Shunt surgery is the main treatment modality for hydrocephalus, the leading cause of brain surgery in children. The efficacy of shunt surgery, particularly in infant hydrocephalus, continues to present serious challenges in achieving improved outcomes. The crucial role of correct adjustments of valve performance levels in shunt outcomes has been underscored. However, there are discrepancies in the performance levels of valves from different companies. This study aims to address this concern by optimizing both the number and range of valve performance levels for infant hydrocephalus, aiming for improved shunt surgery outcomes. We conducted a single-center cohort study encompassing infant hydrocephalus cases that underwent initial shunt surgery without subsequent failure or unimproved outcomes. An unsupervised hierarchical machine learning method was utilized for clustering and reporting the valve drainage pressure values for all patients within each identified cluster. The optimal number of clusters corresponds to the number of valve performance levels, with the valve drainage pressure ranges within each cluster indicating the pressure range for each performance level. Comparisons based on the Silhouette coefficient between 3-7 clusters revealed that this coefficient for the 4-cluster (4-performance level) was at least 28.3% higher than that of other cluster formations in terms of intra-cluster similarity. The Davies-Bouldin index for the 4-performance level was at least 37.2% lower than that of other configurations in terms of inter-cluster dissimilarity. Cluster stability, indicated by a Jaccard index of 71% for the 4-performance level valve, validated the robustness, reliability, and repeatability of our findings. Our suggested optimized drainage pressure ranges for each performance level (1.5–5.0, 5.0–9.0, 9.0–15.0, and 15.0–18.0 cm H2O) may potentially assist neurosurgeons in improving clinical outcomes for patients with shunted infantile hydrocephalus.

中文翻译：

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婴儿脑积水分流阀性能水平的数量和范围的优化：机器学习分析

分流手术是脑积水的主要治疗方式，脑积水是儿童脑部手术的主要原因。分流手术的疗效，尤其是婴儿脑积水的疗效，在改善预后方面仍然面临着严峻的挑战。强调了正确调整阀门性能水平对分流结果的关键作用。然而，不同公司的阀门性能水平存在差异。本研究旨在通过优化婴儿脑积水的瓣膜性能水平的数量和范围来解决这一问题，旨在改善分流手术的结果。我们进行了一项单中心队列研究，涵盖婴儿脑积水病例，这些病例接受了初次分流手术，但没有随后的失败或未改善的结果。利用无监督的分层机器学习方法对每个识别的簇内的所有患者进行聚类和报告瓣膜引流压力值。最佳簇数对应于阀门性能级别的数量，每个簇内的阀门排水压力范围指示每个性能级别的压力范围。基于 3-7 簇之间的 Silhouette 系数的比较表明，就簇内相似性而言，4 簇（4 性能级别）的该系数比其他簇形成的系数至少高 28.3%。就簇间差异而言，4 性能级别的 Davies-Bouldin 指数比其他配置至少低 37.2%。 4 性能级别阀门的 Jaccard 指数为 71%，表明簇稳定性，验证了我们研究结果的稳健性、可靠性和可重复性。我们针对每个性能级别建议的优化排水压力范围（1.5–5.0、5.0–9.0、9.0–15.0 和 15.0–18.0 cm H2O）可能会帮助神经外科医生改善分流婴儿脑积水患者的临床结果。