Operating Room, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
BMC Med Inform Decis Mak. 2024 Sep 16;24(1):257. doi: 10.1186/s12911-024-02671-4.
Elderly patients undergoing recovery from general anesthesia face a heightened risk of critical respiratory events (CREs). Despite this, there is a notable absence of effective predictive tools tailored to this specific demographic. This study aims to develop and validate a predictive model (nomogram) to address this gap. CREs pose significant risks to elderly patients during the recovery phase from general anesthesia, making it an important issue in perioperative care. With the increasing aging population and the complexity of surgical procedures, it is crucial to develop effective predictive tools to improve patient outcomes and ensure patient safety during post-anesthesia care unit (PACU) recovery.
A total of 324 elderly patients who underwent elective general anesthesia in a grade A tertiary hospital from January 2023 to June 2023 were enrolled. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was constructed and represented as a nomogram. Internal validation of the model was performed using Bootstrapping. This study followed the TRIPOD checklist for reporting.
The indicators included in the nomogram were frailty, snoring, patient-controlled intravenous analgesia (PCIA), emergency delirium and cough intensity at extubation. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.990 and 0.981 for the training set and internal validation set, respectively. The optimal cutoff value was determined to be 0.22, based on a Youden index of 0.911. The F1-score was 0.927, and the MCC was 0.896. The calibration curve, Brier score (0.046), and HL test demonstrated acceptable consistency between the predicted and actual results. DCA revealed high net benefits of the nomogram prediction across all threshold probabilities.
This study developed and validated a nomogram to identify elderly patients in the PACU who are at higher risk of CREs. The identified predictive factors included frailty condition, snoring syndrome, PCIA, emergency delirium, and cough intensity at extubation. By identifying patients at higher risk of CREs early on, medical professionals can implement targeted strategies to mitigate the occurrence of complications and provide better postoperative care for elderly patients recovering from general anesthesia.
接受全身麻醉恢复的老年患者面临更高的危急呼吸事件（CREs）风险。尽管如此，针对这一特定人群，缺乏有效的预测工具。本研究旨在开发和验证一种预测模型（诺模图）来解决这一差距。CREs 对全身麻醉恢复期间的老年患者构成重大风险，因此是围手术期护理中的一个重要问题。随着人口老龄化和手术程序的复杂性不断增加，开发有效的预测工具对于改善患者预后和确保麻醉后恢复单元（PACU）恢复期间患者安全至关重要。
本研究共纳入 2023 年 1 月至 6 月在一家 A 级三甲医院接受择期全身麻醉的 324 名老年患者。使用最小绝对收缩和选择算子（LASSO）回归识别危险因素。构建了多变量逻辑回归模型，并表示为诺模图。使用自举法对内模进行验证。本研究按照 TRIPOD 报告清单进行。
诺模图中包含的指标有虚弱、打鼾、患者自控静脉镇痛（PCIA）、紧急谵妄和拔管时咳嗽强度。该模型在训练集和内部验证集中的诊断性能均令人满意，AUC 值分别为 0.990 和 0.981。根据约登指数 0.911，确定最佳截断值为 0.22。F1 得分为 0.927，MCC 得分为 0.896。校准曲线、Brier 评分（0.046）和 HL 检验表明，预测结果与实际结果之间具有较好的一致性。DCA 显示诺模图预测在所有阈值概率下均具有较高的净收益。
本研究开发并验证了一种诺模图，用于识别 PACU 中发生 CREs 风险较高的老年患者。确定的预测因素包括虚弱状况、打鼾综合征、PCIA、紧急谵妄和拔管时咳嗽强度。通过早期识别出 CREs 风险较高的患者，医疗专业人员可以实施针对性策略来减轻并发症的发生，并为接受全身麻醉恢复的老年患者提供更好的术后护理。
