Anesthesia-related perioperative adverse events during in-patient and out-patient procedures.

PURPOSE: This paper aims to determine the one-year incidence of, and risk factors for, perioperative adverse events during in-patient and out-patient anesthesia-assisted procedures. DESIGN/METHODOLOGY/APPROACH: A quality assurance database was the primary data source. Outcome variables were death and the occurrence of any adverse event. Risk factors were ASA physical status (PS), age, duration and type of anesthesia care, number of operating rooms running, concurrency level and medical staff. Data were stratified by in-patient or out-patient, surgical (e.g. thoracotomy) or non-surgical (e.g. electroconvulsive therapy), and were analyzed using Chi square, Fisher's exact test and generalized estimating equations. FINDINGS: Of 27,970 procedures, 49.8 percent were out-patient and greater than 80 percent were surgical. For surgical procedures, adverse event rates were higher for in-patient than out-patient procedures (2.11 percent vs. 1.45 percent; p < 0.001). For non-surgical procedures, adverse event rates were similar for in-patients and out-patients (0.54 percent vs. 0.36 percent). The types of adverseevents differed for in-patient and out-patient surgical procedures (p < 0.001), but not for non-surgical procedures. ASA PS, age, duration of anesthesia care, anesthesia type and medical staff assigned to the case were each associated with adverse event rates, but the association depended on the type of procedure. PRACTICAL IMPLICATIONS: In-patient and out-patient surgical procedures differ in the incidence of perioperative adverse events, and in risk factors, suggesting a need to develop separate monitoring strategies. ORIGINALITY/VALUE: The paper is the first to assess perioperative adverse events amongst in-patient and out-patient procedures.

Postoperative mortality after inpatient surgery: Incidence and risk factors.

PURPOSE: This study determined the incidence of and identified risk factors for 48 hour (h) and 30 day (d) postoperative mortality after inpatient operations. METHODS: A retrospective cohort study was conducted using Anesthesiology's Quality Indicator database as the main data source. The database was queried for data related to the surgical procedure, anesthetic care, perioperative adverse events, and birth/death/operation dates. The 48 h and 30 d cumulative incidence of postoperative mortality was calculated and data were analyzed using Chi-square or Fisher's exact test and generalized estimating equations. RESULTS: The 48 h and 30 d incidence of postoperative mortality was 0.57% and 2.1%, respectively. Higher American Society of Anesthesiologists physical status scores, extremes of age, emergencies, perioperative adverse events and postoperative Intensive Care Unit admission were identified as risk factors. The use of monitored anesthesia care or general anesthesia versus regional or combined anesthesia was a risk factor for 30 d postoperative mortality only. Time under anesthesia care, perioperative hypothermia, trauma, deliberate hypotension and invasive monitoring via arterial, pulmonary artery or cardiovascular catheters were not identified as risk factors. CONCLUSIONS: Our findings can be used to track postoperative mortality rates and to test preventative interventions at our institution and elsewhere.

Development and validation of a new index to measure emergency department crowding.

OBJECTIVES: To develop a quantitative measure of emergency department (ED) crowding and busyness. METHODS: A five-week study in spring 2002 in an urban teaching ED compared a new index (the Emergency Department Work Index [EDWIN]) with attending physician and nurse ratings of crowding. EDWIN is defined as summation operator n(i)t(i)/N(a)(B(T)-B(A)), where n(i) = number of patients in the ED in triage category i, t(i) = triage category, N(a) = number of attending physicians on duty, B(T) = number of treatment bays, and B(A) = number of admitted patients in the ED. The triage system used is the Emergency Severity Index (ESI), which was modified by reversing the ranking of triage categories; that is, an ESI score of 1 represented the least acute patient and 5 the sickest. EDWIN was calculated every two hours in a convenience sample of 60 eight-hour shifts. With each measurement, the charge attending physician and nurse estimated how busy/crowded the ED was, using a Likert scale. Nurse and physician assessments were averaged and compared with EDWIN scores. Data were analyzed with SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: A total of 2,647 patients aged 18 years and older were assessed at 225 time points over 35 consecutive days. Nurses and physicians showed good interrater agreement of crowding assessment (weighted kappa 0.61, 95% confidence interval = 0.53 to 0.69). Median EDWIN scores and interquartile ranges (IQRs) when the ED was rated as not busy, average, and very busy were 1.07 (IQR = 0.80 to 1.55), 1.55 (IQR = 1.16 to 1.93), and 1.83 (IQR = 1.42 to 2.45) (p < 0.001). The ED was on diversion for 17 time blocks (6.5% of all blocks), with a median EDWIN of 2.77 (IQR = 1.83 to 3.63), compared with an EDWIN of 1.45 (IQR = 1.05 to 2.00) when not on diversion (p < 0.001). EDWIN scores correlated weakly with various process-of-care measures chosen as secondary end points. CONCLUSIONS: EDWIN correlated well with staff assessment of ED crowding and diversion. The index can be programmed into tracking software for use as a "dashboard" to alert staff when the ED is approaching crisis. If validated across other sites, EDWIN may provide a tool to compare crowding levels among different EDs.