Optimizing patient exposure in interventional cardiology is key to avoid skin injuries.
To establish predictive models of peak skin dose (PSD) during percutaneous coronary intervention (PCI), chronic total occlusion percutaneous coronary intervention (CTO), and transcatheter aortic valve implantation (TAVI) procedures.
A total of 534 PCI, 219 CTO, and 209 TAVI were collected from 12 hospitals in eight European countries. Independent associations between PSD and clinical and technical dose determinants were examined for those procedures using multivariate statistical analysis. A priori and a posteriori predictive models were built using stepwise multiple linear regressions. A fourfold cross-validation was performed, and models' performance was evaluated using the root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and linear correlation coefficient (r).
Multivariate analysis proved technical parameters to overweight clinical complexity indices with PSD mainly affected by fluoroscopy time, tube voltage, tube current, distance to detector, and tube angulation for PCI. For CTO, these were body mass index, tube voltage, and fluoroscopy contribution. For TAVI, these parameters were sex, fluoroscopy time, tube voltage, and cine acquisitions. When benchmarking the predictive models, the correlation coefficients were r = 0.45 for the a priori model and r = 0.89 for the a posteriori model for PCI. These were 0.44 and 0.67, respectively, for the CTO a priori and a posteriori models, and 0.58 and 0.74, respectively, for the TAVI a priori and a posteriori models.
A priori predictive models can help operators estimate the PSD before performing the intervention while a posteriori models are more accurate estimates and can be useful in the absence of skin dose mapping solutions.