2021;
2:390-408. [PMID:
37362407 PMCID:
PMC10197269 DOI:
10.1515/almed-2021-0039]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/16/2021] [Indexed: 06/28/2023]
Abstract
Objectives
The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity.
Methods
A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort.
Results
The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85-0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71-0.89), with a rate of correct classifications of 73%.
Conclusions
The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.
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