Using Administrative Billing Codes to Identify Acute Musculoskeletal Infections in Children

Hosp Pediatr. 2023 Feb 1;13(2):182-195. doi: 10.1542/hpeds.2022-006821.

Abstract

Background and objectives: Acute hematogenous musculoskeletal infections (MSKI) are medical emergencies with the potential for life-altering complications in afflicted children. Leveraging administrative data to study pediatric MSKI is difficult as many infections are chronic, nonhematogenous, or occur in children with significant comorbidities. The objective of this study was to validate a case-finding algorithm to accurately identify children hospitalized with acute hematogenous MSKI using administrative billing codes.

Methods: This was a multicenter validation study using the Pediatric Health Information System (PHIS) database. Hospital admissions for MSKI were identified from 6 PHIS hospitals using discharge diagnosis codes. A random subset of admissions underwent manual chart review at each site using predefined criteria to categorize each admission as either "acute hematogenous MSKI" (AH-MSKI) or "not acute hematogenous MSKI." Ten unique coding algorithms were developed using billing data. The sensitivity and specificity of each algorithm to identify AH-MSKI were calculated using chart review categorizations as the reference standard.

Results: Of the 492 admissions randomly selected for manual review, 244 (49.6%) were classified as AH-MSKI and 248 (50.4%) as not acute hematogenous MSKI. Individual algorithm performance varied widely (sensitivity 31% to 91%; specificity 52% to 98%). Four algorithms demonstrated potential for future use with receiver operating characteristic area under the curve greater than 80%.

Conclusions: Identifying children with acute hematogenous MSKI based on discharge diagnosis alone is challenging as half have chronic or nonhematogenous infections. We validated several case-finding algorithms using administrative billing codes and detail them here for future use in pediatric MSKI outcomes.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Child
  • Databases, Factual
  • Hospitalization
  • Humans
  • Infections*
  • Retrospective Studies
  • Sensitivity and Specificity