Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

Abstract

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis

“…A total of 20 studies were included in the qualitative and quantitative synthesis ( Fig. 1 , Table 1 ) [ 2 , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] ]. One study which we included described an OR value.…”

High admission blood glucose independently predicts poor prognosis in COVID-19 patients: A systematic review and dose-response meta-analysis

“…Table S4). In quantitative analysis, 10 studies were excluded as seven [21][22][23][24][25][26][27] only reported Pvalue and three [28][29][30] reported different effect measures.…”

Applications of artificial intelligence in battling against covid-19: A literature review

“…Some research have tried to study the use of AI approaches in identifying covid-19 cases via these data. Using the clinical and laboratory features obtained at admission, a machine learning algorithm is proposed in [113] which predicts if patients require mechanical ventilation or will die or survive when hospitalized. In order to evaluate the early risk assessment for patients, in [114] demographic data, physiological clinical variables and laboratory results from electronic healthcare records are extracted and used with applied multivariate logistic regression, random forest and extreme gradient boosted trees.…”