Concordance Between Registry and Administrative Data in the Determination of Comorbidity: A Multi-institutional Study

Ann Surg. 2020 Dec;272(6):1006-1011. doi: 10.1097/SLA.0000000000003247.

Abstract

Objective: To characterize agreement between administrative and registry data in the determination of patient-level comorbidities.

Background: Previous research finds poor agreement between these 2 types of data in the determination of outcomes. We hypothesized that concordance between administrative and registry data would also be poor.

Methods: A cohort of inpatient operations (length of stay 1 day or greater) was obtained from a consortium of 8 hospitals. Within each hospital, National Surgical Quality Improvement Program (NSQIP) data were merged with intra-institutional inpatient administrative data. Twelve different comorbidities (diabetes, hypertension, congestive heart failure, hemodialysis-dependence, cancer diagnosis, chronic obstructive pulmonary disease, ascites, sepsis, smoking, steroid, congestive heart failure, acute renal failure, and dyspnea) were analyzed in terms of agreement between administrative and NSQIP data.

Results: Forty-one thousand four hundred thirty-two inpatient surgical hospitalizations were analyzed in this study. Concordance (Cohen Kappa value) between the 2 data sources varied from 0.79 (diabetes) to 0.02 (dyspnea). Hospital variation in concordance (intersite variation) was quantified using a test of homogeneity. This test found significant intersite variation at a level of P < 0.001 for each of the comorbidities except for dialysis (P = 0.07) and acute renal failure (P = 0.19). These findings imply significant differences between hospitals in their generation of comorbidity data.

Conclusion: This study finds significant differences in how administrative versus registry data assess patient-level comorbidity. These differences are of concern to patients, payers, and providers, each of which had a stake in the integrity of these data. Standardized definitions of comorbidity and periodic audits are necessary to ensure data accuracy and minimize bias.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Aged
  • Cohort Studies
  • Comorbidity
  • Female
  • Hospital Records*
  • Humans
  • Male
  • Medical Records*
  • Middle Aged
  • Postoperative Complications / epidemiology*
  • Registries*