Development of a diagnostic multivariable prediction model of a positive SARS-CoV-2 RT-PCR result in healthcare workers with suspected SARS-CoV-2 infection in hospital settings

Abstract

Background: Despite declining COVID-19 incidence, healthcare workers (HCWs) still face an elevated risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. We developed a diagnostic multivariate model to predict positive reverse transcription polymerase chain reaction (RT-PCR) results in HCWs with suspected SARS-CoV-2 infection.

Methods: We conducted a cross-sectional study on episodes involving suspected SARS-CoV-2 symptoms or close contact among HCWs in Bogotá, Colombia. Potential predictors were chosen based on clinical relevance, expert knowledge, and literature review. Logistic regression was used, and the best model was selected by evaluating model fit with Akaike Information Criterion (AIC), deviance, and maximum likelihood.

Results: The study included 2498 episodes occurring between March 6, 2020, to February 2, 2022. The selected variables were age, socioeconomic status, occupation, service, symptoms (fever, cough, fatigue/weakness, diarrhea, anosmia or dysgeusia), asthma, history of SARS-CoV-2, vaccination status, and population-level RT-PCR positivity. The model achieved an AUC of 0.79 (95% CI 0.77-0.81), with 93% specificity, 36% sensitivity, and satisfactory calibration.

Conclusions: We present an innovative diagnostic prediction model that as a special feature includes a variable that represents SARS-CoV-2 epidemiological situation. Given its performance, we suggest using the model differently based on the level of viral circulation in the population. In low SARS-CoV-2 circulation periods, the model could serve as a replacement diagnostic test to classify HCWs as infected or not, potentially reducing the need for RT-PCR. Conversely, in high viral circulation periods, the model could be used as a triage test due to its high specificity. If the model predicts a high probability of a positive RT-PCR result, the HCW may be considered infected, and no further testing is performed. If the model indicates a low probability, the HCW should undergo a COVID-19 test. In resource-limited settings, this model can help prioritize testing and reduce expenses.

Fig 1. Episodes of suspected SARS-CoV-2 infection among HCWs of Hospital Universitario San Ignacio from March 6, 2020 to February 2, 2022, selected for the study according to eligibility criteria.

Fig 2. “Diagnostic performance of the selected model for the prediction of a positive RT-PCR result for SARS-CoV-2 in healthcare workers with suspected infection in a hospital setting.

A. Receiver Operating Characteristic (ROC) curve. B. Calibration graph of the model”.

Fig 3. Diagnostic performance of the selected model for the prediction of a positive RT-PCR result for SARS-CoV-2 in healthcare workers with suspected infection in a hospital setting under conditions of low (RT-PCR positivity in the city below 15%) and high circulation (RT-PCR positivity in the city 15% or higher) of the virus.

A1. Receiver Operating Characteristic (ROC) curve and A2. Calibration graph for the prediction model during periods when the population RT-PCR positivity is below 15% (low positivity). B1. Receiver Operating Characteristic (ROC) curve and B2. Calibration graph for the prediction model during periods when the population RT-PCR positivity is 15% or higher (high positivity).