BACKGROUND The novel coronavirus disease 2019(COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit(ICU) admission among COVID-19 patients at hospital admission.METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest(RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models.RESULTS There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort(median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar(49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods(0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

Supported by Shenzhen Municipal Government’s"Peacock Plan"; No. KQTD2016053112051497;

COVID-19; Intensive care units; Machine learning; Prognostic predictive model; Risk stratification;


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