A novel survival algorithm in COVID-19 intensive care patients: the classification and regression tree (CRT) method

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Dagistanli, Sevinc
Sonmez, Suleyman
Unsel, Murat
Bozdag, Emre
Kocatas, Ali
BOŞAT, Merve
Caliskan, Zeynep
GÜNVER, Mehmet Güven
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Background/aim: The present study aimed to create a decision tree for the identification of clinical, laboratory and radiological data of individuals with COVID-19 diagnosis or suspicion of Covid-19 in the Intensive Care Units of a Training and Research Hospital of the Ministry of Health on the European side of the city of Istanbul. Materials and methods: The present study, which had a retrospective and sectional design, covered all the 97 patients treated with Covid-19 diagnosis or suspicion of COVID-19 in the intensive care unit between 12 March and 30 April 2020. In all cases who had symptoms admitted to the COVID-19 clinic, nasal swab samples were taken and thoracic CT was performed when considered necessary by the physician, radiological findings were interpreted, clinical and laboratory data were included to create the decision tree. Results: A total of 61 (21 women, 40 men) of the cases included in the study died, and 36 were discharged with a cure from the intensive care process. By using the decision tree algorithm created in this study, dead cases will be predicted at a rate of 95%, and those who survive will be predicted at a rate of 81%. The overall accuracy rate of the model was found at 90%. Conclusions: There were no differences in terms of gender between dead and live patients. Those who died were older, had lower MON, MPV, and had higher D-Dimer values than those who survived.
COVID-19 intensive care patients, Survival algorithm, CRT method
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