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

Preoperative imaging biomarkers combined with tap test for predicting shunt surgery outcome in idiopathic normal pressure hydrocephalus: a multicenter retrospective study

Objectives

The study aims to investigate the predictive performance of preoperative imaging features combined with tap test for the outcomes of ventriculoperitoneal (VP) shunt in idiopathic normal pressure hydrocephalus (iNPH).

Methods

In this multicenter retrospective study, 166 iNPH patients who underwent VP shunt surgery between August 2019 and November 2023 were included. Preoperative clinical characteristics and imaging features were collected. Preoperative clinical assessment and at least 3 months of postoperative follow-up were performed. Multivariable logistic regression, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate predictive performance.

Results

Out of 166 total patients, 96 were responders and 70 non-responders. The tap test showed significant difference between two group (p < 0.01). Multivariable logistic regression identified that a positive disproportionately enlarged subarachnoid space (DESH) sign (OR = 0.09, 95% CI: 0.04-0.22, p < 0.001) and a sharper callosal angle (CA) (OR = 0.97, 95% CI: 0.95-1.00, p = 0.02) were associated with symptom improvement after shunt. The sensitivity, specificity, and AUC of tap test were 0.64, 0.60, and 0.62, respectively. Combining CA and the tap test increased sensitivity to 0.85, while combining DESH, CA, and the tap test improved specificity and AUC to 0.67 and 0.72, respectively.

Conclusion

The findings suggest that the imaging features DESH and CA, when combined with the tap test, enhance the prediction of VP shunt outcomes in iNPH patients. Despite the improved predictive capability, further research focusing on innovative biomarkers for VP shunt is warranted.

Frailty increases the risk of Alzheimer's disease in non-demented individuals: A longitudinal cohort study

BACKGROUND

Frailty, which is considered a potential modifiable risk factor for dementia, continues to generate debate when it comes to Alzheimer's disease (AD). Furthermore, the underlying pathological mechanisms linking frailty to AD remain uncertain.

OBJECTIVE

We aimed to investigate the relationship between frailty and the risk of AD while elucidating the connections between frailty, AD biomarkers, and cognitive function.

METHODS

Total of 829 non-frail (261 robust, 568 pre-frail) and 94 frail individuals from the Alzheimer's Disease Neuroimaging Initiative database were recruited. Kaplan-Meier analysis and Cox regression assessed AD risk across diverse frail statuses in 923 non-demented individuals. Multiple linear regression, mixed effects models and causal mediation analyses bootstrapped 10,000 iterations were conducted to examined underlying associations.

RESULTS

The frail group had a 67.7% increased risk of AD than non-frail group (HR = 1.677; 95%CI, 1.179-2.385; p = 0.004), a 61.8% increased risk of AD than pre-frail group (HR = 1.618; 95%CI, 1.131-2.316; p = 0.009) and a far higher risk of AD than robust group (HR = 2.011; 95%CI, 1.263-3.202; p = 0.003). Frailty was associated with cognitive decline (global cognition, memory and executive function), whole brain and hippocampus atrophy, and ventricle dilation. Higher frail degree predicted faster cognitive decline, brain atrophy and ventricle dilation. Frailty's association with cognition was partially mediated by volume of whole brain (29.54%-30.17% of total effect), hippocampus (18.21%-24.55% of total effect), and ventricle (baseline, 7.62%-10.87% of total effect; change rate, 13.30%-24.33% of total effect).

CONCLUSIONS

Frailty as a potential risk factor for AD, further mechanisms investigation is warranted; mitigating frailty could potentially contribute to AD prevention.

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

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.

Explore the Role of Frailty as a Contributor to the Association Between AT(N) Profiles and Cognition in Alzheimer's Disease

Background

The relationship between Alzheimer's disease (AD)-related pathology and cognition was not exactly consistent.

Objective

To explore whether the association between AD pathology and cognition can be moderated by frailty.

Methods

We included 1711 participants from the Alzheimer's Disease Neuroimaging Initiative database. Levels of cerebrospinal fluid amyloid-β, p-tau, and t-tau were identified for AD-related pathology based on the amyloid-β/tau/neurodegeneration (AT[N]) framework. Frailty was measured using a modified Frailty Index-11 (mFI-11). Regression and interaction models were utilized to assess the relationship among frailty, AT(N) profiles, and cognition. Moderation models analyzed the correlation between AT(N) profiles and cognition across three frailty levels. All analyses were corrected for age, sex, education, and APOEɛ4 status.

Results

In this study, frailty (odds ratio [OR] = 1.71, p < 0.001) and AT(N) profiles (OR = 2.00, p < 0.001) were independently associated with cognitive status. The model fit was improved when frailty was added to the model examining the relationship between AT(N) profiles and cognition (p < 0.001). There was a significant interaction between frailty and AT(N) profiles in relation to cognitive status (OR = 1.12, pinteraction = 0.028). Comparable results were obtained when Mini-Mental State Examination scores were utilized as the measure of cognitive performance. The association between AT(N) profiles and cognition was stronger with the levels of frailty.

Conclusions

Frailty may diminish patients' resilience to AD pathology and accelerate cognitive decline resulting from abnormal AD-related pathology. In summary, frailty contributes to elucidating the relationship between AD-related pathology and cognitive impairment.

Frailty Is Associated With Decreased Survival in Adult Patients With Nonoperative and Operative Traumatic Subdural Hemorrhage: A Retrospective Cohort Study of 381,754 Patients

Objective

We investigated frailty's impact on traumatic subdural hematoma (tSDH), examining its relationship with major complications, length of hospital stay (LOS), mortality, high level of care discharges, and survival probabilities following nonoperative and operative management.

Background

Despite its frequency as a neurosurgical emergency, frailty's impact on tSDH remains underexplored. Frailty characterized by multisystem impairments significantly predicts poor outcomes, necessitating further investigation.

Methods

A retrospective study examining tSDH patients ≥18 years and assigned an abbreviated injury scale score ≥3, and entered into ACS-TQIP between 2007 and 2020. We employed multivariable analyses for risk-adjusted associations of frailty and our outcomes, and Kaplan-Meier plots for survival probability.

Results

Overall, 381,754 tSDH patients were identified by mFI-5 as robust-39.8%, normal-32.5%, frail-20.5%, and very frail-7.2%. There were 340,096 nonoperative and 41,658 operative patients. The median age was 70.0 (54.0-81.0) nonoperative, and 71.0 (57.0-80.0) operative cohorts. Cohorts were predominately male and White. Multivariable analyses showed a stepwise relationship with all outcomes P < 0.001; 7.1% nonoperative and 14.9% operative patients had an 20% to 46% increased risk of mortality, that is, nonoperative: very frail (HR: 1.20 [95% CI: 1.13-1.26]), and operative: very frail (HR: 1.46 [95% CI: 1.38-1.55]). There were precipitous reductions in survival probability across mFI-5 strata.

Conclusion

Frailty was associated with major complications, LOS, mortality, and high level care discharges in a nationwide population of 381,754 patients. While timely surgery may be required for patients with tSDH, rapid deployment of point-of-care risk assessment for frailty creates an opportunity to equip physicians in allocating resources more precisely, possibly leading to better outcomes.

Automatic MRI volumetry in asymptomatic cases at risk for normal pressure hydrocephalus

The occurrence of significant Alzheimer's disease (AD) pathology was described in approximately 30% of normal pressure hydrocephalus (NPH) cases, leading to the distinction between neurodegenerative and idiopathic forms of this disorder. Whether or not there is a specific MRI signature of NPH remains a matter of debate. The present study focuses on asymptomatic cases at risk for NPH as defined with automatic machine learning tools and combines automatic MRI assessment of cortical and white matter volumetry, risk of AD (AD-RAI), and brain age gap estimation (BrainAge). Our hypothesis was that brain aging and AD process-independent volumetric changes occur in asymptomatic NPH-positive cases. We explored the volumetric changes in normal aging-sensitive (entorhinal cortex and parahippocampal gyrus/PHG) and AD-signature areas (hippocampus), four control cortical areas (frontal, parietal, occipital, and temporal), and cerebral and cerebellar white matter in 30 asymptomatic cases at risk for NPH (NPH probability >30) compared to 30 NPH-negative cases (NPH probability <5) with preserved cognition. In univariate regression models, NPH positivity was associated with decreased volumes in the hippocampus, parahippocampal gyrus (PHG), and entorhinal cortex bilaterally. The strongest negative association was found in the left hippocampus that persisted when adjusting for AD-RAI and Brain Age values. A combined model including the three parameters explained 36.5% of the variance, left hippocampal volumes, and BrainAge values, which remained independent predictors of the NPH status. Bilateral PHG and entorhinal cortex volumes were negatively associated with NPH-positive status in univariate models but this relationship did not persist when adjusting for BrainAge, the latter remaining the only predictor of the NPH status. We also found a negative association between bilateral cerebral and cerebellar white matter volumes and NPH status that persisted after controlling for AD-RAI or Brain Age values, explaining between 50 and 65% of its variance. These observations support the idea that in cases at risk for NPH, as defined by support vector machine assessment of NPH-related MRI markers, brain aging-related and brain aging and AD-independent volumetric changes coexist. The latter concerns volume loss in restricted hippocampal and white matter areas that could be considered as the MRI signature of idiopathic forms of NPH.

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

OBJECTIVE

Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence-driven tools for early detection.

METHODS

The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008-July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group.

RESULTS

In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems.

CONCLUSIONS

This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence-driven early detection of iNPH and prompt treatment.

Hospital Frailty Risk Score Predicts Adverse Events and Readmission Following a Ventriculoperitoneal Shunt Surgery for Normal Pressure Hydrocephalus

OBJECTIVE

The aim of this study was to evaluate the impact of a Hospital Frailty Risk Score (HFRS) on unplanned readmission and health care resource utilization in normal pressure hydrocephalus (NPH) patients undergoing a ventriculoperitoneal (VP) shunt surgery.

METHODS

A retrospective cohort study was performed using the 2016-2019 Nationwide Readmission Database. All NPH patients (≥60 years) undergoing a VP shunt surgery were identified using ICD-10-CM diagnostic and procedural codes. Patients were dichotomized into 2 cohorts as follows: Low HFRS (<5) and Intermediate-High HFRS (≥5). A multivariate logistic regression analysis was then used to identify independent predictors of adverse event (AE) and 30- and 90-day readmission.

RESULTS

Of 13,262 patients, 4386 (33.1%) had an Intermediate-High HFRS score. A greater proportion of the Intermediate-High HFRS cohort experienced at least one AE (1.9 vs. 22.1, P < 0.001). The Intermediate-High HFRS cohort also had a longer length of stay (2.3 ± 2.4 days vs. 7.0 ± 7.7 days, P < 0.001), higher non-routine discharge rate (19.9% vs. 39.9%, P < 0.001), and greater admission cost ($14,634 ± 5703 vs. $21,749 ± 15,234, P < 0.001). The Intermediate-High HFRS cohort had higher rates of 30- (7.6% vs. 11.0%, P < 0.001) and 90-day (6.8% vs. 8.3%, P < 0.001) readmissions. On a multivariate regression analysis, Intermediate-High HFRS compared to Low HFRS was an independent predictor of any AE (odds ratio, 16.6; 95% confidence interval, [12.9-21.5]; P < 0.001) and 30-day readmission (odds ratio, 1.4; 95% confidence interval, [1.2-1.7]; P < 0.001).

CONCLUSIONS

Our study suggests that frailty, as defined by HFRS, is associated with increased resource utilization in NPH patients undergoing VP shunt surgery. Furthermore, HFRS was an independent predictor of adverse events and 30-day hospital readmission.

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

Objective

Normal pressure hydrocephalus (NPH) is a neurological condition characterized by a clinical triad of gait disturbance, cognitive impairment, and urinary incontinence in conjunction with ventriculomegaly. Other neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and vascular dementia share some overlapping clinical features. However, there is evidence that patients with comorbid NPH and Alzheimer's or Parkinson's disease may still exhibit good clinical response after CSF diversion. This study aims to evaluate clinical responses after ventriculo-peritoneal shunt (VPS) in a cohort of patients with coexisting NPH and neurodegenerative disease.

Methods

The study has two components; (i) a pilot study was performed that specifically focused upon patients with Complex NPH and following the inclusion of the Complex NPH subtype into consideration for the clinical NPH programme, (ii) a retrospective snapshot study was performed to confirm and characterize differences between Classic and Complex NPH patients being seen consecutively over the course of 1 year within a working subspecialist NPH clinic. We studied the characteristics of patients with Complex NPH, utilizing clinical risk stratification and multimodal biomarkers.

Results

There was no significant difference between responders and non-responders to CSF diversion on comorbidity scales. After VPS insertion, significantly more Classic NPH patients had improved cognition compared to Complex NPH patients (p = 0.005). Improvement in gait and urinary symptoms did not differ between the groups. 26% of the Classic NPH group showed global improvement of the triad, and 42% improved in two domains. Although only 8% showed global improvement of the triad, all Complex NPH patients improved in gait.

Conclusions

Our study has demonstrated that the presence of neurodegenerative disorders co-existing with NPH should not be the sole barrier to the consideration of high-volume tap test or lumbar drainage via a specialist NPH programme. Further characterization of distinct cohorts of NPH with differing degrees of CSF responsiveness due to overlay from neurodegenerative or comorbidity risk burden may aid toward more precise prognostication and treatment strategies. We propose a simplistic conceptual framework to describe NPH by its Classic vs. Complex subtypes to promote the clinical paradigm shift toward subspecialist geriatric neurosurgery by addressing needs for rapid screening tools at the clinical-research interface.

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

Diffusion tensor imaging (DTI) is a relatively novel magnetic resonance-based imaging methodology that can provide valuable insight into the microstructure of white matter tracts of the brain. In this paper, we evaluated the reliability and reproducibility of deriving a semi-automated pseudo-atlas DTI tractography method vs. standard atlas-based analysis alternatives, for use in clinical cohorts with neurodegeneration and ventriculomegaly. We showed that the semi-automated pseudo-atlas DTI tractography method was reliable and reproducible across different cohorts, generating 97.7% of all tracts. However, DTI metrics obtained from both methods were significantly different across the majority of cohorts and white matter tracts (p < 0.001). Despite this, we showed that both methods produced patterns of white matter injury that are consistent with findings reported in the literature and with DTI profiles generated from these methodologies. Scatter plots comparing DTI metrics obtained from each methodology showed that the pseudo-atlas method produced metrics that implied a more preserved neural structure compared to its counterpart. When comparing DTI metrics against a measure of ventriculomegaly (i.e., Evans' Index), we showed that the standard atlas-based method was able to detect decreasing white matter integrity with increasing ventriculomegaly, while in contrast, metrics obtained using the pseudo-atlas method were sensitive for stretch or compression in the posterior limb of the internal capsule. Additionally, both methods were able to show an increase in white matter disruption with increasing ventriculomegaly, with the pseudo-atlas method showing less variability and more specificity to changes in white matter tracts near to the ventricles. In this study, we found that there was no true gold-standard for DTI methodologies or atlases. Whilst there was no congruence between absolute values from DTI metrics, differing DTI methodologies were still valid but must be appreciated to be variably sensitive to different changes within white matter injury occurring concurrently. By combining both atlas and pseudo-atlas based methodologies with DTI profiles, it was possible to navigate past such challenges to describe white matter injury changes in the context of confounders, such as neurodegenerative disease and ventricular enlargement, with transparency and consistency.

Reconsidering Ventriculoperitoneal Shunt Surgery and Postoperative Shunt Valve Pressure Adjustment: Our Approaches Learned From Past Challenges and Failures

Treatment for idiopathic normal pressure hydrocephalus (iNPH) continues to develop. Although ventriculoperitoneal shunt surgery has a long history and is one of the most established neurosurgeries, in the 1970s, the improvement rate of iNPH triad symptoms was poor and the risks related to shunt implantation were high. This led experts to question the surgical indication for iNPH and, over the next 20 years, cerebrospinal fluid (CSF) shunt surgery for iNPH fell out of favor and was rarely performed. However, the development of programmable-pressure shunt valve devices has reduced the major complications associated with the CSF drainage volume and appears to have increased shunt effectiveness. In addition, the development of support devices for the placement of ventricular catheters including preoperative virtual simulation and navigation systems has increased the certainty of ventriculoperitoneal shunt surgery. Secure shunt implantation is the most important prognostic indicator, but ensuring optimal initial valve pressure is also important. Since over-drainage is most likely to occur in the month after shunting, it is generally believed that a high initial setting of shunt valve pressure is the safest option. However, this does not always result in sufficient improvement of the symptoms in the early period after shunting. In fact, evidence suggests that setting the optimal valve pressure early after shunting may cause symptoms to improve earlier. This leads to improved quality of life and better long-term independent living expectations. However, in iNPH patients, the remaining symptoms may worsen again after several years, even when there is initial improvement due to setting the optimal valve pressure early after shunting. Because of the possibility of insufficient CSF drainage, the valve pressure should be reduced by one step (2–4 cmH₂O) after 6 months to a year after shunting to maximize symptom improvement. After the valve pressure is reduced, a head CT scan is advised a month later.