The timely identification of outliers (implants, surgeons or patients) using prospectively collected registry data is confounded by many factors, including the assumption that the sampled population is representative of the entire cohort of patients. In this study we utilized a computer simulation of a joint registry to address the question: How does incomplete enrollment of patients in registries affect the reliability of identification of outliers, and what percent capture of the target population is sufficient? A synthetic registry was created consisting of 10,000 patients (100 surgeons), of whom, 1000 underwent joint replacement using a new implant. A predictive model for the risk of revision was created from data published by the Swedish TKR Registry and the AOANJRR. The pairing of patients, surgeons and implants was randomized and for each assignment, the probability of revision was computed. We then chose random samples of all patients in 10% increments from 10% to 100%, simulating incomplete capture of all potential cases by the registry. For each sample we calculated the number of cases of the new implant predicted to end in revision. The assignments were repeated 2000 times using implants with revision rates of 1.5%, 2.0% and 3.0% per annum vs. 1.0% for all other implants of the same class.INTRODUCTION
MATERIALS AND METHODS
The use of registry data to detect and eliminate inferior devices is based on the assumption that the results of the first cases performed with a new device are indicative of how the same implant would perform with widespread usage. However, existing registry data clearly proves that the performance of individual implants is very surgeon dependent. In this study we utilized a computer simulation of a large implant registry to address the question: How does the pairing of different surgeons with different implants affect the ability of registries to correctly identify inferior devices? A synthetic implant registry was created consisting of 10,000 patients who underwent joint replacement performed by 100 different surgeons using 5 different implants. Hazard functions representing the relative risks for revision associated with individual patients and surgeons were derived from the annual reports of implant registries. The cumulative revision rates (CRR values) of the 5 hypothetical implants were fixed at nominal values of 10%, 15%, 20%, 25%, and 30% at 15 years post operation vs. 10% for average implants. The surgeons were ordered according to their individual probabilities of a revision at less than 15 years post-op. Each surgeon was placed in one of 8 subsets comprised of 12.5% of the total surgeon pool, ranging from the lowest to the highest risk of revision. Patients, surgeons, and implants were randomly matched in an iterative fashion to simulate 500 separate RCTs, starting with the group of surgeons of with the lowest risk, and then repeating the simulation using surgeons with the lowest and second lowest risk of revision. This process was repeated iteratively until all surgeons were enrolled.Background:
Materials and Methods:
The use of registry data to detect and eliminate inferior devices is based on the assumption that the results of the first cases performed with a new device are indicative of how the same implant would perform with widespread usage. However, existing registry data clearly proves that the performance of individual implants is very surgeon dependent. In this study we utilized a computer simulation of a large implant registry to address the question: How does the pairing of different surgeons with different implants affect the ability of registries to correctly identify inferior devices? A synthetic implant registry was created consisting of 10,000 patients who underwent joint replacement performed by 100 different surgeons using 5 different implants. Hazard functions representing the relative risks for revision associated with individual patients and surgeons were derived from the annual reports of implant registries. The cumulative revision rates (CRR values) of the 5 hypothetical implants were fixed at nominal values of 10%, 15%, 20%, 25%, and 30% at 15 years post operation vs. 10% for average implants. The surgeons were ordered according to their individual probabilities of a revision at less than 15 years post-op. Each surgeon was placed in one of 8 subsets comprised of 12.5% of the total surgeon pool, ranging from the lowest to the highest risk of revision. Patients, surgeons, and implants were randomly matched in an iterative fashion to simulate 500 separate RCTs, starting with the group of surgeons of with the lowest risk, and then repeating the simulation using surgeons with the lowest and second lowest risk of revision. This process was repeated iteratively until all surgeons were enrolled.BACKGROUND
MATERIALS AND METHODS
