A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.
【저자키워드】 Risk factors, Infectious diseases, machine learning, Predictive medicine, 【초록키워드】 COVID-19, coronavirus disease, Comorbidity, risk, D-dimer, lactate dehydrogenase, Lymphocyte count, Cohort, Deterioration, International, Patient, Platelet, Urine, BMI, patients, predict, Combination, AUC, high risk, Prothrombin time, Predictive, Factor, 95% CI, website, HCT, selected, resulting, activated, faster, exhibiting, body-mass, common clinical symptom, diagnosed with COVID-19, normalized, 【제목키워드】 COVID-19, risk, Deterioration,