Overcoming barriers to population-based injury research: development and validation of an ICD-10–to–AIS algorithm

Abstract

Background: Hospital administrative databases are a useful source of populationlevel data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10). Methods:We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTR-CDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used κ and intraclass correlation coefficients (ICC) as measures of concordance.Results: In total, 10 431 patients were identified in the OTR-CDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (κ 0.65), as well as patients with a head AIS score of 3 or greater (κ 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81-0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality. Conclusion:Our ICD-10-to-AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10.Contexte : Les bases de données administratives des hôpitaux sont des sources utiles pour obtenir des données démographiques au sujet des patients victimes de blessures; ces bases de données utilisent toutefois le système de classification internationale des maladies (CIM) qui ne permet pas d'estimer directement la gravité des blessures. Nous avons créé et validé un tableau de concordance pour établir les scores de la listetype des blessures (LTB) à partir des codes de diagnostics liés aux traumatismes cités dans la dixième révision du manuel CIM (CIM-10).

Direct medical costs of motorcycle crashes in Ontario

“…ISS and AIS were obtained from ICD-10 codes by means of a validated algorithm. 29 Injury Severity Score was categorized as < 9, 9-15, 16-24 or ≥ 25. We defined "severe injuries" as those with an AIS ≥ 3 and we reported them overall and for each anatomic region.…”

Emergency Access to Neurosurgical Care for Patients with Traumatic Brain Injury

“…Abbreviated Injury Scores were derived using a previously validated ICD-10 to AIS crosswalk algorithm. 9 Patients who presented dead on arrival or those who died within 30 minutes of ED arrival were excluded from analysis because these outcomes are typically not modifiable through improved access to neurosurgical care.…”

Institutional and provider factors impeding access to trauma center care

“…codes by using a validated algorithm. 12 As the objective of this article focused on the likelihood and timeliness of transfer of severely injured patients, we excluded those classified as dead on arrival as well as those who died in the ED, believing that transfer would not have been possible for these patients. As patients with severe injuries are typically admitted to the hospital, we excluded patients discharged home from the ED.…”