A Pervasive Problem: Surgical Wound Class Miscoding in Pediatric Laparoscopic Appendectomy

John M Woodward1, Michael LaRock2, Krystle Bittner3

  • 1University at Buffalo Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14215, USA; University at Buffalo Division of Pediatric Surgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY 14215, USA.

PubMed

Insights

Surgical wound classification (SWC) miscoding is common in pediatric appendectomies, affecting over 20% of cases. This rate doubles to 50% for perforated appendicitis, impacting data accuracy.

Area of Science:

  • Pediatric Surgery
  • Surgical Quality Improvement
  • Health Informatics

Background:

  • Surgical wound classification (SWC) is crucial for predicting surgical site infections (SSIs), patient outcomes, and hospital quality assessment.
  • Accurate SWC is essential for reliable risk adjustment and research in surgical quality.

Purpose of the Study:

  • To determine the prevalence of SWC miscoding in pediatric laparoscopic appendectomies using a national database.
  • To identify miscoding rates in both non-perforated and perforated acute appendicitis cases.

Main Methods:

  • Analysis of data from the NSQIP-P registry (2017-2021) for pediatric laparoscopic appendectomies (CPT: 44970).
  • Evaluation of SWC based on ICD-10 codes for acute appendicitis (K35-K37).
  • Comparison of coded SWC (1-4) against expected classifications for non-perforated (SWC 3) and perforated (SWC 4) appendicitis.

Main Results:

  • A total of 71,374 procedures were analyzed.
  • An overall SWC miscoding rate of at least 22.5% was identified.
  • The miscoding rate was 28.1% for non-perforated and 49.9% for perforated appendicitis.

Conclusions:

  • Pediatric laparoscopic appendectomies exhibit a significant rate of SWC miscoding, exceeding 20% overall.
  • The miscoding rate approaches 50% for perforated appendicitis, highlighting a critical data quality issue.
  • Improved accuracy in SWC coding is necessary for reliable hospital metrics, risk calculators, and SSI research.
Abstract