{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:28:52Z","timestamp":1718238532274},"reference-count":62,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2013Brasil (CAPES)\u2013Finance Code 001"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n               <jats:title>Background<\/jats:title>\n               <jats:p>Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Methods<\/jats:title>\n               <jats:p>In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model\u2019s performance for the selected variables.<\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title>Results<\/jats:title>\n               <jats:p>Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.<\/jats:p>\n            <\/jats:sec>","DOI":"10.7717\/peerj.17428","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T07:53:35Z","timestamp":1718178815000},"page":"e17428","source":"Crossref","is-referenced-by-count":0,"title":["Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study"],"prefix":"10.7717","volume":"12","author":[{"given":"Lander","family":"Dos Santos","sequence":"first","affiliation":[{"name":"State University of Maring\u00e1, Graduate Program in Health Sciences, Maring\u00e1, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lincoln Luis","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Duke University School of Medicine, Durham, NC, United States of 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Dalva","family":"Carvalho","sequence":"additional","affiliation":[{"name":"State University of Maring\u00e1, Graduate Program in Health Sciences, Maring\u00e1, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ra\u00edssa Bocchi","family":"Pedroso","sequence":"additional","affiliation":[{"name":"State University of Maring\u00e1, Graduate Program in Health Sciences, Maring\u00e1, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra Marisa","family":"Pelloso","sequence":"additional","affiliation":[{"name":"State University of Maring\u00e1, Graduate Program in Health Sciences, Maring\u00e1, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4443","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"10.7717\/peerj.17428\/ref-1","doi-asserted-by":"publisher","first-page":"100908","DOI":"10.1016\/j.imu.2022.100908","article-title":"Predicting hospital readmission risk 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