Kalkman J C, Visser K, Moen J, Bonsel J G, Grobbee E D, Moons M K G
Department of Anesthesiology, Division of Perioperative Care and Emergency Medicine, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands Department of Anesthesiology, Academic Medical Center, Amsterdam, The Netherlands Department of Public Health, Academic Medical Center, Amsterdam, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
Pain. 2003 Oct;105(3):415-423. doi: 10.1016/S0304-3959(03)00252-5.
We developed and validated a prediction rule for the occurrence of early postoperative severe pain in surgical inpatients, using predictors that can be easily documented in a preoperative setting. A cohort of surgical inpatients (n=1416) undergoing various procedures except cardiac surgery and intracranial neurosurgery in a University Hospital were studied. Preoperatively the following predictors were collected: age, gender, type of scheduled surgery, expected incision size, blood pressure, heart rate, Quetelet index, the presence and severity of preoperative pain, health-related quality of life the (SF-36), Spielberger's State-Trait Anxiety Inventory (STAI) and the Amsterdam Preoperative Anxiety and Information Scale (APAIS). The outcome was the presence of severe postoperative pain (defined as Numeric Rating Scale > or =8) within the first hour postoperatively. Multivariate logistic regression in combination with bootstrapping techniques (as a method for internal validation) was used to derive a stable prediction model. Independent predictors of severe postoperative pain were younger age, female gender, level of preoperative pain, incision size and type of surgery. The area under the receiver operator characteristic (ROC) curve was 0.71 (95% CI: 0.68-0.74). Adding APAIS scores (measures of preoperative anxiety and need for information), but not STAI, provided a slightly better model (ROC area 0.73). The reliability of this extended model was good (Hosmer and Lemeshow test p-value 0.78). We have demonstrated that severe postoperative pain early after awakening from general anesthesia can be predicted with a scoring rule, using a small set of variables that can be easily obtained from all patients at the preoperative visit. Before this internally validated preoperative prediction rule can be applied in clinical practice to support anticipatory pain management, external validation in other clinical settings is necessary.
我们开发并验证了一种针对外科住院患者术后早期严重疼痛发生情况的预测规则，所使用的预测指标能够在术前轻松记录。对某大学医院中接受除心脏手术和颅内神经外科手术之外各种手术的一组外科住院患者（n = 1416）进行了研究。术前收集了以下预测指标：年龄、性别、预定手术类型、预期切口大小、血压、心率、体重指数、术前疼痛的存在及严重程度、健康相关生活质量（SF - 36）、斯皮尔伯格状态 - 特质焦虑量表（STAI）以及阿姆斯特丹术前焦虑与信息量表（APAIS）。观察结果为术后第一小时内是否存在严重术后疼痛（定义为数字评分量表≥8）。采用多变量逻辑回归结合自抽样技术（作为内部验证的一种方法）来推导一个稳定的预测模型。术后严重疼痛的独立预测指标为年龄较小、女性、术前疼痛程度、切口大小和手术类型。受试者操作特征（ROC）曲线下面积为0.71（95%置信区间：0.68 - 0.74）。加入APAIS评分（术前焦虑和信息需求的度量指标）而非STAI，可得到一个稍好的模型（ROC面积0.73）。这个扩展模型的可靠性良好（Hosmer和Lemeshow检验p值0.78）。我们已经证明，使用一组可在术前访视时从所有患者轻松获取的少量变量，通过评分规则可以预测全身麻醉苏醒后早期的严重术后疼痛。在这个经过内部验证的术前预测规则能够应用于临床实践以支持预期性疼痛管理之前，有必要在其他临床环境中进行外部验证。
