目的:Chiari畸形I型(CM-I)是一种先天性疾病,占人口的0.1%。在有症状的情况下,后颅窝减压术(PFD)是首选的治疗方法。手术是,然而,与可能需要再次入院或重新手术干预的围手术期和术后并发症相关。考虑到相关的财务成本和对患者健康的影响,我们需要能够评估此类不良事件发生可能性的预测工具.因此,这项研究的目的是利用机器学习算法来开发CM-I儿科患者PFD后30天再入院和再手术的预测模型。
方法:这是一项基于国家外科质量改善计划-儿科数据库数据的回顾性研究。符合条件的患者是在2012年至2021年期间接受CM-I的PFD(当前程序术语代码61343)的患者。排除因肿瘤或血管病变而接受手术的患者。计划外的30天再入院和计划外的30天再手术是主要的研究结果。考虑的其他结果数据包括住院时间,30天并发症,放电处理,30天死亡率随机生成训练和测试样本(80:20),使用逻辑回归研究30天的再入院和再手术,决策树,随机森林(RF),K-最近的邻居,和高斯朴素贝叶斯算法。
结果:共纳入7106例接受PFD的儿科患者。中位年龄为9.2岁(IQR4.7,14.2岁)。大多数患者为女性(56%)。30天再入院率和再手术率分别为7.5%和3.4%,分别。头痛(32%)和伤口相关并发症(30%)是30天再入院的最常见原因,在伤口修正和液体或血液排出时(62%),其次是脑脊液转移相关程序(28%),是30天再次手术的最常见原因。与其他模型相比,RF分类器对30天再入院(曲线下面积[AUC]0.960)和再手术(AUC0.990)的预测准确性最高。关于特征重要性分析,性别,发育迟缓,种族,呼吸道疾病,早产,脑积水,和先天性/遗传异常是对两种RF模型贡献最大的一些变量。
结论:使用大规模的全国性数据集,开发了用于预测30天再入院和再操作的机器学习模型,并实现了高精度。这突出了机器学习在儿科CM-I的风险分层和手术决策中的实用性。
OBJECTIVE: Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients\' well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I.
METHODS: This was a retrospective study based on data from the National Surgical Quality Improvement Program-Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms.
RESULTS: A total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models.
CONCLUSIONS: Using a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.