Predicting the risk of motor vehicle crash in the first year after cardioverter-defibrillator implantation
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Background
Baseline health and driving data might allow clinicians to personalize medical driving restrictions after implantable cardioverter-defibrillator (ICD) implantation.
Methods
Using 22 years of population-based administrative data from British Columbia, Canada, we identified licensed drivers with a first ICD implantation between 1998 and 2018. After stratifying by ICD indication (primary vs secondary prevention of sudden cardiac death), we applied regression techniques to baseline health and driving data to estimate each driver’s 1- year crash risk. We assessed optimism-corrected discrimination and calibration of the final model using 200 bootstrapped samples.
Results
In the first year after implantation, there were 352 crashes among 3652 primary prevention ICD recipients and 270 crashes among 3408 secondary prevention ICD recipients. Crash prediction models exhibited poor discrimination (c-statistics 0.60 and 0.61, respectively) but good calibration (calibration slopes 1.14 and 1.07). The strongest predictors of crash among primary prevention ICD recipients were male sex, active vehicle insurance in the past year, and the number of crashes in the past year. The strongest predictors of crash among secondary prevention ICD recipients were male sex, no history of seizure, an active prescription for opioids, and active vehicle insurance in the past year.
Conclusions
Crash prediction models based on health and driving data had a limited ability to distinguish individuals who subsequently crashed from individuals who did not. Observed crash risks are likely to be strongly influenced by unobserved changes in road exposure (the hours or miles driven per week), limiting the application of these risk scores by clinicians and policymakers.
