Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial
BackgroundPredicting clinical improvement after hospital admission in patients with COVID-19 is crucial for effective resource allocation. Machine-learning tools can help identify patients likely to show clinical improvement based on real-world data. This study used two approaches—least absolute shr...
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Frontiers Media S.A.
2025-05-01
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| author | Caio César Souza Conceição Camila Marinelli Martins Mayck Medeiros Silva Hugo Caire de Castro Faria Neto Davide Chiumello Davide Chiumello Davide Chiumello Patricia Rieken Macedo Rocco Fernanda Ferreira Cruz Pedro Leme Silva |
| author_facet | Caio César Souza Conceição Camila Marinelli Martins Mayck Medeiros Silva Hugo Caire de Castro Faria Neto Davide Chiumello Davide Chiumello Davide Chiumello Patricia Rieken Macedo Rocco Fernanda Ferreira Cruz Pedro Leme Silva |
| author_sort | Caio César Souza Conceição |
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| description | BackgroundPredicting clinical improvement after hospital admission in patients with COVID-19 is crucial for effective resource allocation. Machine-learning tools can help identify patients likely to show clinical improvement based on real-world data. This study used two approaches—least absolute shrinkage and selection operator (LASSO) and CombiROC—to identify predictive variables at hospital admission for detecting clinical improvement after 7 days.MethodsA secondary analysis was conducted on the modified intention-to-treat placebo group from a previous randomized clinical trial (RCT, NCT04561219) of patients with COVID-19. The analysis assessed clinical, laboratory, and blood markers at admission to predict clinical improvement, defined as a two-point increase on the World Health Organization clinical progression scale after 7 days. LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. AUCs were compared using DeLong’s algorithm.ResultsOverall, 203 patients were included in the analysis, and they were divided into two groups; clinical improvement (n = 154) and no clinical improvement (n = 49). The median age was 55 years (interquartile range, 46–66 years); 65% were male. LASSO identified three predictive variables (SaO2, hematocrit, and interleukin [IL]-13) with high sensitivity of 98% (95% confidence interval [CI], 92–100%) but low specificity of 26% (95% CI, 10–48%) for clinical improvement. CombiROC selected a broader set of variables (T cell–attracting chemokine, hemoglobin, hepatocyte growth factor, hematocrit, IL-3, PDGF-BB, RANTES, and SaO2), achieving balanced sensitivity of 82% (95% CI, 69–91%) and specificity of 74% (95% CI, 49–91%). LASSO and CombiROC showed comparable accuracy (82 and 80%, respectively) and similar area under the ROC curves (LASSO: AUC, 0.704; 95% CI, 0.571–0.837; CombiROC: AUC, 0.823; 95% CI, 0.708–0.937; p = 0.185).ConclusionFor patients hospitalized with COVID-19 pneumonia, predictive variables identified by LASSO and CombiROC analyses demonstrated similar accuracy and AUCs in predicting clinical improvement. LASSO, with fewer variables (SaO2, hematocrit, and IL-13) showed high sensitivity but low specificity, whereas CombiROC’s broader selection of variables provided balanced sensitivity and specificity for predicting clinical improvement.Clinical trial registrationBrazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219. |
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| spelling | doaj-art-2fb0fcd8faea421791f6b95a16642d092025-08-20T03:51:58ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15619801561980Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trialCaio César Souza Conceição0Camila Marinelli Martins1Mayck Medeiros Silva2Hugo Caire de Castro Faria Neto3Davide Chiumello4Davide Chiumello5Davide Chiumello6Patricia Rieken Macedo Rocco7Fernanda Ferreira Cruz8Pedro Leme Silva9Laboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, BrazilAAC&T Research Consulting LTDA, Curitiba, BrazilLaboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, BrazilLaboratory of Immunopharmacology, Oswaldo Cruz Institute (Fiocruz), Rio de Janeiro, BrazilDepartment of Health Sciences, University of Milan, Milan, ItalyAnaesthesia and Intensive Care, San Paolo University Hospital, Milan, ItalyCoordinated Research Center on Respiratory Failure, University of Milan, Milan, ItalyLaboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, BrazilLaboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, BrazilLaboratory of Pulmonary Investigation, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, BrazilBackgroundPredicting clinical improvement after hospital admission in patients with COVID-19 is crucial for effective resource allocation. Machine-learning tools can help identify patients likely to show clinical improvement based on real-world data. This study used two approaches—least absolute shrinkage and selection operator (LASSO) and CombiROC—to identify predictive variables at hospital admission for detecting clinical improvement after 7 days.MethodsA secondary analysis was conducted on the modified intention-to-treat placebo group from a previous randomized clinical trial (RCT, NCT04561219) of patients with COVID-19. The analysis assessed clinical, laboratory, and blood markers at admission to predict clinical improvement, defined as a two-point increase on the World Health Organization clinical progression scale after 7 days. LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. AUCs were compared using DeLong’s algorithm.ResultsOverall, 203 patients were included in the analysis, and they were divided into two groups; clinical improvement (n = 154) and no clinical improvement (n = 49). The median age was 55 years (interquartile range, 46–66 years); 65% were male. LASSO identified three predictive variables (SaO2, hematocrit, and interleukin [IL]-13) with high sensitivity of 98% (95% confidence interval [CI], 92–100%) but low specificity of 26% (95% CI, 10–48%) for clinical improvement. CombiROC selected a broader set of variables (T cell–attracting chemokine, hemoglobin, hepatocyte growth factor, hematocrit, IL-3, PDGF-BB, RANTES, and SaO2), achieving balanced sensitivity of 82% (95% CI, 69–91%) and specificity of 74% (95% CI, 49–91%). LASSO and CombiROC showed comparable accuracy (82 and 80%, respectively) and similar area under the ROC curves (LASSO: AUC, 0.704; 95% CI, 0.571–0.837; CombiROC: AUC, 0.823; 95% CI, 0.708–0.937; p = 0.185).ConclusionFor patients hospitalized with COVID-19 pneumonia, predictive variables identified by LASSO and CombiROC analyses demonstrated similar accuracy and AUCs in predicting clinical improvement. LASSO, with fewer variables (SaO2, hematocrit, and IL-13) showed high sensitivity but low specificity, whereas CombiROC’s broader selection of variables provided balanced sensitivity and specificity for predicting clinical improvement.Clinical trial registrationBrazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219.https://www.frontiersin.org/articles/10.3389/fmed.2025.1561980/fullCOVID-19biomarkersmachine learningLASSOCombiROCclinical improvement |
| spellingShingle | Caio César Souza Conceição Camila Marinelli Martins Mayck Medeiros Silva Hugo Caire de Castro Faria Neto Davide Chiumello Davide Chiumello Davide Chiumello Patricia Rieken Macedo Rocco Fernanda Ferreira Cruz Pedro Leme Silva Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial Frontiers in Medicine COVID-19 biomarkers machine learning LASSO CombiROC clinical improvement |
| title | Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial |
| title_full | Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial |
| title_fullStr | Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial |
| title_full_unstemmed | Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial |
| title_short | Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial |
| title_sort | predicting clinical outcomes at hospital admission of patients with covid 19 pneumonia using artificial intelligence a secondary analysis of a randomized clinical trial |
| topic | COVID-19 biomarkers machine learning LASSO CombiROC clinical improvement |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1561980/full |
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