A Multicenter Assessment of the Accuracy of Claims Data in Appendicitis Research.

Journal: Annals Of Surgery
Published:
Abstract

Objective: To investigate accuracy of ICD-9/10 billing codes in a multicenter cohort.

Background: Health services research on appendicitis often relies on administrative databases. However, billing codes may misclassify disease severity, as we demonstrated previously in a single institution study.

Methods: We performed a multicenter study of adult patients with appendicitis who presented to one of six US medical centers during 2012-2015 (ICD-9 era) and 2018-2021 (ICD-10 era). Patients were identified based on ICD codes. Diagnosis was confirmed via chart review. Each patient was characterized as complicated or uncomplicated based on AAST criteria; this was considered the gold standard. Billing codes were compared to gold standard to calculate test parameters (i.e., sensitivity).

Results: 1832 patients met inclusion criteria. 54.1% were male, 25% non-white, and 44% publicly insured or uninsured. In total, 21.1% of patients had complicated appendicitis based on gold standard: 18.8% (312/1661) of surgical patients and 43.9% (75/171) of non-operative patients (P<0.001). Among all patients, 17.3% had a billing code for complicated appendicitis (12.5% true positives and 4.8% false positives). 40.8% (158 of 387) of patients with complicated appendicitis were misclassified as having uncomplicated appendicitis via ICD codes. Sensitivity and PPV for complicated appendicitis were 0.59 (95% CI: 0.54-0.64) and 0.72 (95% CI: 0.67-0.77), respectively.

Conclusions: Billing codes have poor sensitivity and PPV for distinguishing complicated from uncomplicated appendicitis. These results have significant implications for how we should interpret data from administrative database studies and construct future analyses. Inaccuracies in billing codes negatively impact hospital reimbursement, with tendency toward underpayment.

Authors
Brendin Beaulieu Jones, Aksel Laudon, Swetha Duraiswamy, Frank Yang, Elizabeth Chen, David Flum, Kasey Lerner, Heather Evans, Alex Charboneau, Vlad Simianu, Lauren Thompson, Faris Azar, Victoria Valdes, Chaitan Narsule, Sabrina Sanchez, Frederick Drake