Department of Anesthesiology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan Province, China.
Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Luzhou, Sichuan Province, China.
PLoS One. 2024 Apr 2;19(4):e0297337. doi: 10.1371/journal.pone.0297337. eCollection 2024.
With the improvement of medical level, the number of elderly patients is increasing, and the postoperative outcome of the patients cannot be ignored. However, there have been no studies on the relationship between preoperative heart rate variability (HRV) and Perioperative Neurocognitive Disorders (PND). The purpose of this study was to explore the correlation between (HRV) and (PND), postoperative intensive care unit (ICU), and hospital stay in patients undergoing non-cardiac surgery.
This retrospective analysis included 687 inpatients who underwent 24-hour dynamic electrocardiogram examination in our six departments from January 2021 to January 2022. Patients were divided into two groups based on heart rate variability (HRV): high and low. Possible risk factors of perioperative outcomes were screened using univariate analysis, and risk factors were included in multivariate logistic regression to screen for independent risk factors. The subgroup analysis was carried out to evaluate the robustness of the results. The nomogram of PND multi-factor logistic prediction model was constructed. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve was drawn by bootstrap resampling 1000 times for internal verification to evaluate the prediction ability of nomogram.
A total of 687 eligible patients were included. The incidence of low HRV was 36.7% and the incidence of PND was 7.6%. The incidence of PND in the low HRV group was higher than that in the high HRV group (11.8% vs 5.2%), the postoperative ICU transfer rate was higher (15.9% than 9.3%P = 0.009), and the hospital stay was longer [15 (11, 19) vs (13), 0.015]. The multivariable logistic regression analysis showed that after adjusting for other factors, decreased low HRV was identified as an independent risk factor for the occurrence of PND (Adjusted Odds Ratio = 2.095; 95% Confidence Interval: 1.160-3.784; P = 0.014) and postoperative ICU admission (Adjusted Odds Ratio = 1.925; 95% Confidence Interval: 1.128-3.286; P = 0.016). This study drew a nomogram column chart for a multivariate logistic regression model, incorporating age and HRV. The calibration curve shows that the predicted value of the model for the occurrence of cardio-cerebrovascular events is in good agreement with the actual observed value, with C-index of 0.696 (95% CI: 0.626 ~ 0.766). Subgroup analysis showed that low HRV was an independent risk factor for PND in patients with gastrointestinal surgery and ASA Ⅲ, aged ≥ 65 years.
In patients undergoing non-cardiac surgery, the low HRV was an independent risk factor for PND and postoperative transfer to the ICU, and the hospitalization time of patients with low HRV was prolonged. Through establishing a risk prediction model for the occurrence of PND, high-risk patients can be identified during the perioperative period for early intervention.
随着医疗水平的提高,老年患者的数量不断增加,患者的术后结果不容忽视。然而,目前还没有研究表明术前心率变异性(HRV)与围手术期神经认知障碍(PND)之间的关系。本研究旨在探讨非心脏手术患者术前 HRV 与 PND、术后重症监护病房(ICU)入住和住院时间的相关性。
本回顾性分析纳入了 2021 年 1 月至 2022 年 1 月期间我院六个科室进行 24 小时动态心电图检查的 687 名住院患者。根据 HRV 将患者分为高低两组。使用单因素分析筛选围术期结局的可能危险因素,并使用多因素逻辑回归筛选危险因素,以筛选独立危险因素。进行亚组分析以评估结果的稳健性。构建 PND 多因素逻辑预测模型的列线图。通过 bootstrap 重采样 1000 次绘制接受者操作特征(ROC)曲线,并绘制校准曲线进行内部验证,以评估列线图的预测能力。
共纳入 687 名符合条件的患者。低 HRV 发生率为 36.7%,PND 发生率为 7.6%。低 HRV 组 PND 的发生率高于高 HRV 组(11.8%比 5.2%,P=0.009),术后 ICU 转率更高(15.9%比 9.3%,P=0.009),住院时间更长[15(11,19)比(13),0.015]。多因素逻辑回归分析表明,在调整其他因素后,低 HRV 降低被确定为 PND 发生(调整比值比=2.095;95%置信区间:1.160-3.784;P=0.014)和术后 ICU 入住(调整比值比=1.925;95%置信区间:1.128-3.286;P=0.016)的独立危险因素。本研究绘制了一个包含年龄和 HRV 的多因素逻辑回归模型的列线图。校准曲线显示,该模型对心脑血管事件发生的预测值与实际观察值吻合良好,C 指数为 0.696(95%CI:0.626~0.766)。亚组分析表明,低 HRV 是胃肠道手术和 ASA Ⅲ级、年龄≥65 岁患者 PND 的独立危险因素。
在非心脏手术患者中,低 HRV 是 PND 和术后转入 ICU 的独立危险因素,低 HRV 患者的住院时间延长。通过建立 PND 发生风险预测模型,可以在围术期识别高危患者,以便进行早期干预。