Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

Abstract

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

Keywords: COVID-19; Complete blood count; early prediction of mortality risk; machine learning; prognostic model.

FIGURE 1.

Schematic diagram of the experimental frame work.

FIGURE 2.

Top-ranked-10 features using random forest feature selection technique.

FIGURE 3.

ROC curves for top-10 features using logistic regression classifier (Imputation-KNN, Feature selection–Random forest).

FIGURE 4.

Comparison of the top-ranked 8 features identified using random forest algorithm from data imputed using KNN algorithm A) without neutrophils and lymphocyte counts and B) without neutrophils and lymphocytes percentage.

FIGURE 5.

Confusion Matrix of the best performing combination of features using logistic regression classifier: A) without neutrophils and lymphocyte counts and B) without neutrophils and lymphocytes percentage.

FIGURE 6.

Calibration plot comparing predicted and actual death probability of patients with COVID-19: (A) represents the internal validation using Chinese test data, and (B) represents the external validation using Bangladeshi data.

FIGURE 7.

Decision curves analysis comparing different models to predict the death probability of patients with COVID-19. The net benefit balances the mortality risk and potential harm from unnecessary over-intervention for patients with COVID-19.

FIGURE 8.

Multivariate logistic regression-based Nomogram to predict the probability of death. A Nomogram for prediction of death was created using the following seven predictors: Neutrophils count, Age, Platelet count, Monocytes, WBC, Lymphocyte count, Red blood cell distribution width.

FIGURE 9.

Example of the developed nomogram helping early severity classification of mortality of a patient from the dataset collected at Dhaka Medical College.

FIGURE 10.

Mortality risk prediction Web-Application .