作者：牛冰，席从林，陈伟

单位：

1.223002江苏省淮安市第二人民医院神经内科　2.222300江苏省连云港市东海县中医院脑病科

Units：

1.Department of Neurology, Huai'an Second People's Hospital, Huai'an 223002, China 2.Department of Encephalopathy, Donghai County Traditional Chinese Medicine Hospital, Donghai 222300, China

关键词：

蛛网膜下腔出血；动脉瘤性蛛网膜下腔出血；疲乏；影响因素分析；列线图

Keywords：

Subarachnoid hemorrhage; Aneurysmal subarachnoid hemorrhage; Fatigue; Root cause analysis; Nomogram

CLC：

R 743.35

DOI：

10.12114/j.issn.1008-5971.2023.00.209

Funds：

江苏省卫生健康委2019年度医学科研立项项目（Z2019060）

摘要：

目的　探讨动脉瘤性蛛网膜下腔出血（aSAH）患者发生疲乏的影响因素，构建其风险预测列线图模 型并进行验证。方法　选取2020年3月至2022年10月淮安市第二人民医院收治的aSAH患者153例为调查对象，采用一 般资料调查表、病情及围术期资料调查表、社会支持评定量表（SSRS）、疲劳严重度量表（FSS）于患者出院后6个月 对其进行调查。根据是否发生疲乏将患者分成疲乏组（FSS评分≥4.0分）和非疲乏组（FSS评分＜4.0分）。采用多因 素Logistic回归分析探讨aSAH患者发生疲乏的影响因素，采用R 3.6.3软件建立aSAH患者发生疲乏的风险预测列线图模 型；采用Hosmer-Lemeshoe拟合优度检验评价该列线图模型的拟合程度，采用ROC曲线分析该列线图模型对aSAH患者 发生疲乏的预测价值，绘制校准曲线以评估该列线图模型预测aSAH患者发生疲乏的效能。结果　153例aSAH患者FSS 评分为2～6分，平均（4.1±0.7）分，发生疲乏61例（39.9%）、未发生疲乏92例（60.1%）。两组年龄、Hunt-Hess 分级、有术后并发症者占比、睡眠状况、社会支持情况比较，差异有统计学意义（P＜0.05）。多因素Logistic回归分 析结果显示，Hunt-Hess分级为Ⅲ～Ⅳ级、有术后并发症、睡眠状况差、低或一般社会支持为aSAH患者发生疲乏的危 险因素（P＜0.05）。基于多因素Logistic回归分析结果，构建aSAH患者发生疲乏的风险预测列线图模型，结果显示， Hunt-Hess分级为Ⅲ～Ⅳ级时，赋予80.5分；有术后并发症时，赋予100.0分；睡眠状况差时，赋予 73.0分；低或一般社 会支持时，赋予67.5分。Hosmer-Lemeshoe拟合优度检验结果显示，该列线图模型拟合较好（χ 2 =9.475 ，P=0.252）。 ROC曲线分析结果显示，该列线图模型预测aSAH患者发生疲乏的AUC为0.792〔95%CI（0.708，0.875）〕。校准曲线 分析结果显示，该列线图模型预测aSAH患者发生疲乏的校准曲线贴近理想曲线。结论　Hunt-Hess分级为Ⅲ～Ⅳ级、 有术后并发症、睡眠状况差、低或一般社会支持为aSAH患者发生疲乏的危险因素，基于上述因素构建的列线图模型对 aSAH患者发生疲乏具有一定预测价值，可为aSAH患者疲乏预防策略的制定提供指导依据。

Abstract：

Objective To explore the influencing factors of fatigue in patients with aneurysmal subarachnoid hemorrhage (aSAH) and construct and validate a nomogram model for predicting its risk. Methods A total of 153 patients with aSAH admitted to the Huai'an Second People's Hospital from March 2020 to October 2022 were selected as the research subjects. The General Data Questionnaire, Condition and Perioperative Data Questionnaire, Social Support Rating Scale (SSRS) , Fatigue Severity Scale (FSS) were used to investigate the patients at 6 months after discharge. The patients were divided into fatigue group (FSS score ≥ 4.0 points) and non-fatigue group (FSS score < 4.0 points) according to whether fatigue occurred in patients. Multivariate Logistic regression analysis was used to analyze the influencing factors of fatigue in patients with aSAH. The nomogram model for predicting the risk of fatigue in patients with aSAH was constructed by using the R 3.6.3 software. Hosmer-Lemeshoe goodness of fit test was used to evaluate the fitting degree of the nomogram model. The ROC curve was used to analyze the predictive value of the nomogram model for fatigue in patients with aSAH, and calibration curve was drawn to evaluate the effectiveness of the nomogram model for predicting fatigue in patients with aSAH. Results The FSS score of 153 patients with aSAH was 2 to 6, with an average of (4.1±0.7) . There were 61 patients (39.9%) with fatigue and 92 patients (60.1%) without fatigue. There were significant differences in age, Hunt-Hess grade, the proportion of postoperative complications, sleep status and social support between the two groups (P < 0.05) . Multivariate Logistic regression analysis showed that Hunt-Hess grade of Ⅲ to Ⅳ, postoperative complications, poor sleep status, low or general social support were the risk factors of fatigue in patients with aSAH (P < 0.05) . The nomogram model for predicting fatigue in patients with aSAH was constructed based on the multivariate Logistic regression analysis results, the results showed that when the Hunt-Hess grade was Ⅲ to Ⅳ, 80.5 points were assigned; when postoperative complications occurred, 100.0 points were assigned; when sleep status were poor, 73.0 points were assigned; when social support was low or general, 67.5 points were assigned. The results of Hosmer-Lemeshoe goodness of fit test showed that the nomogram model fitted well ( χ 2 =9.475, P=0.252) . The results of ROC curve analysis showed that the AUC of the nomogram model for predicting fatigue in patients with aSAH was 0.792 [95%CI (0.708, 0.875) ] . The results of calibration curve analysis showed that the calibration curve of the nomogram model for predicting fatigue in patients with aSAH was close to the ideal curve. Conclusion Hunt-Hess grade of Ⅲ to Ⅳ, postoperative complications, poor sleep status, low or general social support are the risk factors of fatigue in patients with aSAH. The nomogram model constructed based on the above factors has a certain predictive value for fatigue in patients with aSAH, and can provide guidance for the development of fatigue prevention strategies in aSAH patients.

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