The Impact of Multimorbidity Burden, Frailty Risk Scoring, and 3-Directional Morphological Indices vs. Testing for CSF Responsiveness in Normal Pressure Hydrocephalus

Abstract

Objective: Multimorbidity burden across disease cohorts and variations in clinico-radiographic presentations within normal pressure hydrocephalus (NPH) confound its diagnosis, and the assessment of its amenability to interventions. We hypothesized that novel imaging techniques such as 3-directional linear morphological indices could help in distinguishing between hydrocephalus vs. non-hydrocephalus and correlate with responsiveness to external lumbar drainage (CSF responsiveness) within NPH subtypes.Methodology: Twenty-one participants with NPH were recruited and age-matched to 21 patients with Alzheimer’s Disease (AD) and 21 healthy controls (HC) selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Patients with NPH underwent testing via the NPH programme with external lumbar drainage (ELD); pre- and post-ELD MRI scans were obtained. The modified Frailty Index (mFI-11) was used to stratify the NPH cohort, including Classic and Complex subtypes, by their comorbidity and frailty risks. The quantitative imaging network tool 3D Slicer was used to derive traditional 2-dimensional (2d) linear measures; Evans Index (EI), Bicaudate Index (BCI) and Callosal Angle (CA), along with novel 3-directional (3d) linear measures; z-Evans Index and Brain per Ventricle Ratio (BVR). 3-Dimensional (3D) ventricular volumetry was performed as an independent correlate of ventriculomegaly to CSF responsiveness.Results: Mean age for study participants was 71.14 ± 6.3 years (18, 85.7% males). The majority (15/21, 71.4%) of participants with NPH comprised the Complex subtype (overlay from vascular risk burden and AD); 12/21 (57.1%) were Non-Responders to ELD. Frailty alone was insufficient in distinguishing between NPH subtypes. By contrast, 3d linear measures distinguished NPH from both AD and HC cohorts, but also correlated to CSF responsiveness. The z-Evans Index was the most sensitive volumetric measure of CSF responsiveness (p = 0.012). Changes in 3d morphological indices across timepoints distinguished between Responders vs. Non-Responders to lumbar testing. There was a significant reduction of indices, only in Non-Responders and across multiple measures (z-Evans Index; p = 0.001, BVR at PC; p = 0.024). This was due to a significant decrease in ventricular measurement (p = 0.005) that correlated to independent 3D volumetry (p = 0.008).Conclusion. In the context of multimorbidity burden, frailty risks and overlay from neurodegenerative disease, 3d morphological indices demonstrated utility in distinguishing hydrocephalus vs. non-hydrocephalus and degree of CSF responsiveness. Further work may support the characterization of patients with Complex NPH who would best benefit from the risks of interventions.

Clinical Outcomes After Ventriculo-Peritoneal Shunting in Patients With Classic vs. Complex NPH

“…We apply the same rationale to the inclusion of significant comorbidities as criteria for Complex NPH; specifically scoring individual comorbidity and frailty burdens alone did not distinguish the two NPH cohorts, whereas regarding their clinical risk stratification as a whole was more effective in separating their differing thresholds of outcomes. In more recent work, we have suggested a further refinement of the term Complex NPH, by expanding on our definition for consistency ( 38 ), to address shortcomings in our preliminary approach to this concept.…”

Neuroimaging data repositories and AI-driven healthcare—Global aspirations vs. ethical considerations in machine learning models of neurological disease

“…We examined whether or not, by perturbing the proportions of cohorts within the pilot dataset, we were able to alter findings of structural brain metrics and interpretation of brain injury patterns resulting from such analyses. We have previously published our methodologies for 3D structural segmentation and DTI profiles (Lock et al, 2019 ; Soon et al, 2021 ). For the mini AI Ethics experiment, imaging datasets were anonymized at source, then subject to further checks and deidentification, defacing and processing prior to usage, according to our lab and institutional protocols.…”

Modeling the Properties of White Matter Tracts Using Diffusion Tensor Imaging to Characterize Patterns of Injury in Aging and Neurodegenerative Disease

“…It is defined as the ratio of the maximum width of the frontal horns of the lateral ventricles to the maximum internal width of the cranial vault as seen on the axial view ( Yamada et al, 2016 ). 3D Slicer was used to re-align T1 scans to the ACPC for consistency and derive the EI ( Soon et al, 2021 ).…”