A systematic approach to using regression modelling and ‘big data’ to derive a meaningful clinical decision rule for epilepsy
Date
2018-08-22
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Abstract
Introduction: clinical decision rules (CDRs) have been developed in a number of medical fields resulting in improved patient outcomes, quality of care, and health economics. Aims: to identify all CDRs developed for epilepsy and to derive one that guides the prescription of the antiepileptic drug (AED), levetiracetam, according to its risk of a psychiatric adverse effect. Methods: a systematic review and meta-analysis was first performed to determine the state of the literature with respect to CDRs in epilepsy. The Health Improvement Network (THIN) electronic medical records register was used to identify patients with epilepsy by employing a modified validated case definition with a 5-year washout. Analyses were restricted to patients receiving AED monotherapy and the association between levetiracetam use and psychiatric adverse effects was explored Cox proportional hazards regression with timevarying covariates. Finally, logistic regression with parameter regularisation and k=5 fold cross validation was used to derive the CDR that predicts the development of psychiatric adverse effects following levetiracetam prescription. Results: the systematic review identified four epilepsy-specific CDRs, none of which guided AED prescription. A total of 9595 presumed incident cases of epilepsy (85.7 cases per 100,000 persons) were identified in THIN. Both carbamazepine (hazard ratio [HR]: 0.84, 95% confidence interval [95% CI]: 0.73– 0.97; p = 0.02) and lamotrigine (HR: 0.83, 95% CI: 0.70–0.99; p = 0.03) were associated with reduced hazards of a psychiatric sign, symptom, or disorder iii compared to no AED treatment. Levetiracetam was not associated with psychiatric adverse effects but the analyses were underpowered (n=202; 3%). All patients receiving levetiracetam (1173/7400; 16%) were included for CDR derivation. Prediction variables were incorporated into multiple logistic regression models with parameter regularisation. Odds of reporting a psychiatric complaint were elevated for females and those with a pre-exposure history of depression, anxiety, recreational drug use, or higher social deprivation. The prediction model performed well (area under the curve [AUC] 0.68; 95% confidence interval 0.58- 0.79 after stratified k=5 fold cross-validation). Using a cut-off threshold 0.1, the CDR had a specificity of 83%. Conclusion: If externally validated and properly implemented, this CDR could be used to guide prescription in clinical practice.
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Epilepsy, levetiracetam, psychiatric adverse effects, prediction modelling, clinical decision rules
Citation
Josephson, C. B. (2018). A systematic approach to using regression modelling and ‘big data’ to derive a meaningful clinical decision rule for epilepsy (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32839