Research on Healthcare Performance RESHAPE, INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France.
Health Data Department, Hospices Civils de Lyon, Lyon, France.
PLoS One. 2024 May 15;19(5):e0303543. doi: 10.1371/journal.pone.0303543. eCollection 2024.
Statistical Process Control (SPC) tools providing feedback to surgical teams can improve patient outcomes over time. However, the quality of routinely available hospital data used to build these tools does not permit full capture of the influence of patient case-mix. We aimed to demonstrate the value of considering time-related variables in addition to patient case-mix for detection of special cause variations when monitoring surgical outcomes with control charts.
A retrospective analysis from the French nationwide hospital database of 151,588 patients aged 18 and older admitted for colorectal surgery between January 1st, 2014, and December 31st, 2018. GEE multilevel logistic regression models were fitted from the training dataset to predict surgical outcomes (in-patient mortality, intensive care stay and reoperation within 30-day of procedure) and applied on the testing dataset to build control charts. Surgical outcomes were adjusted on patient case-mix only for the classical chart, and additionally on secular (yearly) and seasonal (quarterly) trends for the enhanced control chart. The detection of special cause variations was compared between those charts using the Cohen's Kappa agreement statistic, as well as sensitivity and positive predictive value with the enhanced chart as the reference.
Within the 5-years monitoring period, 18.9% (28/148) of hospitals detected at least one special cause variation using the classical chart and 19.6% (29/148) using the enhanced chart. 59 special cause variations were detected overall, among which 19 (32.2%) discordances were observed between classical and enhanced charts. The observed Kappa agreement between those charts was 0.89 (95% Confidence Interval [95% CI], 0.78 to 1.00) for detecting mortality variations, 0.83 (95% CI, 0.70 to 0.96) for intensive care stay and 0.67 (95% CI, 0.46 to 0.87) for reoperation. Depending on surgical outcomes, the sensitivity of classical versus enhanced charts in detecting special causes variations ranged from 0.75 to 0.89 and the positive predictive value from 0.60 to 0.89.
Seasonal and secular trends can be controlled as potential confounders to improve signal detection in surgical outcomes monitoring over time.
为了改善患者的治疗效果，为外科团队提供反馈的统计过程控制（SPC）工具应运而生。然而，用于构建这些工具的常规医院数据的质量并不能完全捕捉患者病例组合的影响。我们旨在展示在使用控制图监测外科手术结果时，除了患者病例组合外，考虑时间相关变量对于检测特殊原因变异的价值。
这是一项来自法国全国医院数据库的回顾性分析，共纳入 2014 年 1 月 1 日至 2018 年 12 月 31 日期间年龄在 18 岁及以上接受结直肠手术的 151588 例患者。使用广义估计方程多水平逻辑回归模型从训练数据集预测手术结果（住院死亡率、重症监护停留时间和术后 30 天内再次手术），并将其应用于测试数据集来构建控制图。对于经典图表，仅根据患者病例组合对手术结果进行调整，而对于增强型图表，则根据季节性（季度）和季节性（年度）趋势进行调整。使用 Cohen's Kappa 一致性统计量比较这些图表之间的特殊原因变异检测，以及使用增强型图表作为参考的敏感性和阳性预测值。
在 5 年监测期间，使用经典图表检测到至少 1 个特殊原因变异的医院占 18.9%（28/148），使用增强型图表检测到的医院占 19.6%（29/148）。总共检测到 59 个特殊原因变异，其中经典图表和增强图表之间观察到 19 个（32.2%）不一致。这些图表之间的观察到的 Kappa 一致性为 0.89（95%置信区间[95%CI]，0.78 至 1.00），用于检测死亡率变化，0.83（95%CI，0.70 至 0.96）用于重症监护停留时间，0.67（95%CI，0.46 至 0.87）用于再次手术。根据手术结果，经典图表与增强图表在检测特殊原因变异方面的敏感性范围为 0.75 至 0.89，阳性预测值范围为 0.60 至 0.89。
季节性和季节性趋势可以作为潜在的混杂因素进行控制，以改善随时间推移的外科手术结果监测中的信号检测。
