Using Machine Learning to Predict mortality for first-day COVID-19 patients presenting to the ICU

Abstract

<b>Rationale:</b> There is an urgent need for early prediction of the severity of COVID-19 in ICU patients to optimize treatment strategies. <b>Objectives:</b> Early prediction of mortality using machine learning based on typical laboratory results and clinical data on the day of ICU admission. <b>Methods:</b> We studied retrospectively 263 COVID-19 ICU patients. Logistic regression and random forest (RF) algorithms were utilized to build classification models. The impact of each marker on the RF predictions was studied by the local interpretable model-agnostic explanation technique (LIME-SP). <b>Results:</b> Among 66 parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume, white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patients’ outcomes with a sensitivity of 70% and a specificity of 75%. <b>Conclusions:</b> The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.