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Original Investigation
January 28, 2019

Prediction Tools for Psychiatric Adverse Effects After Levetiracetam Prescription

Author Affiliations
  • 1Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  • 2Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Alberta, Canada
  • 3Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
  • 4O’Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
  • 5Desid Labs Inc, Calgary, Alberta, Canada
  • 6Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York
  • 7Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
  • 8Department of Psychology, University of Calgary, Calgary, Alberta, Canada
  • 9Clinical Research Unit, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
JAMA Neurol. 2019;76(4):440-446. doi:10.1001/jamaneurol.2018.4561
Key Points

Question  Can routine clinical data be used to predict which patients with epilepsy will experience a psychiatric adverse effect from levetiracetam?

Findings  Among 1173 patients with epilepsy receiving levetiracetam in this open cohort study, 2 prediction models were created: one for the overall population and one for those without a history of a psychiatric sign, symptom, or disorder during the study period. The corresponding areas under the curve were 68% and 72%, respectively, and specificity was maximized using threshold cutoffs of 0.10 (full model) and 0.14 (second model); a score below these thresholds indicates safety of prescription.

Meaning  Levetiracetam has rapidly become a drug of first choice, and these models can be used to predict the risk of psychiatric adverse effects.

Abstract

Importance  Levetiracetam is a commonly used antiepileptic drug, yet psychiatric adverse effects are common and may lead to treatment discontinuation.

Objective  To derive prediction models to estimate the risk of psychiatric adverse effects from levetiracetam use.

Design, Setting, and Participants  Retrospective open cohort study. All patients meeting the case definition for epilepsy after the Acceptable Mortality Reporting date in The Health Improvement Network (THIN) database based in the United Kingdom (inclusive January 1, 2000, to May 31, 2012) who received a first-ever prescription for levetiracetam were included. Of 11 194 182 patients registered in THIN, this study identified 7400 presumed incident cases (66.1 cases per 100 000 persons) over a maximum of 12 years’ follow-up. The index date was when patients received their first prescription code for levetiracetam, and follow-up lasted 2 years or until an event, loss to follow-up, or censoring. The analyses were performed on April 22, 2018.

Exposure  A presumed first-ever prescription for levetiracetam.

Main Outcomes and Measures  The outcome of interest was a Read code for any psychiatric sign, symptom, or disorder as reached through consensus by 2 authors. This study used regression techniques to derive 2 prediction models, one for the overall population and one for those without a history of a psychiatric sign, symptom, or disorder during the study period.

Results  Among 1173 patients with epilepsy receiving levetiracetam, the overall median age was 39 (interquartile range, 25-56) years, and 590 (50.3%) were female. A total of 14.1% (165 of 1173) experienced a psychiatric symptom or disorder within 2 years of index prescription. The odds of reporting a psychiatric symptom were significantly elevated for women (odds ratio [OR], 1.41; 95% CI, 0.99-2.01; P = .05) and those with a preexposure history of higher social deprivation (OR, 1.15; 95% CI, 1.01-1.31; P = .03), depression (OR, 2.20; 95% CI, 1.49-3.24; P < .001), anxiety (OR, 1.74; 95% CI, 1.11-2.72; P = .02), or recreational drug use (OR, 2.02; 95% CI, 1.20-3.37; P = .008). The model performed well after stratified k = 5-fold cross-validation (area under the curve [AUC], 0.68; 95% CI, 0.58-0.79). There was a gradient in risk, with probabilities increasing from 8% for 0 risk factors to 11% to 17% for 1, 17% to 31% for 2, 30% to 42% for 3, and 49% when all risk factors were present. For those free of a preexposure psychiatric code, a second model performed comparably well after k = 5-fold cross-validation (AUC, 0.72; 95% CI, 0.54-0.90). Specificity was maximized using threshold cutoffs of 0.10 (full model) and 0.14 (second model); a score below these thresholds indicates safety of prescription.

Conclusions and Relevance  This study derived 2 simple models that predict the risk of a psychiatric adverse effect from levetiracetam. These algorithms can be used to guide prescription in clinical practice.

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