Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

Abstract

Background The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. Objectives To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients’ data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Results Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Conclusion Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

“…In Table 1 we show the type of model validation that each study used to split data into train and test groups, indicating the number of subjects and the corresponding number of survived and non-survived subjects. Internal validation was performed in 15/24 studies [ 24 , 25 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 42 ].…”

“…A total of 19/24 studies adopted binary features [ 20 , 21 , 22 , 24 , 25 , 26 , 27 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 ]. 1/24 study dichotomized continuous feature’s value in a binary form [ 28 ].…”

“…A total of 16/24 studies adopted continuous features [ 21 , 22 , 24 , 27 , 29 , 30 , 31 , 32 , 33 , 35 , 37 , 38 , 40 , 41 , 42 , 43 ]. A total of 2/24 studies dichotomized binary feature in continuous feature associating a Charlson comorbidity score to the feature’s value [ 39 , 40 ].…”

“…Most studies used a high number of starting features [24,27,[29][30][31][32][33]35,[37][38][39]. We found 8/24 articles in which SHAP method was used to optimize survival prediction in COVID [22,[24][25][26]29,32,[39][40][41]44].…”

“…A total of 19/24 studies adopted binary features [20][21][22][24][25][26][27][30][31][32][33][34][35][36][37][38][40][41][42][43]]. 1/24 study dichotomized continuous feature's value in a binary form [28].…”