目的:开发列线图,为识别腹部手术后有疼痛风险的患者提供筛查工具。
背景:急性术后疼痛的风险预测模型可以启动预防策略,这对术后疼痛管理和恢复是有价值的。尽管关于危险因素的研究越来越多,不同研究的结果不一致.此外,很少有研究全面探索术后急性疼痛的预测因素并建立预测模型。
方法:一项前瞻性观察性研究。
方法:从2022年6月至2022年12月,共有352名接受腹部手术的患者参加了这项调查。根据二元逻辑回归的结果,开发了一个列线图来预测腹部手术后急性疼痛的可能性。通过辨别和校准来评估列线图的预测性能。内部验证通过Bootstrap进行1000次重新采样。
结果:共有139例患者在腹部手术后出现急性术后疼痛,发病率为39.49%。年龄<60岁,婚姻状况(未婚,离婚,或丧偶),术中瑞芬太尼的消耗量>2mg,留置引流管,睡眠质量差,高度痛苦的灾难,低疼痛自我效能感,和未使用PCIA是腹部手术后患者疼痛控制不足的预测因素.使用这些变量,我们开发了一个列线图模型。所有测试指标表明,该模型具有可靠的判别和校准。
结论:本研究建立了一个在线动态预测模型,可以对腹部手术后的急性疼痛进行个性化风险评估。我们的模型具有良好的区分和校准,并在内部被验证为风险评估的有用工具。
结论:构建的列线图模型可以成为预测腹部手术患者术后急性疼痛风险的实用工具。这将有助于实现术后疼痛的个性化管理和预防策略。
■本研究采用了加强流行病学观察研究报告(STROBE)指南。
■手术前,研究组成员访问了符合纳入标准的患者,并向他们解释了研究的目的和范围.知情同意后,他们完成了问卷。术后前3天由床边护士定期评估和记录患者疼痛评分(VAS)。其他信息来自医疗记录。
OBJECTIVE: To develop a nomogram to provide a screening tool for recognising patients at risk of post-operative pain undergoing abdominal operations.
BACKGROUND: Risk prediction models for acute post-operative pain can allow initiating prevention strategies, which are valuable for post-operative pain management and recovery. Despite the increasing number of studies on risk factors, there were inconsistent findings across different studies. In addition, few studies have comprehensively explored predictors of post-operative acute pain and built prediction models.
METHODS: A prospective observational study.
METHODS: A total of 352 patients undergoing abdominal operations from June 2022 to December 2022 participated in this investigation. A nomogram was developed for predicting the probability of acute pain after abdominal surgery according to the results of binary logistic regression. The nomogram\'s predictive performance was assessed by discrimination and calibration. Internal validation was performed via Bootstrap with 1000 re-samplings.
RESULTS: A total of 139 patients experienced acute post-operative pain following abdominal surgery, with an incidence of 39.49%. Age <60, marital status (unmarried, divorced, or widowed), consumption of intraoperative remifentanil >2 mg, indwelling of drainage tubes, poor quality sleep, high pain catastrophizing, low pain self-efficacy, and PCIA not used were predictors of inadequate pain control in patients after abdominal surgery. Using these variables, we developed a nomogram model. All tested indicators showed that the model has reliable discrimination and calibration.
CONCLUSIONS: This study established an online dynamic predictive model that can offer an individualised risk assessment of acute pain after abdominal surgery. Our model had good differentiation and calibration and was verified internally as a useful tool for risk assessment.
CONCLUSIONS: The constructed nomogram model could be a practical tool for predicting the risk of experiencing acute post-operative pain in patients undergoing abdominal operations, which would be helpful to realise personalised management and prevention strategies for post-operative pain.
UNASSIGNED: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were adopted in this study.
UNASSIGNED: Before the surgery, research group members visited the patients who met the inclusion criteria and explained the purpose and scope of the study to them. After informed consent, they completed the questionnaire. The patients\' pain scores (VAS) were regularly assessed and documented by the bedside nurse for the first 3 days following surgery. Other information was obtained from medical records.