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Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study.
Arch Acad Emerg Med. 2020; 8(1):e59.AA

Abstract

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.

Authors+Show Affiliations

Department of Epidemiology, Scho ol of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Department of Forensic Medicine, Tehran University of Medical Sciences, Tehran, Iran.Department of Epidemiology, Scho ol of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Department of Epidemiology, Scho ol of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Tehran University of Medical Sciences, Tehran, Iran.Safety Promotion and Injury Prevention Research center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32613201

Citation

Bazmi, Elham, et al. "Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation Study." Archives of Academic Emergency Medicine, vol. 8, no. 1, 2020, pp. e59.
Bazmi E, Behnoush B, Hashemi Nazari S, et al. Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study. Arch Acad Emerg Med. 2020;8(1):e59.
Bazmi, E., Behnoush, B., Hashemi Nazari, S., Khodakarim, S., Behnoush, A. H., & Soori, H. (2020). Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study. Archives of Academic Emergency Medicine, 8(1), e59.
Bazmi E, et al. Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation Study. Arch Acad Emerg Med. 2020;8(1):e59. PubMed PMID: 32613201.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Seizure Prediction Model in Acute Tramadol Poisoning; a Derivation and Validation study. AU - Bazmi,Elham, AU - Behnoush,Behnam, AU - Hashemi Nazari,Saeed, AU - Khodakarim,Soheila, AU - Behnoush,Amir Hossein, AU - Soori,Hamid, Y1 - 2020/05/17/ PY - 2020/7/3/entrez PY - 2020/7/3/pubmed PY - 2020/7/3/medline KW - Clinical decision-making KW - poisoning KW - seizures KW - tramadol SP - e59 EP - e59 JF - Archives of academic emergency medicine JO - Arch Acad Emerg Med VL - 8 IS - 1 N2 - 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. SN - 2645-4904 UR - https://www.unboundmedicine.com/medline/citation/32613201/Seizure_Prediction_Model_in_Acute_Tramadol_Poisoning L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32613201/ DB - PRIME DP - Unbound Medicine ER -
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