Chabros Jeremi, Placek Michal M, Chu Ka Hing, Beqiri Erta, Hutchinson Peter J, Czosnyka Zofia, Czosnyka Marek, Joannides Alexis, Smielewski Peter
University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, CB2 0SP, Cambridge, UK.
Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Biomedical Campus, CB2 0SP, Cambridge, UK.
Brain Spine. 2024 May 22;4:102837. doi: 10.1016/j.bas.2024.102837. eCollection 2024.
Cerebrospinal fluid (CSF) infusion test analysis allows recognizing and appropriately evaluating CSF dynamics in the context of normal pressure hydrocephalus (NPH), which is crucial for effective diagnosis and treatment. However, existing methodology possesses drawbacks that may compromise the precision and interpretation of CSF dynamics parameters.
This study aims to circumvent these constraints by introducing an innovative analysis method grounded in Bayesian inference.
A single-centre retrospective cohort study was conducted on 858 patients who underwent a computerized CSF infusion test between 2004 and 2020. We developed a Bayesian framework-based method for parameter estimation and compared the results to the current, gradient descent-based approach. We evaluated the accuracy and reliability of both methods by analysing erroneous prediction rates and curve fitting errors.
The Bayesian method surpasses the gradient descent approach, reflected in reduced inaccurate prediction rates and an improved goodness of model fit. On whole cohort level both techniques produced comparable results. However, the Bayesian method holds an added advantage by providing uncertainty intervals for each parameter. Sensitivity analysis revealed significance of the CSF production rate parameter and its interplay with other variables. The resistance to CSF outflow demonstrated excellent robustness.
The proposed Bayesian approach offers a promising solution for improving robustness of CSF dynamics assessment in NPH, based on CSF infusion tests. Additional provision of the uncertainty measure for each diagnostic metric may perhaps help to explain occasional poor diagnostic performance of the test, offering a robust framework for improved understanding and management of NPH.
脑脊液（CSF）输注试验分析有助于在正常压力脑积水（NPH）的背景下识别并适当地评估脑脊液动力学，这对于有效的诊断和治疗至关重要。然而，现有的方法存在一些缺点，可能会影响脑脊液动力学参数的精度和解读。
本研究旨在通过引入一种基于贝叶斯推理的创新分析方法来规避这些限制。
对2004年至2020年间接受计算机化脑脊液输注试验的858例患者进行了单中心回顾性队列研究。我们开发了一种基于贝叶斯框架的参数估计方法，并将结果与当前基于梯度下降的方法进行比较。我们通过分析错误预测率和曲线拟合误差来评估两种方法的准确性和可靠性。
贝叶斯方法优于梯度下降法，表现为错误预测率降低和模型拟合优度提高。在整个队列水平上，两种技术产生了可比的结果。然而，贝叶斯方法具有额外的优势，即能为每个参数提供不确定性区间。敏感性分析揭示了脑脊液生成率参数的重要性及其与其他变量的相互作用。脑脊液流出阻力显示出极佳的稳健性。
基于脑脊液输注试验，所提出的贝叶斯方法为提高NPH中脑脊液动力学评估的稳健性提供了一个有前景的解决方案。为每个诊断指标额外提供不确定性度量可能有助于解释该试验偶尔出现的较差诊断性能，为更好地理解和管理NPH提供一个稳健的框架。
