Infectious Surgical Complications are Not Dichotomous: Characterizing Discordance Between Administrative Data and Registry Data

Ann Surg. 2018 Jan;267(1):81-87. doi: 10.1097/SLA.0000000000002041.

Abstract

Objective: To characterize reasons for discordance between administrative data and registry data in the determination of postoperative infectious complications.

Background: Data regarding the occurrence of postoperative surgical complications are identified through either administrative or registry data. Rates of complications vary significantly between these two types of data; the reasons for this are not well-understood.

Methods: The occurrence of 30-day inpatient infectious complications (pneumonia, sepsis, surgical site infection, and urinary tract infection) was compared between the NSQIP and administrative mechanisms at 4 academic hospitals between 2012 and 2014. In each situation where the NSQIP and administrative data were discordant regarding the occurrence of a specific complication, a 2-clinician chart abstraction was performed to characterize the reasons for discordance as (i) administrative coding error, (ii) NSQIP coding error, (iii) "question of criteria", where the discordance was the result of differences in criteria, or (iv) "dually incorrect", where both data sources coded the complication incorrectly.

Results: The cohort included 19,163 patients undergoing surgery in 4 different academic hospitals. Rates of infectious complications varied up to 5-fold between the two data sources. A total of 717 discordant complications were identified. Of these, the greatest portion (43%) was due to "question of criteria," followed by administrative coding error (37%), NSQIP error (15%), and dually incorrect (5%).

Conclusions: With a goal of improving existing mechanisms for measuring surgical quality, definitions for the occurrence of a postoperative complication need to be developed and applied consistently. Progress toward this goal will enable patients and payers to better take advantage of recent advances in healthcare data transparency.

Publication types

  • Multicenter Study

MeSH terms

  • Databases, Factual
  • Female
  • Follow-Up Studies
  • Hospital Administration / statistics & numerical data*
  • Hospital Records*
  • Humans
  • Incidence
  • Inpatients*
  • Male
  • Middle Aged
  • Registries*
  • Retrospective Studies
  • Surgical Wound Infection / epidemiology*
  • United States / epidemiology