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Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study

Elham Bazmi, Behnam Behnoush, Saeed Hashemi Nazari, Soheila Khodakarim, Amir Hossein Behnoush, Hamid Soori




Introduction: Seizure is a common complication of tramadol poisoning and predicting it will help clinicians in preventing seizure and better management of patients. This study aimed to develop and validate a prediction model to assess the risk of seizure in acute tramadol poisoning.

Methods: This retrospective observational study was conducted on 909 patients with acute tramadol poisoning in Baharloo Hospital, Tehran, Iran, (2015-2019). Several available demographic, clinical, and para-clinical characteristics were considered as potential predictors of seizure and extracted from clinical records. The data were split into derivation and validation sets (70/30 split) via random sampling. Derivation set was used to develop a multivariable logistic regression model. The model was tested on the validation set and its performance was assessed with receiver operating characteristic (ROC) curve.

Results: The mean (standard deviation (SD)) of patients’ age was 23.75 (7.47) years and 683 (75.1%) of them were male. Seizures occurred in 541 (60%) patients.  Univariate analysis indicated that sex, pulse rate (PR), arterial blood Carbone dioxide pressure (PCO2), Glasgow Coma Scale (GCS), blood bicarbonate level, pH, and serum sodium level could predict the chance of seizure in acute tramadol poisoning. The final model in derivation set consisted of sex, PR, GCS, pH, and blood bicarbonate level. The model showed good accuracy on the validation set with an area under the ROC curve of 0.77 (95% CI: 0.67–0.87).

Conclusion: Representation of this model as a decision tree could help clinicians to identify high-risk patients with tramadol poisoning-induced seizure and in decision-making at triage of emergency departments in hospitals.


Clinical decision-making; tramadol; poisoning; seizures


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DOI: https://doi.org/10.22037/aaem.v8i1.723

DOI (PDF): https://doi.org/10.22037/aaem.v8i1.723.g811