Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning.
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: Th...
| Publicado en: | Archives of Academic Emergency Medicine Vol. 13; no. 1; pp. 1 - 17 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Shahid Beheshti University of Medical Sciences
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190500721&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190500721 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26454904 MFMK jtl: Archives of Academic Emergency Medicine issn: 26454904 maglogo: N pubinfo: dt: 2025 vid: 13 iid: 1 pid: 87963 pub: Shahid Beheshti University of Medical Sciences artinfo: ui: 190500721 190500721 190500721 10.22037/aaem.v13i1.2804 190500721 ppf: 1 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Machine Learning-Based Prognostic Prediction Models in Calcium Channel Blockers Poisoning. aug: au: Mostafazadeh, Babak Hosseini, Sayed Masoud Shadnia, Shahin Mehmandoost, Mahdi Taremi, Mahsa Mohtarami, Seyed Ali Talab Evini, Peyman Erfan Rahimi, Mitra Eini, Pooya Taherkhani, Amirreza Amini, Nahal Babaeian Heidarli, Elmira Meshkini, Mohammad Amine, Leena Thabet, Hafedh affil: Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. sug: subj: Machine Learning Prediction Models Calcium Channel Blockers Poisoning Drug Toxicity Prognosis Calcium Channel Blockers Adverse Effects Overdose Complications Cardiovascular Risk Factors Risk Assessment Predictive Value of Tests Poisoning Prognosis Drug Toxicity Mortality Human Funding Source Iran Male Female Infant Child, Preschool Child Adolescence Adult Middle Age Aged Aged, 80 and Over Retrospective Design Record Review Cross Sectional Studies Amlodipine Poisoning Diltiazem Poisoning Verapamil Poisoning Electrocardiography Descriptive Statistics Data Analysis Software Kruskal-Wallis Test Chi Square Test Two-Tailed Test Confidence Intervals Sensitivity and Specificity ROC Curve Glasgow Coma Scale Body Temperature Oxygen Saturation Troponin Alanine Aminotransferase Blood Creatinine Blood Potassium Blood Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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