【罂粟摘要】预测大手术后的剧烈疼痛：围手术期质量改进计划 (PQIP) 数据集的二次分析

预测大手术后的剧烈疼痛：围手术期质量改进计划 (PQIP) 数据集的二次分析

贵州医科大学    麻醉与心脏电生理课题组

翻 译：柏 雪  编 辑：柏 雪  审 校：曹 莹

背景：急性术后疼痛很常见，令人痛苦并且与发病率增加有关。有针对性的干预可以阻止其发展。我们的目标是开发并在内部验证一种预测工具，以预先识别大手术后导致严重疼痛风险的患者。

方法：我们分析了来自英国围手术期质量改进计划的数据，以开发和验证逻辑回归模型：主要结局为使用术前变量预测术后第一天的剧烈疼痛。二次分析包括使用围手术期变量。这项研究纳入了17079例接受大手术的患者数据，3140 名 (18.4%) 患者报告有剧烈疼痛，其中在女性、癌症患者或胰岛素依赖型糖尿病患者、术前吸烟者和服用基础阿片类药物的患者中更为普遍。

结果：我们的最终模型包括 25 个术前预测因子，其校正C统计量为 0.66 且校准良好（平均绝对误差 0.005，p = 0.35）。决策曲线分析提出了 20-30% 预测风险的最佳临界值来识别高风险个体。潜在可改变的风险因素包括吸烟状况和患者的心理健康指标。不可改变的因素包括患者年龄、性别及手术因素。通过添加术中变量（似然比 χ2 496.5，p < 0.001）来增强鉴别能力，但不是通过添加基础阿片类药物的使用数据。

结论：在内部验证中，我们的术前预测模型经过了很好的校准，但辨别力适中。纳入围手术期协变量后性能得到改善，这表明单独的术前变量不足以充分预测术后疼痛。

原始文献来源：R. A. Armstrong,  A. Fayaz,  G. L. P. Manning, et, al.  Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset. Anaesthesia 2023 doi:10.1111/anae.

英文原文：

Predicting severe pain after major surgery: a secondary analysis of the Peri-operative Quality Improvement Programme (PQIP) dataset.

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to preemptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Perioperative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulindependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 preoperative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20–30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain.

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