OBJECTIVES: New York State Trauma Registry data were analyzed to determine whether there is a significant relationship between the volume of trauma patients treated by a trauma center and its risk-adjusted inpatient mortality rate. METHODS: Stepwise logistic regression was used to identify significant independent predictors of mortality, their weights, and the probability of in-hospital mortality for each patient. These data were then used to calculate risk-adjusted mortality rates for various ranges of hospital volume. Ranges were identified on the basis of homogeneity of mortality rates, the number of hospitals in each range, and the number of patients in each range. Three volume measures were used: (1) total annual volume of trauma cases > or = 1200 and total annual volume > or = 240 for patients with Injury Severity Score (ISS) > or = 15 (equivalent to American College of Surgeons [ACS] criteria), (2) total annual volume of patients with ISS > or = 15, and (3) total annual volume of cases in the Registry (approximately, inpatients with ISS > or = 9). RESULTS: Results show that the 35 New York State trauma centers not meeting the ACS criteria had lower, but not significantly lower, observed and risk-adjusted mortality rates (7.62% and 8.25%, respectively) than the corresponding rates for the 8 New York State trauma centers that met the ACS criteria (9.36% and 8.83%, respectively). Regarding the other two criteria, hospital ranges representing lower annual volumes tended to have somewhat lower, although not significantly lower, observed and risk-adjusted mortality rates. For example, using a total annual volume for patients with ISS > or = 15, the risk-adjusted mortality rates for the volume ranges 1-150, 151-250, and 251+ were 7.78%, 9.23%, and 8.70%, respectively. CONCLUSIONS: We were unable to document an inverse relationship between hospital volume and inpatient mortality rate for trauma centers in New York State. Volume criteria should not be considered indicators of the quality of trauma care.
OBJECTIVES: New York State Trauma Registry data were analyzed to determine whether there is a significant relationship between the volume of traumapatients treated by a trauma center and its risk-adjusted inpatient mortality rate. METHODS: Stepwise logistic regression was used to identify significant independent predictors of mortality, their weights, and the probability of in-hospital mortality for each patient. These data were then used to calculate risk-adjusted mortality rates for various ranges of hospital volume. Ranges were identified on the basis of homogeneity of mortality rates, the number of hospitals in each range, and the number of patients in each range. Three volume measures were used: (1) total annual volume of trauma cases > or = 1200 and total annual volume > or = 240 for patients with Injury Severity Score (ISS) > or = 15 (equivalent to American College of Surgeons [ACS] criteria), (2) total annual volume of patients with ISS > or = 15, and (3) total annual volume of cases in the Registry (approximately, inpatients with ISS > or = 9). RESULTS: Results show that the 35 New York State trauma centers not meeting the ACS criteria had lower, but not significantly lower, observed and risk-adjusted mortality rates (7.62% and 8.25%, respectively) than the corresponding rates for the 8 New York State trauma centers that met the ACS criteria (9.36% and 8.83%, respectively). Regarding the other two criteria, hospital ranges representing lower annual volumes tended to have somewhat lower, although not significantly lower, observed and risk-adjusted mortality rates. For example, using a total annual volume for patients with ISS > or = 15, the risk-adjusted mortality rates for the volume ranges 1-150, 151-250, and 251+ were 7.78%, 9.23%, and 8.70%, respectively. CONCLUSIONS: We were unable to document an inverse relationship between hospital volume and inpatient mortality rate for trauma centers in New York State. Volume criteria should not be considered indicators of the quality of trauma care.
Authors: David C Lee; Brendan G Carr; Tony E Smith; Van C Tran; Daniel Polsky; Charles C Branas Journal: Popul Health Manag Date: 2015-02-06 Impact factor: 2.459
Authors: K John McConnell; Craig D Newgard; Richard J Mullins; Melanie Arthur; Jerris R Hedges Journal: Health Serv Res Date: 2005-04 Impact factor: 3.402
Authors: C A Kühne; S Ruchholtz; C Buschmann; J Sturm; C K Lackner; A Wentzensen; B Bouillon; C Waydhas; C Weber Journal: Unfallchirurg Date: 2006-05 Impact factor: 1.000