Journal of neurointerventional surgery · 2022
Assessing the volume-outcome relationship of carotid artery stenting in nationwide administrative data: a challenge of patient population bias.
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Koester Stefan W, Cole Tyler S, Kimata Anna R, Ma Kevin L, Benner Dimitri, Catapano Joshua S, Rumalla Kavelin, Lawton Michael T, Ducruet Andrew F, Albuquerque Felipe C
Аннотация
BACKGROUND: Studies have shown an association between surgical treatment volume and improved quality metrics. This study evaluated nationwide results in carotid artery stenting (CAS) procedural readmission rates, costs, and length of stay based on hospital treatment volume.
METHODS: We used the Nationwide Readmissions Database for carotid stenosis from 2010 to 2015. Patients receiving CAS were matched based on demographics, illness severity, and relevant comorbidities. Patients were matched 1:1 between low- and high-volume centers using a non-parametric preprocessing matching program to adjust for parametric causal inferences. Nearest-neighbor propensity score matching was performed using logit distance.
RESULTS: Low- and high-volume centers admitted a mean (SD) of 4.68 (3.79) and 25.10 (16.86) patients undergoing CAS per hospital, respectively. Comorbidities were significantly different and initially could not be adequately matched. Because of significant differences in baseline patient population characteristics after attempted matching between low- and high-volume centers, we used propensity adjustment with multivariate analysis. Using this alternative approach, no significant differences were observed between low- and high-volume centers for the presence of any complication, postoperative stroke, postoperative myocardial infarction, and readmission at 30 days.
CONCLUSION: In 1:1 nearest-neighbor matching with a high number of patients, our analysis did not result in well-matched cohorts for the effect of case volume on outcomes. Comparing analytical techniques for various outcomes highlights that outcome disparities may not be related to quality differences based on hospital size, but rather variability in patient populations between low- and high-volume institutions.
METHODS: We used the Nationwide Readmissions Database for carotid stenosis from 2010 to 2015. Patients receiving CAS were matched based on demographics, illness severity, and relevant comorbidities. Patients were matched 1:1 between low- and high-volume centers using a non-parametric preprocessing matching program to adjust for parametric causal inferences. Nearest-neighbor propensity score matching was performed using logit distance.
RESULTS: Low- and high-volume centers admitted a mean (SD) of 4.68 (3.79) and 25.10 (16.86) patients undergoing CAS per hospital, respectively. Comorbidities were significantly different and initially could not be adequately matched. Because of significant differences in baseline patient population characteristics after attempted matching between low- and high-volume centers, we used propensity adjustment with multivariate analysis. Using this alternative approach, no significant differences were observed between low- and high-volume centers for the presence of any complication, postoperative stroke, postoperative myocardial infarction, and readmission at 30 days.
CONCLUSION: In 1:1 nearest-neighbor matching with a high number of patients, our analysis did not result in well-matched cohorts for the effect of case volume on outcomes. Comparing analytical techniques for various outcomes highlights that outcome disparities may not be related to quality differences based on hospital size, but rather variability in patient populations between low- and high-volume institutions.