Sources of Variation in Anesthetic Drug Costs.

BACKGROUND: Increasing attention has been focused on health care expenditures, which include anesthetic-related drug costs. Using data from 2 large academic medical centers, we sought to identify significant contributors to anesthetic drug cost variation. METHODS: Using anesthesia information management systems, we calculated volatile and intravenous drug costs for 8 types of inpatient surgical procedures performed from July 1, 2009, to December 31, 2011. For each case, we determined patient age, American Society of Anesthesiologists (ASA) physical status, gender, institution, case duration, in-room provider, and attending anesthesiologist. These variables were then entered into 2 fixed-effects linear regression models, both with logarithmically transformed case cost as the outcome variable. The first model included duration, attending anesthesiologist, patient age, ASA physical status, and patient gender as independent variables. The second model included case type, institution, patient age, ASA physical status, and patient gender as independent variables. When all variables were entered into 1 model, redundancy analyses showed that case type was highly correlated (R = 0.92) with the other variables in the model. More specifically, a model that included case type was no better at predicting cost than a model without the variable, as long as that model contained the combination of attending anesthesiologist and case duration. Therefore, because we were interested in determining the effect both variables had on cost, 2 models were created instead of 1. The average change in cost resulting from each variable compared to the average cost of the reference category was calculated by first exponentiating the ß coefficient and subtracting 1 to get the percent difference in cost. We then multiplied that value by the mean cost of the associated reference group. RESULTS: A total of 5504 records were identified, of which 4856 were analyzed. The median anesthetic drug cost was $38.45 (25th percentile = $23.23, 75th percentile = $63.82). The majority of the variation was not described by our models-35.2% was explained in the model containing case duration, and 32.3% was explained in the model containing case type. However, the largest sources of variation our models identified were attending anesthesiologist, case type, and procedure duration. With all else held constant, the average change in cost between attending anesthesiologists ranged from a cost decrease of $41.25 to a cost increase of $95.67 (10th percentile = -$19.96, 90th percentile = +$20.20) when compared to the provider with the median value for mean cost per case. The average change in cost between institutions was significant but minor ($5.73). CONCLUSIONS: The majority of the variation was not described by the models, possibly indicating high per-case random variation. The largest sources of variation identified by our models included attending anesthesiologist, procedure type, and case duration. The difference in cost between institutions was statistically significant but was minor. While many prior studies have found significant savings resulting from cost-reducing interventions, our findings suggest that because the overall cost of anesthetic drugs was small, the savings resulting from interventions focused on the clinical practice of attending anesthesiologists may be negligible, especially in institutions where access to more expensive drugs is already limited. Thus, cost-saving efforts may be better focused elsewhere.

Anticipated and unanticipated difficult airway management.

PURPOSE OF REVIEW: Management of difficult airway is far from optimal despite of continuous progress in science and technology. The purpose of this review is to summarize the current research in the field and bring readers up to date. RECENT FINDINGS: New technologies for intubation make providers more confident to handle difficult airways, but there is lack of evidence indicating the reduction in incidence of 'cannot intubate cannot ventilate (CICV)'. Optimization of mask ventilation should reduce the incidence of difficult mask ventilation but it is greatly underappreciated. Even optimization of preoxygenation is not directly associated with any decreased incidence of difficult airway, but it prolongs time of safe apnea oxygenation; therefore, is likely to improve the outcome of the patients if CICV occurs. SUMMARY: Improvement of managing difficult airway relies on optimized mask ventilation, utilization of the appropriate tools for intubation, maximization of the safe apnea oxygenation time, prompt surgical airway in response to severe hypoxia in case effective noninvasive interventions are not achievable. It seems that a simplified and concise algorithm of difficult airway management needs to be established in order to enable providers to easily remember and execute.