ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized
COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis. Objective: To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population. Methodology: Observational study with retrospecti...
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Elsevier
2025-06-01
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| Series: | Global Epidemiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590113324000476 |
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| author | Vinicius Lins Costa Ok Melo Pedro Emmanuel Alvarenga Americano do Brasil, PhD |
| author_facet | Vinicius Lins Costa Ok Melo Pedro Emmanuel Alvarenga Americano do Brasil, PhD |
| author_sort | Vinicius Lins Costa Ok Melo |
| collection | DOAJ |
| description | COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis. Objective: To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population. Methodology: Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression. Results: Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were − 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289. Conclusion: The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (https://pedrobrasil.shinyapps.io/INDWELL/). The clinical benefit and the performance in different scenarios are yet to be known. |
| format | Article |
| id | doaj-art-f0aabef3b599424593d3a7c4f5cbae9e |
| institution | Kabale University |
| issn | 2590-1133 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Epidemiology |
| spelling | doaj-art-f0aabef3b599424593d3a7c4f5cbae9e2025-01-03T04:08:55ZengElsevierGlobal Epidemiology2590-11332025-06-019100181ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalizedVinicius Lins Costa Ok Melo0Pedro Emmanuel Alvarenga Americano do Brasil, PhD1Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, BrazilCorresponding author at: Av. Brasil, 4365 - Manguinhos, Rio de Janeiro CEP: 21040-360, Brazil.; Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, BrazilCOVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis. Objective: To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population. Methodology: Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression. Results: Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were − 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289. Conclusion: The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (https://pedrobrasil.shinyapps.io/INDWELL/). The clinical benefit and the performance in different scenarios are yet to be known.http://www.sciencedirect.com/science/article/pii/S2590113324000476COVID-19PrognosisPrediction modelsCritical statusMortality |
| spellingShingle | Vinicius Lins Costa Ok Melo Pedro Emmanuel Alvarenga Americano do Brasil, PhD ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized Global Epidemiology COVID-19 Prognosis Prediction models Critical status Mortality |
| title | ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized |
| title_full | ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized |
| title_fullStr | ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized |
| title_full_unstemmed | ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized |
| title_short | ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized |
| title_sort | accredit validation of clinical score for progression of covid 19 while hospitalized |
| topic | COVID-19 Prognosis Prediction models Critical status Mortality |
| url | http://www.sciencedirect.com/science/article/pii/S2590113324000476 |
| work_keys_str_mv | AT viniciuslinscostaokmelo accreditvalidationofclinicalscoreforprogressionofcovid19whilehospitalized AT pedroemmanuelalvarengaamericanodobrasilphd accreditvalidationofclinicalscoreforprogressionofcovid19whilehospitalized |