Abstract：
OBJECTIVE: The occurrence and predictors of symptomatic subdural hygroma (SSH) subsequent to the fenestration of pediatric intracranial arachnoid cysts (IACs) are unclear. In this study, the authors aimed to investigate the likelihood of an SSH following IAC fenestration and the impact on operative efficacy with the ultimate goal of constructing a nomogram.
METHODS: The medical records of 1782 consecutive patients who underwent surgical treatment at the Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were reviewed. Among these patients, a training cohort (n = 1214) underwent surgery during an earlier period and was used for the development of a nomogram. The remaining patients formed the validation cohort (n = 568) and were used to confirm the performance of the developed model. The development of the nomogram involved the use of potential predictors, while internal validation was conducted using a bootstrap-resampling approach.
RESULTS: SSH was detected in 13.2% (160 of 1214) of patients in the training cohort and in 11.1% (63 of 568) of patients in the validation cohort. Through multivariate analysis, several factors including Galassi type, IAC distance to the basal cisterns, temporal bulge, midline shift, IAC shape in the coronal view, area of the stoma, and artery location near the stoma were identified as independent predictors of SSH. These 7 predictors were used to construct a nomogram, which exhibited a concordance statistic (C-statistic) of 0.826 and demonstrated good calibration. Following internal validation, the nomogram maintained good calibration and discrimination with a C-statistic of 0.799 (95% CI 0.665-0.841). Patients who had nomogram scores < 30 or ≥ 30 were considered to be at low and high risk of SSH occurrence, respectively.
CONCLUSIONS: The predictive model and derived nomogram achieved satisfactory preoperative prediction of SSH. Using this nomogram, the risk for an individual patient can be estimated, and the appropriate surgery can be performed in high-risk patients.
METHODS: The medical records of 1782 consecutive patients who underwent surgical treatment at the Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were reviewed. Among these patients, a training cohort (n = 1214) underwent surgery during an earlier period and was used for the development of a nomogram. The remaining patients formed the validation cohort (n = 568) and were used to confirm the performance of the developed model. The development of the nomogram involved the use of potential predictors, while internal validation was conducted using a bootstrap-resampling approach.
RESULTS: SSH was detected in 13.2% (160 of 1214) of patients in the training cohort and in 11.1% (63 of 568) of patients in the validation cohort. Through multivariate analysis, several factors including Galassi type, IAC distance to the basal cisterns, temporal bulge, midline shift, IAC shape in the coronal view, area of the stoma, and artery location near the stoma were identified as independent predictors of SSH. These 7 predictors were used to construct a nomogram, which exhibited a concordance statistic (C-statistic) of 0.826 and demonstrated good calibration. Following internal validation, the nomogram maintained good calibration and discrimination with a C-statistic of 0.799 (95% CI 0.665-0.841). Patients who had nomogram scores < 30 or ≥ 30 were considered to be at low and high risk of SSH occurrence, respectively.
CONCLUSIONS: The predictive model and derived nomogram achieved satisfactory preoperative prediction of SSH. Using this nomogram, the risk for an individual patient can be estimated, and the appropriate surgery can be performed in high-risk patients.
摘要：
目的：小儿颅内蛛网膜囊肿（IACs）开窗后有症状的硬膜下积液（SSH）的发生和预测因素尚不清楚。在这项研究中,作者旨在研究IAC开窗后发生SSH的可能性以及对手术疗效的影响,最终目的是构建列线图.
方法：回顾性分析了在上海交通大学医学院附属新华医院接受手术治疗的1782例连续患者的病历。在这些患者中,一个训练队列(n=1214)在较早时期接受了手术,并用于形成列线图.其余患者形成验证队列(n=568)并用于确认开发的模型的性能。列线图的发展涉及潜在预测因子的使用，而内部验证是使用自举-重采样方法进行的。
结果：在训练队列中有13.2%（1214人中有160人）的患者和在验证队列中有11.1%（568人中有63人）的患者中检测到SSH。通过多变量分析，几个因素，包括Galassi型，IAC到基底水箱的距离，颞叶隆起,中线移位,IAC在冠状视图中的形状，造口的区域，和造口附近的动脉位置被确定为SSH的独立预测因子。这7个预测因子被用来构建一个列线图，表现出0.826的一致性统计量（C统计量），并显示出良好的校准。在内部验证之后，列线图保持良好的校准和鉴别,C统计量为0.799(95%CI0.665-0.841).列线图评分<30或≥30的患者被认为是发生SSH的风险较低和较高。分别。
结论：预测模型和推导的列线图获得了令人满意的SSH术前预测。使用这个列线图，可以估计单个患者的风险，并且可以对高危患者进行适当的手术。
方法：回顾性分析了在上海交通大学医学院附属新华医院接受手术治疗的1782例连续患者的病历。在这些患者中,一个训练队列(n=1214)在较早时期接受了手术,并用于形成列线图.其余患者形成验证队列(n=568)并用于确认开发的模型的性能。列线图的发展涉及潜在预测因子的使用，而内部验证是使用自举-重采样方法进行的。
结果：在训练队列中有13.2%（1214人中有160人）的患者和在验证队列中有11.1%（568人中有63人）的患者中检测到SSH。通过多变量分析，几个因素，包括Galassi型，IAC到基底水箱的距离，颞叶隆起,中线移位,IAC在冠状视图中的形状，造口的区域，和造口附近的动脉位置被确定为SSH的独立预测因子。这7个预测因子被用来构建一个列线图，表现出0.826的一致性统计量（C统计量），并显示出良好的校准。在内部验证之后，列线图保持良好的校准和鉴别,C统计量为0.799(95%CI0.665-0.841).列线图评分<30或≥30的患者被认为是发生SSH的风险较低和较高。分别。
结论：预测模型和推导的列线图获得了令人满意的SSH术前预测。使用这个列线图，可以估计单个患者的风险，并且可以对高危患者进行适当的手术。
