Jean Guglielminotti1, Guohua Li. 1. From the Department of Anesthesiology, Columbia University College of Physicians and Surgeons, New York, New York (J.G., G.L.); Département d'Anesthésie-Réanimation, APHP, Hôpital Bichat-Claude Bernard, Paris, France (J.G.); INSERM, UMR 1137, IAME, Paris, France (J.G.); and Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York (G.L.).
Abstract
BACKGROUND: The rate of anesthesia-related adverse events (ARAEs) is recommended for monitoring patient safety across hospitals. To ensure comparability, it is adjusted for patients' characteristics with logistic models (i.e., risk adjustment). The rate adjusted for patient-level characteristics and hospital affiliation through multilevel modeling is suggested as a better metric. This study aims to assess a multilevel model-based rate of ARAEs. METHODS: Data were obtained from the State Inpatient Database for New York 2008-2011. Discharge records for labor and delivery and ARAEs were identified with International Classification of Diseases, Ninth Revision, Clinical Modification codes. The rate of ARAEs for each hospital during 2008-2009 was calculated using both the multilevel and the logistic modeling approaches. Performance of the two methods was assessed with (1) interhospital variability measured by the SD of the rates; (2) reclassification of hospitals; and (3) prediction of hospital performance in 2010-2011. Rankability of each hospital was assessed with the multilevel model. RESULTS: The study involved 466,442 discharge records in 2008-2009 from 144 hospitals. The overall observed rate of ARAEs in 2008-2009 was 4.62 per 1,000 discharges [95% CI, 4.43 to 4.82]. Compared with risk adjustment, multilevel modeling decreased SD of ARAE rates from 4.7 to 1.3 across hospitals, reduced the proportion of hospitals classified as good performers from 18% to 10%, and performed similarly well in predicting future ARAE rates. Twenty-six hospitals (18%) were nonrankable due to inadequate reliability. CONCLUSION: The multilevel modeling approach could be used as an alternative to risk adjustment in monitoring obstetric anesthesia safety across hospitals.
BACKGROUND: The rate of anesthesia-related adverse events (ARAEs) is recommended for monitoring patient safety across hospitals. To ensure comparability, it is adjusted for patients' characteristics with logistic models (i.e., risk adjustment). The rate adjusted for patient-level characteristics and hospital affiliation through multilevel modeling is suggested as a better metric. This study aims to assess a multilevel model-based rate of ARAEs. METHODS: Data were obtained from the State Inpatient Database for New York 2008-2011. Discharge records for labor and delivery and ARAEs were identified with International Classification of Diseases, Ninth Revision, Clinical Modification codes. The rate of ARAEs for each hospital during 2008-2009 was calculated using both the multilevel and the logistic modeling approaches. Performance of the two methods was assessed with (1) interhospital variability measured by the SD of the rates; (2) reclassification of hospitals; and (3) prediction of hospital performance in 2010-2011. Rankability of each hospital was assessed with the multilevel model. RESULTS: The study involved 466,442 discharge records in 2008-2009 from 144 hospitals. The overall observed rate of ARAEs in 2008-2009 was 4.62 per 1,000 discharges [95% CI, 4.43 to 4.82]. Compared with risk adjustment, multilevel modeling decreased SD of ARAE rates from 4.7 to 1.3 across hospitals, reduced the proportion of hospitals classified as good performers from 18% to 10%, and performed similarly well in predicting future ARAE rates. Twenty-six hospitals (18%) were nonrankable due to inadequate reliability. CONCLUSION: The multilevel modeling approach could be used as an alternative to risk adjustment in monitoring obstetric anesthesia safety across hospitals.
Authors: Gary Abel; Catherine L Saunders; Silvia C Mendonca; Carolynn Gildea; Sean McPhail; Georgios Lyratzopoulos Journal: BMJ Qual Saf Date: 2017-08-28 Impact factor: 7.035