Estimates and confidence intervals for mean postoperative health-care-associated infections per week among operating rooms and other anesthetizing locations.

purposeFor anesthesia practitioners to contribute to reduced postoperative health-care-associated infections, they need to know which anesthetizing locations at their facilities have infections per week with confidence intervals (CI) exceeding relevant thresholds. We sought the development of more precise estimates and CIs.

methodsWe conducted a retrospective cohort study of the 2024 fiscal year of surgical cases at a large teaching hospital in Iowa (University of Iowa Health Care Medical Center, Iowa City, IA, USA), with 90-day postoperative infection codes from the International Classification of Diseases, 10th Revision, as diagnosed by surgical teams. Following earlier simulations, we used Poisson regression with heteroscedasticity-consistent robust variance estimation. We compared CI widths for new postoperative health-care-associated infections per week per room to the corresponding method of batch means, quantified as costs of Type I errors (e.g., unnecessary vs appropriate Staphylococcus aureus transmission monitoring) and Type II errors (e.g., failure to mitigate vs successful reductions in excess infections).

resultsThe 75 operating rooms and other anesthetizing locations ("rooms") studied had 1,095/42,978 (2.6%) cases with patients who developed postoperative infections. The 75 rooms × 366 days equaled 27,450 room days, with 96.2% having no patient who developed a postoperative infection (26,401), 3.7% having one infection, 0.2% having two infections, and 0.01% having three. There was no significant serial correlation between days by room or between rooms by day. The preceding case of a patient who developed a postoperative infection did not significantly increase the probability of the next case in the room being of a patient who developed an infection, all 75 Šidák-corrected P ≥ 0.32. Counts of infections per day were consistent with Poisson distributions for 73/75 rooms. The other two rooms had ratios of sample variances to sample means of 1.32 and 1.78, respectively. Among the 15 rooms with a mean of ≥ 0.50 infections per week, the 99% CIs with Poisson regression were narrower than using the method of batch means by approximately 35%. Pooling all rooms, the CI width was approximately 38% less. These reductions were equivalent to increasing sample sizes from 1.00 year to 2.35 and 2.60 years or reducing costs by 88% or 92%, respectively.

conclusionsPoisson regression with robust variance estimation resulted in more precise estimates than did the method of batch means, which will lower health care costs. We recommend reporting counts of postoperative health-care-associated infections per week for each anesthetizing location by using this method.