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.
<h4>Background</h4>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 chai...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316207 |
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| author | Sandra Liliana Valderrama-Beltrán Juliana Cuervo-Rojas Martín Rondón Juan Sebastián Montealegre-Diaz Juan David Vera Samuel Martinez-Vernaza Alejandra Bonilla Camilo Molineros Viviana Fierro Atilio Moreno Leidy Villalobos Beatriz Ariza Carlos Álvarez-Moreno |
| author_facet | Sandra Liliana Valderrama-Beltrán Juliana Cuervo-Rojas Martín Rondón Juan Sebastián Montealegre-Diaz Juan David Vera Samuel Martinez-Vernaza Alejandra Bonilla Camilo Molineros Viviana Fierro Atilio Moreno Leidy Villalobos Beatriz Ariza Carlos Álvarez-Moreno |
| author_sort | Sandra Liliana Valderrama-Beltrán |
| collection | DOAJ |
| description | <h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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. |
| format | Article |
| id | doaj-art-331ac17bdfd94f83a7c1b3c1bba5c96e |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-331ac17bdfd94f83a7c1b3c1bba5c96e2025-01-07T05:32:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031620710.1371/journal.pone.0316207Development 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.Sandra Liliana Valderrama-BeltránJuliana Cuervo-RojasMartín RondónJuan Sebastián Montealegre-DiazJuan David VeraSamuel Martinez-VernazaAlejandra BonillaCamilo MolinerosViviana FierroAtilio MorenoLeidy VillalobosBeatriz ArizaCarlos Álvarez-Moreno<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.https://doi.org/10.1371/journal.pone.0316207 |
| spellingShingle | Sandra Liliana Valderrama-Beltrán Juliana Cuervo-Rojas Martín Rondón Juan Sebastián Montealegre-Diaz Juan David Vera Samuel Martinez-Vernaza Alejandra Bonilla Camilo Molineros Viviana Fierro Atilio Moreno Leidy Villalobos Beatriz Ariza Carlos Álvarez-Moreno 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. PLoS ONE |
| title | 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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 |
| url | https://doi.org/10.1371/journal.pone.0316207 |
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