Background Risk scores are needed to predict the risk of death in severe coronavirus disease 2019 (COVID-19) patients in the context of rapid disease progression. Methods Using data from China (training dataset, n = 96), prediction models were developed by logistic regression and then risk scores were established. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) was used for external validation. Results A NSL model (area under the curve (AUC) 0.932) and a NL model (AUC 0.903) were developed based on neutrophil percentage and lactate dehydrogenase with and without oxygen saturation (SaO 2 ) using the training dataset. AUCs of the NSL and NL models in the test dataset were 0.910 and 0.871, respectively. The risk scoring systems corresponding to these two models were established. The AUCs of the NSL and NL scores in the training dataset were 0.928 and 0.901, respectively. At the optimal cut-off value of NSL score, the sensitivity and specificity were 94% and 82%, respectively. The sensitivity and specificity of NL score were 94% and 75%, respectively. Conclusions These scores may be used to predict the risk of death in severe COVID-19 patients and the NL score could be used in regions where patients’ SaO 2 cannot be tested. Supplementary Information The online version contains supplementary material available at 10.1186/s12985-021-01538-8.
【저자키워드】 SARS-CoV-2, severe COVID-19, prediction, Hospital mortality, 【초록키워드】 COVID-19, neutrophil, risk, lactate dehydrogenase, China, Disease progression, oxygen saturation, Iran, Region, Sensitivity and specificity, Patient, Logistic regression, cross, predict, external validation, AUC, risk score, scoring system, risk of death, supplementary material, severe coronavirus disease, test dataset, cut-off value, training dataset, Result, tested, was used, severe COVID-19 patient, 【제목키워드】 hospital, neutrophil, lactate dehydrogenase, oxygen saturation, predict, score, mortality risk, severe COVID-19 patient,