BACKGROUND: We hypothesized that time of day of surgery would influence the incidence of anesthetic adverse events (AEs). METHODS: Clinical observations reported in a quality improvement database were categorized into different AEs that reflected (1) error, (2) harm, and (3) other AEs (error or harm could not be determined) and were analyzed for effects related to start hour of care. RESULTS: As expected, there were differences in the rate of AEs depending on start hour of care. Compared with a reference start hour of 7 am, other AEs were more frequent for cases starting during the 3 pm and 4 pm hours (p < 0.0001). Post hoc inspection of data revealed that the predicted probability increased from a low of 1.0% at 9 am to a high of 4.2% at 4 pm. The two most common event types (pain management and postoperative nausea and vomiting) may be primary determinants of these effects. CONCLUSIONS: Our results indicate that clinical outcomes may be different for patients anesthetized at the end of the work day compared with the beginning of the day. Although this may result from patient related factors, medical care delivery factors such as case load, fatigue, and care transitions may also be influencing the rate of anesthetic AEs for cases that start in the late afternoon.
BACKGROUND: We hypothesized that time of day of surgery would influence the incidence of anesthetic adverse events (AEs). METHODS: Clinical observations reported in a quality improvement database were categorized into different AEs that reflected (1) error, (2) harm, and (3) other AEs (error or harm could not be determined) and were analyzed for effects related to start hour of care. RESULTS: As expected, there were differences in the rate of AEs depending on start hour of care. Compared with a reference start hour of 7 am, other AEs were more frequent for cases starting during the 3 pm and 4 pm hours (p < 0.0001). Post hoc inspection of data revealed that the predicted probability increased from a low of 1.0% at 9 am to a high of 4.2% at 4 pm. The two most common event types (pain management and postoperative nausea and vomiting) may be primary determinants of these effects. CONCLUSIONS: Our results indicate that clinical outcomes may be different for patients anesthetized at the end of the work day compared with the beginning of the day. Although this may result from patient related factors, medical care delivery factors such as case load, fatigue, and care transitions may also be influencing the rate of anesthetic AEs for cases that start in the late afternoon.
Authors: M Sesmu Arbous; Anneke E E Meursing; Jack W van Kleef; Jaap J de Lange; Huub H A J M Spoormans; Paul Touw; Frans M Werner; Diederick E Grobbee Journal: Anesthesiology Date: 2005-02 Impact factor: 7.892
Authors: Kevin B Ricci; Amy P Rushing; Angela M Ingraham; Vijaya T Daniel; Anghela Z Paredes; Adrian Diaz; Victor K Heh; Holly E Baselice; Wendelyn M Oslock; Scott A Strassels; Heena P Santry Journal: J Trauma Acute Care Surg Date: 2019-10 Impact factor: 3.313
Authors: Edward J Tanner; Kara C Long; Qin Zhou; Rachel M Brightwell; Ginger J Gardner; Nadeem R Abu-Rustum; Mario M Leitao; Yukio Sonoda; Richard R Barakat; Alexia Iasonos; Dennis S Chi Journal: Gynecol Oncol Date: 2012-04-13 Impact factor: 5.482
Authors: Zirka H Anastasian; John G Gaudet; Laura C Levitt; Joanna L Mergeche; Eric J Heyer; Mitchell F Berman Journal: J Neurosurg Anesthesiol Date: 2014-04 Impact factor: 3.956