Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts, USA.
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USA.
BMJ Qual Saf. 2020 Jun;29(6):472-481. doi: 10.1136/bmjqs-2018-008976. Epub 2019 Nov 8.
Surgical site infections (SSIs) are common costly hospital-acquired conditions. While statistical process control (SPC) use in healthcare has increased, limited rigorous empirical research compares and optimises these methods for SSI surveillance. We sought to determine which SPC chart types and design parameters maximise the detection of clinically relevant SSI rate increases while minimising false alarms.
Systematic retrospective data analysis and empirical optimisation.
We analysed 12 years of data on 13 surgical procedures from a network of 58 community hospitals. Statistically significant SSI rate increases (signals) at individual hospitals initially were identified using 50 different SPC chart variations (Shewhart or exponentially weighted moving average, 5 baseline periods, 5 baseline types). Blinded epidemiologists evaluated the clinical significance of 2709 representative signals of potential outbreaks (out of 5536 generated), rating them as requiring 'action' or 'no action'. These ratings were used to identify which SPC approaches maximised sensitivity and specificity within a broader set of 3600 individual chart variations (additional baseline variations and chart types, including moving average (MA), and five control limit widths) and over 32 million dual-chart combinations based on different baseline periods, reference data (network-wide vs local hospital SSI rates), control limit widths and other calculation considerations. Results were validated with an additional year of data from the same hospital cohort.
The optimal SPC approach to detect clinically important SSI rate increases used two simultaneous MA charts calculated using lagged rolling baseline windows and 1 SD limits. The first chart used 12-month MAs with 18-month baselines and best identified small sustained increases above network-wide SSI rates. The second chart used 6-month MAs with 3-month baselines and best detected large short-term increases above individual hospital SSI rates. This combination outperformed more commonly used charts, with high sensitivity (0.90; positive predictive value=0.56) and practical specificity (0.67; negative predictive value=0.94).
An optimised combination of two MA charts had the best performance for identifying clinically relevant small but sustained above-network SSI rates and large short-term individual hospital increases.
手术部位感染（SSI）是常见的高成本医院获得性疾病。虽然统计过程控制（SPC）在医疗保健中的应用有所增加，但很少有严格的实证研究对这些方法进行比较和优化，以用于 SSI 监测。我们旨在确定哪种 SPC 图表类型和设计参数可以最大程度地检测到临床相关的 SSI 率增加，同时最小化误报。
系统的回顾性数据分析和实证优化。
我们分析了来自 58 家社区医院网络的 12 年 13 种手术的数据。最初，使用 50 种不同的 SPC 图表变化（休哈特或指数加权移动平均，5 个基线期，5 个基线类型）在单个医院中确定具有统计学意义的 SSI 率增加（信号）。盲法传染病专家评估了 2709 个潜在暴发信号（5536 个信号中）的临床意义，将其评定为需要“采取行动”或“无需采取行动”。这些评分用于确定哪种 SPC 方法在更广泛的 3600 个单个图表变化范围内（包括移动平均（MA）和五个控制限宽度）以及基于不同基线期、参考数据（网络范围与医院 SSI 率）、控制限宽度和其他计算考虑因素的 3200 多万个双图表组合中，可以最大程度地提高敏感性和特异性。使用来自同一医院队列的额外一年数据对结果进行了验证。
检测临床重要的 SSI 率增加的最佳 SPC 方法是使用两个同时计算的 MA 图表，使用滞后滚动基线窗口和 1SD 限制。第一张图表使用 12 个月的 MA 和 18 个月的基线，最能识别出高于网络 SSI 率的小持续升高。第二张图表使用 6 个月的 MA 和 3 个月的基线，最能检测到高于单个医院 SSI 率的大短期升高。该组合的性能优于更常用的图表，具有较高的敏感性（0.90；阳性预测值=0.56）和实际特异性（0.67；阴性预测值=0.94）。
两种 MA 图表的最佳组合具有最佳性能，可用于识别临床相关的小但持续高于网络的 SSI 率以及大的短期个体医院升高。
