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Spatial heterogeneity in risk-adjusted return of spontaneous circulation after out-of-hospital cardiac arrest: the
Dokyeong Lee1,2, Martin Bender3, Eiko Spielmann3
1Institute of Public Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany. dokyeong.lee@charite.de.
Background:
Registry-based benchmarking of return of spontaneous circulation (ROSC) requires well-calibrated risk adjustment across Emergency Medical Services (EMS) systems. Existing risk-adjustment models, however, do not account for urbanization-related heterogeneity, which may contribute to systematic urbanization-stratified miscalibration, bias cross-system benchmarking, and misdirect quality improvement. We developed and validated an urbanization-adjusted ROSC after cardiac arrest (URACA) by incorporating Eurostat's Degree of Urbanisation (DEGURBA) into RACA and assessed whether it improves calibration across urbanization levels without compromising discrimination.
Methods:
Using 2014-2023 German Resuscitation Registry data, we identified 145,780 adult (≥18 years) out-of-hospital cardiac arrests and randomly split cases into development and validation cohorts. DEGURBA was recoded as metropolises (≥500,000 inhabitants), cities (<500,000), and non-urban areas (all others). We refitted RACA and fitted URACA using logistic regression with multiple imputation for missing values. In validation, we assessed calibration (calibration belts), discrimination (area under receiver operating characteristic curve; AUROC), prediction error (Brier score), and reclassification (continuous net reclassification improvement; cNRI).
Results:
Refitted RACA exhibited urbanization-stratified miscalibration, overpredicting in non-urban areas and underpredicting in metropolises and cities. URACA reduced these systematic deviations: in non-urban areas, predicted ROSC closely matched observed ROSC across the full risk range, and in metropolises and cities, the mid-range underprediction was resolved. Discrimination and overall accuracy were unchanged (AUROC 0.735 vs 0.737; Brier 0.202 for both), while reclassification improved (cNRI 9.4%).
Conclusions:
URACA mitigates urbanization-stratified calibration bias while preserving discrimination, enhancing the comparability of risk-adjusted ROSC benchmarking across EMS systems.
