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Machine learning model for prediction of coronavirus disease 2019 within 6 months after three doses of BNT162b2 in
1Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China.
Insights
A machine learning model effectively identified individuals at low risk of COVID-19 after three BNT162b2 vaccine doses. The neural network model integrated clinical variables for accurate risk prediction in Hong Kong.
Area of Science:
- Medical Informatics
- Epidemiology
- Vaccinology
Background:
- The BNT162b2 vaccine is widely used for COVID-19 prevention.
- Predicting individual risk post-vaccination is crucial for public health strategies.
- Machine learning offers potential for personalized risk assessment.
Purpose of the Study:
- To develop and evaluate a machine learning model for predicting COVID-19 risk.
- To identify key clinical predictors of COVID-19 in three-dose BNT162b2 vaccine recipients.
- To assess the performance of various machine learning models in risk prediction.
Main Methods:
- A cohort of 304 three-dose BNT162b2 recipients in Hong Kong was studied.
- Demographic, comorbidity, medication, and laboratory data were collected.
- Six machine learning models, including a neural network, were trained and tested.
Main Results:
- The neural network model demonstrated the best performance with an AUC of 0.74.
- Key predictors included age, diabetes, controlled attenuation parameter (CAP), and liver function tests.
- The model achieved high sensitivity (90%) and negative predictive value (98%) for low-risk identification.
Conclusions:
- A neural network model integrating 15 clinical variables effectively identified low-risk individuals.
- This model can aid in stratifying COVID-19 risk among vaccinated populations.
- Clinical variables are valuable for predicting COVID-19 outcomes post-vaccination.
Introduction:
We aimed to develop a machine learning (ML) model to predict the risk of coronavirus disease 2019 (COVID-19) among three-dose BNT162b2 vaccine recipients in Hong Kong.
Methods:
A total of 304 individuals who had received three doses of BNT162b2 were recruited from three vaccination centres in Hong Kong between May and August 2021. The dataset was randomly divided into training (n=184) and testing (n=120) sets in a 6:4 ratio. Demographics, co-morbidities and medications, blood tests (complete blood count, liver and renal function tests, glycated haemoglobin level, lipid profile, and presence of hepatitis B surface antigen), and controlled attenuation parameter (CAP) were used to develop six ML models (logistic regression, linear discriminant analysis, random forest, naïve Bayes, neural network [NN], and extreme gradient boosting models) to predict COVID-19 risk. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV).
Results:
Among the study population (median age: 50.9 years [interquartile range=43.6-57.8]; men: 30.9% [n=94]), 27 participants (8.9%) developed COVID-19 within 6 months. Fifteen clinical variables were used to train the models. The NN model achieved the best performance, with an AUC of 0.74 (95% confidence interval [95% CI]=0.60-0.88). Using the optimal cut-off value based on the maximised Youden index, sensitivity, specificity, PPV, and NPV were 90% (95% CI=55%-100%), 58% (95% CI=48%-68%), 16% (95% CI=8%-29%), and 98% (95% CI=92%-100%), respectively. The top predictors in the NN model include age, prediabetes/diabetes, CAP, alanine aminotransferase level, and aspartate aminotransferase level.
Conclusion:
An NN model integrating 15 clinical variables effectively identified individuals at low risk of COVID-19 following three doses of BNT162b2.
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