Tong Wang, Jiahui Hao, Jialei Zhou, Gang Chen, Haitao Shen, Qing Sun
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引用次数: 0
摘要
肺炎是动脉瘤性蛛网膜下腔出血(aSAH)患者常见的术后并发症,与预后不良和死亡率升高有关。本研究的目的是建立一个蛛网膜下腔出血患者术后肺炎(POP)的预测模型。研究人员对在苏州大学附属第一医院神经外科接受手术的 308 名 aSAH 患者进行了回顾性分析。采用单变量、多变量逻辑回归和拉索回归分析来分析 POP 的风险因素。利用接收者操作特征曲线(ROC)、校准曲线和决策曲线分析(DCA)对所建模型进行评估。最后,研究了用不同的机器学习方法对这六个变量建模的有效性。在我们的患者队列中,23.4%(n = 72/308)的患者经历过 POP。单变量、多变量逻辑回归分析和套索回归分析显示,年龄、Hunt-Hess 分级、机械通气、白细胞计数、淋巴细胞计数和血小板计数是 POP 的独立风险因素。随后,这六个因素被用于建立最终模型。我们发现,年龄、Hunt-Hess 分级、机械通气、白细胞计数、淋巴细胞计数和血小板计数是 aSAH 患者 POP 的独立危险因素。通过与其他研究和机器学习模型的验证和比较,我们的新型预测模型在有效预测 aSAH 患者住院期间发生肺炎的可能性方面表现出了很高的效率。
Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.
Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pneumonia (POP) in patients with aSAH. A retrospective analysis was conducted on 308 patients with aSAH who underwent surgery at the Neurosurgery Department of the First Affiliated Hospital of Soochow University. Univariate and multivariate logistic regression and lasso regression analysis were used to analyze the risk factors for POP. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the constructed model. Finally, the effectiveness of modeling these six variables in different machine learning methods was investigated. In our patient cohort, 23.4% (n = 72/308) of patients experienced POP. Univariate, multivariate logistic regression analysis and lasso regression analysis revealed age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count as independent risk factors for POP. Subsequently, these six factors were used to build the final model. We found that age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count were independent risk factors for POP in patients with aSAH. Through validation and comparison with other studies and machine learning models, our novel predictive model has demonstrated high efficacy in effectively predicting the likelihood of pneumonia during the hospitalization of aSAH patients.
期刊介绍:
The goal of Neurosurgical Review is to provide a forum for comprehensive reviews on current issues in neurosurgery. Each issue contains up to three reviews, reflecting all important aspects of one topic (a disease or a surgical approach). Comments by a panel of experts within the same issue complete the topic. By providing comprehensive coverage of one topic per issue, Neurosurgical Review combines the topicality of professional journals with the indepth treatment of a monograph. Original papers of high quality are also welcome.