BACKGROUND: Delays to defibrillation are associated with worse survival after in-hospital cardiac arrest, but the degree to which hospitals vary in defibrillation response times and hospital predictors of delays remain unknown. METHODS: Using hierarchical models, we evaluated hospital variation in rates of delayed defibrillation (>2 minutes) and its impact on survival among 7479 adult inpatients with cardiac arrests at 200 hospitals within the National Registry of Cardiopulmonary Resuscitation. RESULTS: Adjusted rates of delayed defibrillation varied substantially among hospitals (range, 2.4%-50.9%), with hospital-level effects accounting for a significant amount of the total variation in defibrillation delays after adjusting for patient factors. We found a 46% greater odds of patients with identical covariates getting delayed defibrillation at one randomly selected hospital compared with another. Among traditional hospital factors evaluated, however, only bed volume (reference category: <200 beds; 200-499 beds: odds ratio [OR], 0.62 [95% confidence interval {CI}, 0.48-0.80]; >or=500 beds: OR, 0.74 [95% CI, 0.53-1.04]) and arrest location (reference category: intensive care unit; telemetry unit: OR, 1.92 [95% CI, 1.65-2.22]; nonmonitored unit: OR, 1.90 [95% CI, 1.61-2.24]) were associated with differences in rates of delayed defibrillation. Wide variation also existed in adjusted hospital rates of survival to discharge (range, 5.3%-49.6%), with higher survival among hospitals in the top-performing quartile for defibrillation time (compared with the bottom quartile: OR for top quartile, 1.41 [95% CI, 1.11-1.77]). CONCLUSIONS: Rates of delayed defibrillation vary widely among hospitals but are largely unexplained by traditional hospital factors. Given its association with improved survival, future research is needed to better understand best practices in the delivery of defibrillation at top-performing hospitals.
BACKGROUND: Delays to defibrillation are associated with worse survival after in-hospital cardiac arrest, but the degree to which hospitals vary in defibrillation response times and hospital predictors of delays remain unknown. METHODS: Using hierarchical models, we evaluated hospital variation in rates of delayed defibrillation (>2 minutes) and its impact on survival among 7479 adult inpatients with cardiac arrests at 200 hospitals within the National Registry of Cardiopulmonary Resuscitation. RESULTS: Adjusted rates of delayed defibrillation varied substantially among hospitals (range, 2.4%-50.9%), with hospital-level effects accounting for a significant amount of the total variation in defibrillation delays after adjusting for patient factors. We found a 46% greater odds of patients with identical covariates getting delayed defibrillation at one randomly selected hospital compared with another. Among traditional hospital factors evaluated, however, only bed volume (reference category: <200 beds; 200-499 beds: odds ratio [OR], 0.62 [95% confidence interval {CI}, 0.48-0.80]; >or=500 beds: OR, 0.74 [95% CI, 0.53-1.04]) and arrest location (reference category: intensive care unit; telemetry unit: OR, 1.92 [95% CI, 1.65-2.22]; nonmonitored unit: OR, 1.90 [95% CI, 1.61-2.24]) were associated with differences in rates of delayed defibrillation. Wide variation also existed in adjusted hospital rates of survival to discharge (range, 5.3%-49.6%), with higher survival among hospitals in the top-performing quartile for defibrillation time (compared with the bottom quartile: OR for top quartile, 1.41 [95% CI, 1.11-1.77]). CONCLUSIONS: Rates of delayed defibrillation vary widely among hospitals but are largely unexplained by traditional hospital factors. Given its association with improved survival, future research is needed to better understand best practices in the delivery of defibrillation at top-performing hospitals.
Authors: Véronique L Roger; Alan S Go; Donald M Lloyd-Jones; Emelia J Benjamin; Jarett D Berry; William B Borden; Dawn M Bravata; Shifan Dai; Earl S Ford; Caroline S Fox; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Diane M Makuc; Gregory M Marcus; Ariane Marelli; David B Matchar; Claudia S Moy; Dariush Mozaffarian; Michael E Mussolino; Graham Nichol; Nina P Paynter; Elsayed Z Soliman; Paul D Sorlie; Nona Sotoodehnia; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2011-12-15 Impact factor: 29.690
Authors: Véronique L Roger; Alan S Go; Donald M Lloyd-Jones; Robert J Adams; Jarett D Berry; Todd M Brown; Mercedes R Carnethon; Shifan Dai; Giovanni de Simone; Earl S Ford; Caroline S Fox; Heather J Fullerton; Cathleen Gillespie; Kurt J Greenlund; Susan M Hailpern; John A Heit; P Michael Ho; Virginia J Howard; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Diane M Makuc; Gregory M Marcus; Ariane Marelli; David B Matchar; Mary M McDermott; James B Meigs; Claudia S Moy; Dariush Mozaffarian; Michael E Mussolino; Graham Nichol; Nina P Paynter; Wayne D Rosamond; Paul D Sorlie; Randall S Stafford; Tanya N Turan; Melanie B Turner; Nathan D Wong; Judith Wylie-Rosett Journal: Circulation Date: 2010-12-15 Impact factor: 29.690
Authors: Paul S Chan; Harlan M Krumholz; John A Spertus; Philip G Jones; Peter Cram; Robert A Berg; Mary Ann Peberdy; Vinay Nadkarni; Mary E Mancini; Brahmajee K Nallamothu Journal: JAMA Date: 2010-11-15 Impact factor: 56.272
Authors: Monique L Anderson; Graham Nichol; David Dai; Paul S Chan; Laine Thomas; Sana M Al-Khatib; Robert A Berg; Steven M Bradley; Eric D Peterson Journal: JAMA Cardiol Date: 2016-04-01 Impact factor: 14.676
Authors: Dana P Edelson; Trevor C Yuen; Mary E Mancini; Daniel P Davis; Elizabeth A Hunt; Joseph A Miller; Benjamin S Abella Journal: J Hosp Med Date: 2014-02-19 Impact factor: 2.960