Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning
Babak Mostafazadeh1, Sayed Masoud Hosseini1, Shahin Shadnia1
1Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Insights
Machine learning models accurately predict outcomes for calcium channel blocker (CCB) poisoning. XGBoost and CatBoost showed superior performance, aiding early risk stratification for CCB poisoning patients.
Area of Science:
- Toxicology
- Medical Informatics
- Cardiovascular Medicine
Background:
- Calcium channel blocker (CCB) poisoning presents a significant toxicological emergency with severe cardiovascular complications.
- Accurate prediction of CCB poisoning outcomes is crucial for timely and effective patient management.
Purpose of the Study:
- To evaluate the accuracy of various machine learning (ML) models in predicting outcomes of CCB poisoning.
- To identify key prognostic factors associated with CCB poisoning using ML techniques.
Main Methods:
- A retrospective cross-sectional study of 274 CCB poisoning cases (2019-2024).
- Trained ML models (XGBoost, CatBoost, Random Forest, AdaBoost) on clinical and laboratory data.
- Utilized feature selection to identify 18 prognostic factors and assessed model performance using AUC, accuracy, precision, recall, and F1-score.
Main Results:
- Feature selection identified 18 key prognostic factors, including temperature, GCS-eye response, ECG findings, and various lab values.
- XGBoost and CatBoost achieved the highest predictive performance with macro-averaged AUC values of 0.9899 and 0.9983, respectively.
- These ML models outperformed traditional statistical methods in risk stratification for CCB poisoning.
Conclusions:
- ML models, particularly XGBoost and CatBoost, demonstrate high accuracy in predicting CCB poisoning outcomes.
- These models offer a valuable data-driven framework for early risk stratification in clinical settings.
- Future research should focus on multi-center validation and integration into clinical decision support systems.
Introduction:
Calcium channel blocker (CCB) poisoning is a critical toxicological emergency that can result in severe complications, particularly cardiovascular effects. This study aimed to evaluate the accuracy of Machine learning (ML) models in predicting the outcomes of CCB poisoning.
Methods:
This retrospective cross-sectional study analyzed the medical records of patients diagnosed with CCB poisoning at Loghman Hakim Hospital between 2019 and 2024. The accuracy of machine learning (ML) models in predicting the outcomes of CCB poisoning and identifying its predictive factors was evaluated. Various ML models, including XGBoost, CatBoost, Random Forest, and AdaBoost, were trained on clinical and laboratory data. Then, feature selection was performed to identify the most relevant variables. The hold-out set was randomly selected to avoid selection bias. Model performance was assessed using accuracy, precision, recall, F1-score, and macro-averaged area under the receiver operating characteristic (ROC) curve (AUC).
Results:
274 CCB poisoning cases with the mean age of 31.99± 17.47 (range: 1.5 to 89) years were evaluated (70.4% female). Feature selection identified 18 key prognostic factors, including body temperature, whole bowel irrigation, need for cardiology consultation, arterial oxygen saturation, Glasgow coma scale (GCS)-eye response, electrocardiography (ECG) findings, serum level of alkaline phosphatase (ALP), pH-venous blood gas (VBG), HCO3-VBG, serum level of lactate dehydrogenase (LDH), blood sugar, pulse rate, fraction of inspired oxygen (FiO2), time elapsed from ingestion to admission, troponin, serum level of alanine aminotransferase (ALT), serum level of creatinine, and serum level of potassium. Among the ML models, XGBoost and CatBoost demonstrated the highest predictive performance, with macro-averaged AUC values of 0.9899 (95%confidence interval (CI): 0.98-0.99) and 0.9983 (95%CI: 0.997-0.999), respectively. These models outperformed traditional statistical approaches, providing enhanced risk stratification for patients with CCB poisoning.
Conclusion:
This study highlights the potential of ML-based models for predicting outcomes in CCB poisoning, offering a data-driven framework for early risk stratification. The superior performance of XGBoost and CatBoost suggests their clinical applicability. Future research should focus on external validation in multi-center settings and real-time integration into clinical decision-making systems.
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