| Author | França, Allan R. M. | |
| Author | Rocha, Eduardo | |
| Author | Bastos, Leonardo S. L. | |
| Author | Bozza, Fernando A. | |
| Author | Kurtz, Pedro | |
| Author | Maccariello, Elizabeth | |
| Author | Silva, José Roberto Lapa e | |
| Author | Salluh, Jorge I. F. | |
| Access date | 2024-03-14T00:37:40Z | |
| Available date | 2024-03-14T00:37:40Z | |
| Document date | 2024 | |
| Citation | FRANÇA, Allan R. M. et al. Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19. Journal of Critical Care, v. 80, p. 1-9, Apr. 2024. | en_US |
| ISSN | 0883-9441 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/63035 | |
| Sponsorship | This study was supported by the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES) - Finance Code 001, Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) and the Pontifical Catholic University of Rio de Janeiro, and by departmental funds from the D’Or Institute for Research and Education. All authors carried out the research independently of the funding bodies. The findings and conclusions in this manuscript reflect the opinions of the authors alone. | en_US |
| Language | eng | en_US |
| Publisher | Elsevier | en_US |
| Rights | restricted access | |
| Title | Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19 | en_US |
| Type | Article | |
| DOI | 10.1016/j.jcrc.2023.154480 | |
| Abstract | Purpose: To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. Materials and methods: Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed. Results: Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]). Conclusions: An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients. | en_US |
| Affilliation | Federal University of Rio de Janeiro. Postgraduate Program of Internal Medicine. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal University of Rio de Janeiro. Postgraduate Program of Internal Medicine. Rio de Janeiro, RJ, Brazil / D'Or Institute for Research and Education. Postgraduate Program. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | D'Or Institute for Research and Education. Postgraduate Program. Rio de Janeiro, RJ, Brazil / Oswaldo Cruz Foundation. Evandro Chagas National Institute of Infectious Disease. Clinical Research Laboratory in Intensive Medicine. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | D'Or Institute for Research and Education. Postgraduate Program. Rio de Janeiro, RJ, Brazil / Hospital Copa Star. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | D'Or Institute for Research and Education. Postgraduate Program. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal University of Rio de Janeiro. Postgraduate Program of Internal Medicine. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Federal University of Rio de Janeiro. Postgraduate Program of Internal Medicine. Rio de Janeiro, RJ, Brazil / D'Or Institute for Research and Education. Postgraduate Program. Rio de Janeiro, RJ, Brazil. | en_US |
| Subject | COVID-19 | en_US |
| Subject | Respiratory Failure | en_US |
| Subject | Acute Kidney Injury | en_US |
| Subject | Outcomes | en_US |
| Subject | Machine Learning | en_US |
| Subject | Renal Replacement Therapy | en_US |
| e-ISSN | 1557-8615 | |
| Embargo date | 2030-12-31 | |
| xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |
