Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

Abstract

Background Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyses were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Emergency department characteristics and associations with intensive care admission among patients with coronavirus disease 2019

“…We report the characteristics and associated outcomes of patients seen in the EDs in our hospital system during the initial phase of the COVID-19 pandemic in Seattle. 8,12,16,17,[32][33][34][35] We found that vital signs were strong predictors of ICU admission, We report a higher proportion of patients who were admitted to the ICU than prior cohorts. 5,7,9,11 Given that our mortality rate was similar to prior cohorts, this is likely a reflection of our hospital system during the time these data were gathered.…”

A Novel Machine Learning Based Screening Method For High-Risk Covid-19 Patients Based On Simple Blood Exams

“…In another study, a regression analysis showed that Creactive protein (CRP) was significantly associated with aggravation of non-severe COVID-19 patients, with an area under the curve of 0.844 (95% confidence interval, 0.761-0.926) and an optimal threshold value of 26.9 mg/L [16]. In a Spanish study, COVID-19 patients with normal levels of lymphocytes or mild lymphopenia, imbalanced lymphocyte subpopulations were early markers of in-hospital mortality [26].…”