Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning
Archives of Academic Emergency Medicine,
Vol. 13 No. 1 (2025),
6 Shahrivar 2025
,
Page e79
https://doi.org/10.22037/aaem.v13i1.2804
Abstract
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.
- calcium channel blockers
- poisoning
- prognosis
- machine learning
How to Cite
References
1. Alshaya OA, Alhamed A, Althewaibi S, Fetyani L, Alshehri S, Alnashmi F, et al. Calcium Channel Blocker Toxicity: A Practical Approach. J Multidiscip Healthc. 2022;15:1851-62.
2. McKeever RG, Patel P, Hamilton RJ. Calcium Channel Blockers. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC.; 2024.
3. Chakraborty RK, Hamilton RJ. Calcium Channel Blocker Toxicity. In: [Internet] IS, editor. Calcium Channel Blocker Toxicity. Treasure Island (FL): StatPearls Publishing; 2024.
4. Gummin DD, Mowry JB, Beuhler MC, Spyker DA, Rivers LJ, Feldman R, et al. 2022 Annual Report of the National Poison Data System(®) (NPDS) from America's Poison Centers(®): 40th Annual Report. Clin Toxicol (Phila). 2023;61(10):717-939.
5. Zeinvand M, Hoseini T, Rahmani A. A Clinico-Epidemiological Study on Poisonings due to Cardiovascular Drugs in Ahvaz, Iran. Asia Pac J Med Toxicol. 2017;6(1):25-8.
6. Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc. 2023;16:1779-91.
7. Mirzakhani F, Sadoughi F, Hatami M, Amirabadizadeh A. Which model is superior in predicting ICU survival: artificial intelligence versus conventional approaches. BMC Med Inform Decis Mak. 2022;22(1):167.
8. Hosseini SM, Rahimi M, Afrash MR, Ziaeefar P, Yousefzadeh P, Pashapour S, et al. Prediction of acute organophosphate poisoning severity using machine learning techniques. Toxicology. 2023;486:153431.
9. Mostafazadeh B, Shadnia S, Hosseini SM, Rahimi M, Talaie H, Mohtarami SA, et al. Prediction of Pralidoxime Dose in Patients with Organophosphate Poisoning Using Machine Learning Techniques. Health Scope. 2024;13(2):e143897.
10. Mehrpour O, Hoyte C, Al Masud A, Biswas A, Schimmel J, Nakhaee S, et al. Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opin Drug Metab Toxicol. 2023;19(6):367-80.
11. Mehrpour O, Hoyte C, Goss F, Shirazi FM, Nakhaee S. Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases. Drug Chem Toxicol. 2023;46(4):692-8.
12. Mehrpour O, Saeedi F, Abdollahi J, Amirabadizadeh A, Goss F. The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States. J Res Med Sci. 2023;28:49.
13. Mehrpour O, Saeedi F, Vohra V, Hoyte C. Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS). Drug Chem Toxicol. 2024;47(5):556-63.
14. Contributors G. XGBoost Interview Questions 2024 [Available from: https://github.com/Devinterview-io/xgboost-interview-questions.
15. Chen T, Guestrin C, editors. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining; 2016.
16. Paik ES, Lee JW, Park JY, Kim JH, Kim M, Kim TJ, et al. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. J Gynecol Oncol. 2019;30(4):e65.
17. Kang MW, Kim J, Kim DK, Oh KH, Joo KW, Kim YS, et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit Care. 2020;24(1):42.
18. Chen PW, Baune NA, Zwir I, Wang J, Swamidass V, Wong AWK. Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study. Int J Environ Res Public Health. 2021;18(4):1634.
19. Mozafari N, Talaie H, Shoaei SD, Hashemian M, Mahdavinejad A. Survey on Hypothermia and Hyperthermia in Poisoned Patients in a Unique Referral Hospital, Tehran, Iran. Iran Red Crescent Med J. 2016;18(4):e35483.
20. Gordon CJ. Toxic-induced hypothermia and hypometabolism: Do they increase uncertainty in the extrapolation of toxicological data from experimental animals to humans? Neurosci Biobehav Rev. 1991;15(1):95-8.
21. Musselman ME, Saely S. Diagnosis and treatment of drug-induced hyperthermia. Am J Health Syst Pharm. 2013;70(1):34-42.
22. Eyer F, Zilker T. Bench-to-bedside review: mechanisms and management of hyperthermia due to toxicity. Crit Care. 2007;11(6):236.
23. Deguigne M, Legeay M, Scholastique A-S, Chauveau P, Descatha A. Whole-bowel irrigation in cases of poisoning: A retrospective multicentre study of feasibility, tolerability, and effectiveness. Aust Crit Care. 2023;36(3):298-306.
24. Cumpston KL, Aks SE, Sigg T, Pallasch E. Whole Bowel Irrigation and the Hemodynamically Unstable Calcium Channel Blocker Overdose: Primum Non Nocere. J Emerg Med. 2010;38(2):171-4.
25. Tenenbein M. The role of whole bowel irrigation in the treatment of toxic ingestions. Br J Clin Pharmacol. 2023;89(8):2359-61.
26. Baid H, Kaeley N, Singh S, Mahala P, Chawang H, Datta SS, et al. Treatment Modalities in Calcium Channel Blocker Overdose: A Systematic Review. Cureus. 2023;15(8):e42854.
27. Myerson SG, Choudhury RP, Mitchell AR. Emergencies in cardiology: Oxford University Press, USA; 2010.
28. Subramanian R, Roebuck A, Joshi H, Drouin M. Predictors of Mortality in Adults With Calcium Channel Blocker Toxicity Receiving Extra Corporeal Membrane Oxygenation Support: An Extracorporeal Life Support Organization Registry Analysis. ASAIO J. 2025;71(3):200-203.
29. Lau MT, Wong CLW. Utility of triage shock index in predicting patient outcome in calcium channel blocker poisoning. Hong Kong J Emerg Med. 2020;30(2):73-8.
30. LiverTox: Clinical and Research Information on Drug-Induced Liver Injury [Internet]. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases; 2012-. Calcium Channel Blockers. [Updated 2017 Jan 11]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK548577/.
31. Ramesha KN, Rao KB, Kumar GS. Pattern and outcome of acute poisoning cases in a tertiary care hospital in Karnataka, India. Indian J Crit Care Med. 2009;13(3):152-5.
32. Xue C, Zeng J, Li W. Clinical characteristics and toxicological spectrum analysis of 493 cases of acute poisoning in children. BMC Emerg Med. 2024;24(1):181.
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