Abstract
Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).Results: Among 66 documented 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 (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset.Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. 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.
Highlights
As of September 6, 2021, COVID-19 has caused more than 219 million infections worldwide and resulted in more than 4.55 million deaths
We propose a personalized machine-learning (ML) method for predicting mortality in COVID-19 patients based on routinely available laboratory and clinical data on the day of intensive care unit (ICU) admission
We retrospectively studied 797 adult patients with severe COVID19 infection confirmed through reverse transcription-polymerase chain reaction (RT-PCR)
Summary
As of September 6, 2021, COVID-19 has caused more than 219 million infections worldwide and resulted in more than 4.55 million deaths. Complications are more common among elderly patients and people with preexisting conditions, and the rate of intensive care unit (ICU) admission is substantially higher in these groups [1, 2]. ICU admissions rely on the critical care capacity of the health care system. Iran, which is the primary testbed for this study, was one of the first countries hit by COVID-19. The ICU admission rate involves about 32% of all hospitalizations, and the ICU mortality rate is about 39% [3]. With the potential of new waves of COVID-19 infections driven by more transmissible variants, ICU hospitalization numbers are expected to rise, leading to shortages of ICU beds and critical management equipment
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