Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

Abstract

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients–Decision Tree Modeling Approach

“…There is emerging literature evidence related to implementing machine learning approaches to understand the pattern and key contributors to COVID-19 infections and prognosis. This current study's findings that included one of the largest population-based cohorts and explored the predictors of specific outcomes such as hospitalization, ICU admission or death are consistent with literature ( 8 , 25 ).…”

“…Studies have reported that age, sex, smoking status, underlying health conditions, and clinical presentation including breathing difficulties, fever and cough are important risk factors associated with severe outcome(s) ( 3 – 8 ). Despite an increase in well-conducted prediction analysis with regression modeling, a direct translation of the research findings into patient care remains challenging.…”

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

“…Yang et al [ 59 ] proposed a new model for analyzing clinical characteristics and predicting death outcomes in severe COVID-19 patients. The authors developed a clinically useful and easily interpretable DT-based model to help clinicians rapidly identify COVID-19 patients with high mortality risks.…”

“… Identification and validation of prognostic factors in COVID-19 patients Demographic data including, clinical data including and outcome (28-day mortality) Medium Low Gulati et al [ 56 ] Linear SVC, Perceptron, Passive Aggressive, Logistic Regression, etc. Sentiment classification of discussion related to COVID-19 pandemic Tweets related to COVID-19 pandemic Medium Low Singh et al [ 57 ] Ensemble Support Vector Machine COVID-19 detection Lung tomography scan data High Low Wu et al [ 58 ] Joint Classification and Segmentation COVID-19 diagnosis Chest CT images Medium Medium Yang et al [ 59 ] Decision Tree Death outcome prediction Medical records (demographics, clinical characteristics, and laboratory test results) Medium High Lella and Pja [ 60 ] Deep Convolutional Neural Network Diagnosis of COVID-19 disease Human respiratory sounds such as voice, dry cough, and breath, High Low Qayyum et al [ 61 ] Depth-wise deep learning Detection and diagnosis of COVID-19 infection Lungs X-rays images High Low Roy et al [ 62 ] Spatial Transformer Networks-based Deep learning Classification and Localization of COVID-19 Markers Lung ultrasonography (LUS) images. …”

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree

“…LDH is considered a general marker of cell or tissue injury and reflects the severity of inflammation. Elevated LDH levels are a predictor of severe COVID-19 ( 29 – 31 ). Fibrinogen has also been shown to be higher in patients with severe disease than in those without severe disease ( 32 ) and to predict poor prognosis in patients with COVID-19 ( 33 ).…”