Department of Anesthesiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
Department of Anesthesiology, University of California Davis, Davis, California, USA.
BMJ Open Qual. 2024 Nov 13;13(4):e002947. doi: 10.1136/bmjoq-2024-002947.
Time and money are limited resources to pursue quality improvement (QI) goals. Computer simulation using Monte Carlo methods may help focus resources towards the most efficacious interventions to pursue.
This observational, cross-sectional study analysed the length of stay (LOS) for adult American Society of Anesthesiologists (ASA) 1-3 patients in the postanaesthesia care unit (PACU) at a major academic medical centre. Data were collected retrospectively from 1 April 2023 to 31 March 2024. Statistical analysis with Monte Carlo methods simulated the per cent reduction in PACU LOS following the elimination of postoperative nausea and vomiting (PONV), hypothermia (initial temperature<36°C), severe pain (pain score≥7) or moderate opioid use (≥ 50 mcg fentanyl or≥0.4 mg hydromorphone).
The PACU LOS of 7345 patients were included in this study. PONV was experienced by 10.29% of patients and was associated with a mean PACU LOS of 96.64 min (±33.98 min). Hypothermia was the least frequent complication, experienced by 8.93% of patients and was associated with a mean PACU LOS of 83.55 min (±35.99 min). Severe pain and moderate opioid use were seen in 34.05% and 40.83% of patients, respectively and were associated with PACU LOS that were shorter than those experienced by patients with PONV. Monte Carlo simulations demonstrated that the greatest impact on PACU LOS (12.5% (95% CI 12.0% to 13.0%)) would result from the elimination of moderate opioid use.
Although PONV was associated with the longest PACU LOS, statistical simulation with Monte Carlo methods demonstrated the greatest per cent reduction in PACU LOS would result from the elimination of moderate opioid use, thus indicating the most efficacious project to pursue.
Statistical simulation with Monte Carlo methods can help guide QI teams to the most efficacious project or intervention to pursue.
时间和资金是有限的资源，用于追求质量改进（QI）目标。使用蒙特卡罗方法进行计算机模拟可以帮助将资源集中用于最有效的干预措施。
这项观察性、横断面研究分析了在一家主要学术医疗中心的麻醉后护理单元（PACU）中，美国麻醉医师协会（ASA）1-3 级成年患者的住院时间（LOS）。数据从 2023 年 4 月 1 日至 2024 年 3 月 31 日回溯收集。使用蒙特卡罗方法进行的统计分析模拟了消除术后恶心和呕吐（PONV）、低体温（初始体温<36°C）、严重疼痛（疼痛评分≥7）或中度阿片类药物使用（≥50mcg 芬太尼或≥0.4mg 氢吗啡酮）后 PACU LOS 的百分比减少。
这项研究共纳入了 7345 名 PACU 患者。10.29%的患者发生 PONV，其 PACU LOS 平均为 96.64 分钟（±33.98 分钟）。低体温是最不常见的并发症，8.93%的患者发生低体温，其 PACU LOS 平均为 83.55 分钟（±35.99 分钟）。严重疼痛和中度阿片类药物使用分别见于 34.05%和 40.83%的患者，其 PACU LOS 短于发生 PONV 的患者。蒙特卡罗模拟显示，消除中度阿片类药物使用对 PACU LOS 的影响最大（12.5%（95%CI 12.0%至 13.0%））。
虽然 PONV 与最长的 PACU LOS 相关，但蒙特卡罗方法的统计模拟显示，消除中度阿片类药物使用可使 PACU LOS 百分比降低最大，从而表明最有效的项目应予以实施。
使用蒙特卡罗方法进行统计模拟可以帮助 QI 团队确定最有效的项目或干预措施。
