Abstract
Abstract
Purpose: Patient radiation doses in cardiovascular and interventional radiology are highly variable for similar procedures. This random nature may be better described by a distribution function, compared to a linear regression. This study develops a distribution function to characterize patient dose distributions and estimate probabilistic risk. Methods: Reference air kerma from 8647 patients over six years were retrospectively collected from an EP lab and two Cath labs. Data was first sorted into low dose (<100mGy) and high dose cases (>100mGy), and histograms of the data created. Dagum and Inverse gamma distributions were chosen to initially fit to both low and high dose cases. Fits between model and the data was optimized, and a linear regression analysis performed to obtain R squared values and standard errors for the correlation between model and data. Risk probabilities were estimated according to the modeled distribution function. BMI and time distributions were analyzed to understand their influence on the inverse gamma distribution error found in the data. 75th percentiles from both descriptive statistics and model were calculated. Results: The inverse gamma distribution can be used to characterize radiation dose distributions. Model predicted cases for radiation dose 3000mGy< x <5000mGy and >5000mGy are approximately 42 and 0 for 3651 cases for lab#1, and 14 and 1 for 3197 cases for lab#2, respectively, while the actual cases are 10 and 0, and 16 and 2. Descriptive and model statistics generated different 75th percentile levels for sorted data compared to unsorted data. Time has a greater influence on inverse gamma distribution function than BMI. Conclusion: This study creates a framework to understand the random error present in radiology practices that cause wide variations in patient radiation doses. It also provides an approach to evaluate different IR areas in terms of effectiveness of dose reduction measures.