[白细胞衍生标记物对心脏瓣膜手术后谵妄的预测价值]。

Q3 Medicine
Xintian Zhang, Yanhu Ge, Dongni Zhang, Jun Ma
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Multivariate Logistic regression analysis showed that CCI score [odds ratio (OR) = 1.394, 95% confidence interval (95%CI) was 1.038-1.872, P = 0.027], perioperative atrial fibrillation (OR = 3.697, 95%CI was 1.711-7.990, P < 0.001), duration of cardiopulmonary bypass (OR = 1.008, 95%CI was 1.002-1.015, P = 0.016), length of ICU stay (OR = 1.006, 95%CI was 1.002-1.010, P = 0.002), NLR difference (OR = 1.029, 95%CI was 1.009-1.050, P = 0.005) and PWR difference (OR = 1.044, 95%CI was 1.009-1.080, P = 0.013) were independently correlated with POD. POD predictive model was constructed by multivariate Logistic regression analysis result: POD predictive model index = -4.970+0.336×CCI score+1.317×perioperative atrial fibrillation+0.009×duration of cardiopulmonary bypass+0.006×length of ICU stay+0.030×NLR difference+0.044×PWR difference. ROC curve analysis showed that the area under the ROC curve (AUC) of NLR difference for predicting POD was 0.659 (95%CI was 0.583-0.735), the optimal critical value was 16.62, the sensitivity was 60.2%, and the specificity was 70.4% (P < 0.05). The AUC of PWR difference for predicting POD was 0.608 (95%CI was 0.528-0.688), the optimal critical value was 25.68, the sensitivity was 51.8%, and the specificity was 75.7% (P < 0.05). 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引用次数: 0

摘要

目的探讨白细胞衍生标记物对心脏瓣膜手术患者术后谵妄(POD)的预测价值:方法:进行了一项前瞻性队列研究。研究对象为 2021 年 10 月至 2023 年 3 月在首都医科大学附属北京安贞医院接受心脏瓣膜手术的患者。研究人员收集了患者的人口统计学、基线和围手术期数据,并计算了患者术前和术后 24 小时内的中性粒细胞与淋巴细胞比值(NLR)和血小板与白细胞比值(PWR)。对术后 1-5 天内或 5 天内出院的患者每天进行两次谵妄评估。根据评估结果,将患者分为谵妄组和非谵妄组。比较两组患者的临床指标。采用多元 Logistic 回归分析筛选 POD 的独立危险因素,并建立 POD 预测模型。结果:共有 235 名患者参与分析,其中 83 名患者患有 POD(35.32%),152 名患者未患有 POD(64.68%)。与非谵妄组相比,谵妄组患者的夏尔森合并症指数(CCI)评分较高,迷你精神状态检查(MMSE)评分较低。在围手术期数据方面,与非谵妄组相比,谵妄组患者的手术时间、心肺旁路时间、重症监护室(ICU)住院时间、机械通气时间和术后住院时间更长,围手术期心房颤动发生率更高,出院生活评分更低。在白细胞衍生标记物方面,两组患者术后 24 小时内的 NLR 均显著高于术前,而 PWR 则显著低于术前。谵妄组术后24小时内的NLR、PWR差异和NLR差异均明显高于非谵妄组。多变量逻辑回归分析显示,CCI 评分[几率比(OR)= 1.394,95% 置信区间(95%CI)为 1.038-1.872,P = 0.027]、围手术期心房颤动(OR = 3.697,95%CI 为 1.711-7.990,P < 0.001)、心肺旁路持续时间(OR = 1.008,95%CI为1.002-1.015,P = 0.016)、ICU住院时间(OR = 1.006,95%CI为1.002-1.010,P = 0.002)、NLR差异(OR = 1.029,95%CI为1.009-1.050,P = 0.005)和PWR差异(OR = 1.044,95%CI为1.009-1.080,P = 0.013)与POD独立相关。多元 Logistic 回归分析结果构建了 POD 预测模型:POD预测模型指数=-4.970+0.336×CCI评分+1.317×围手术期心房颤动+0.009×心肺旁路时间+0.006×ICU住院时间+0.030×NLR差异+0.044×PWR差异。ROC 曲线分析显示,NLR 差值预测 POD 的 ROC 曲线下面积(AUC)为 0.659(95%CI 为 0.583-0.735),最佳临界值为 16.62,灵敏度为 60.2%,特异度为 70.4%(P < 0.05)。预测 POD 的 PWR 差值的 AUC 为 0.608(95%CI 为 0.528-0.688),最佳临界值为 25.68,灵敏度为 51.8%,特异度为 75.7%(P < 0.05)。POD预测模型预测POD的AUC为0.805(95%CI为0.745-0.865),最佳临界值为0.39,敏感性为74.7%,特异性为79.6%(P<0.05):NLR和PWR的差异与POD独立相关,这对预测心脏瓣膜手术后的POD具有潜在价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Predictive value of leukocyte derived markers for postoperative delirium after cardiac valve surgery].

Objective: To explore the predictive value of leukocyte derived markers for postoperative delirium (POD) in patients undergoing cardiac valve surgery.

Methods: A prospective cohort study was conducted. The patients who underwent cardiac valve surgery admitted to Beijing Anzhen Hospital of Capital Medical University from October 2021 to March 2023 were enrolled. The demographic, baseline and perioperative data were collected, and the neutrophil to lymphocyte ratio (NLR) and platelet to white blood cell ratio (PWR) were calculated before operation and within 24 hours after operation. Delirium assessment was conducted twice a day for patients within 1-5 days after surgery or discharged within 5 days. According to the evaluation results, the patients were divided into delirium group and non-delirium group. The clinical indexes between the two groups were compared. Multivariate Logistic regression analysis was used to screen the independent risk factors of POD, and the POD predictive model was constructed. The predictive value of POD predictive model was evaluated by receiver operator characteristic curve (ROC curve).

Results: A total of 235 patients were enrolled in the analysis, of which 83 patients had POD (35.32%) and 152 patients did not have POD (64.68%). Compared with the non-delirious group, the patients in the delirious group had higher Charlson comorbidity index (CCI) score and lower mini-mental state examination (MMSE) score. In terms of perioperative data, compared with the non-delirium group, the patients in the delirium group had longer operative time, duration of cardiopulmonary bypass, length of intensive care unit (ICU) stay, duration of mechanical ventilation, and postoperative hospital stay, higher incidence of perioperative atrial fibrillation, and lower discharge life score. In terms of leukocyte derived markers, NLR within 24 hours after surgery in both groups were significantly higher than those before surgery, and PWR were significantly lower than those before surgery. The NLR within 24 hours after surgery, PWR difference and NLR difference in the delirium group were significantly higher than those in the non-delirium group. Multivariate Logistic regression analysis showed that CCI score [odds ratio (OR) = 1.394, 95% confidence interval (95%CI) was 1.038-1.872, P = 0.027], perioperative atrial fibrillation (OR = 3.697, 95%CI was 1.711-7.990, P < 0.001), duration of cardiopulmonary bypass (OR = 1.008, 95%CI was 1.002-1.015, P = 0.016), length of ICU stay (OR = 1.006, 95%CI was 1.002-1.010, P = 0.002), NLR difference (OR = 1.029, 95%CI was 1.009-1.050, P = 0.005) and PWR difference (OR = 1.044, 95%CI was 1.009-1.080, P = 0.013) were independently correlated with POD. POD predictive model was constructed by multivariate Logistic regression analysis result: POD predictive model index = -4.970+0.336×CCI score+1.317×perioperative atrial fibrillation+0.009×duration of cardiopulmonary bypass+0.006×length of ICU stay+0.030×NLR difference+0.044×PWR difference. ROC curve analysis showed that the area under the ROC curve (AUC) of NLR difference for predicting POD was 0.659 (95%CI was 0.583-0.735), the optimal critical value was 16.62, the sensitivity was 60.2%, and the specificity was 70.4% (P < 0.05). The AUC of PWR difference for predicting POD was 0.608 (95%CI was 0.528-0.688), the optimal critical value was 25.68, the sensitivity was 51.8%, and the specificity was 75.7% (P < 0.05). The AUC of POD predictive model for predicting POD was 0.805 (95%CI was 0.745-0.865), the optimal critical value was 0.39, the sensitivity was 74.7%, and the specificity was 79.6% (P < 0.05).

Conclusions: The differences of NLR and PWR are independently related to POD, which has potential value in predicting POD after cardiac valve surgery.

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Zhonghua wei zhong bing ji jiu yi xue
Zhonghua wei zhong bing ji jiu yi xue Medicine-Critical Care and Intensive Care Medicine
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