Citations

Background: The objective of this research article is to compare endoscopic treatment versus shunting procedures for failed endoscopic third ventriculostomies (ETVs) in pediatric patients with hydrocephalus. Methods: We did a systematic review based on preferred reporting items for systematic reviews and meta-analyses guidelines on Studies involving pediatric patients (aged 0–18 years) diagnosed with hydrocephalus, reporting on the use of repeat ETV (Re-ETV) or Ventriculoperitoneal shunting (VPS) as a treatment option following failed ETV. Comparative studies, including randomized controlled trials, cohort studies, and any prospective studies, are included. Studies published in the English language conducted between 2001 and 2023 are included. Results: Forty articles were selected for full-text review. Out of which nine articles that clearly addressed the topic of Re-ETV and/or VPS placement after failure of primary ETV were deemed suitable for analysis. A data set of 663 patients was analyzed. Re-ETV was done in 220 patients (33.18%) and VPS Placement was done in 443 patients (66.81%). The primary ETV failure rates ranged from 16.6 to 60.89%. There was a higher failure rate of Re-ETV (74.98%) compared to VPS (22.26%) indicating that VPS is generally more successful as a secondary intervention. The presence of hemorrhage during primary ETV suggested more benefit from VPS placement rather than Re-ETV (P < 0.05). Conclusion: Our systematic review suggests that VPS placement is the more prevalent choice after primary ETV failure, likely due to its higher overall success rate and the nature of complications. The wide variability in failure rates and follow-up durations suggests that treatment outcomes can differ greatly between patients and studies. Decisions regarding secondary interventions should be individualized, considering patient-specific factors such as age, complications, and timing of intervention.

BACKGROUND AND OBJECTIVES: Aneurysm risk prediction remains an imprecise science that places patients at risk for either over or undertreatment. Machine learning (ML) models may improve clinical practice by adding precision to risk assessment. This study aims to comprehensively assess the current landscape of ML applications in predicting the risk of aneurysm rupture and compare the performance with the widely used PHASES score. METHODS: A systematic review of PubMed, Scopus, and Web of Science was conducted. All studies using ML tools to predict the rupture risk of intracranial aneurysms were included. Meta-analysis was conducted with consideration to the ML algorithms and compared with the PHASES score. RESULTS: Thirty-six studies involving 22 462 patients were included in the final analysis. ML techniques, including 124 models using 25 algorithms, were employed. Among various ML models, while they had comparable diagnostic performance, deep learning exhibited a slightly better performance profile (sensitivity = 0.792, specificity = 0.788, and accuracy = 0.778 in external validation). Based on our analysis, ML, regardless of the algorithm, provides comparable sensitivity (0.743 vs 0.771, P = .60) and higher specificity (0.763 vs 0.507, P < .01) compared with the PHASES score. Consistently, pooling the area under the receiver operating characteristic curve (AUC) for 60 ML models and 5 PHASES score data, ML models exhibited higher AUC (0.84 vs 0.64, P < .01). Using hemodynamic parameters as input for models improved specificity ( P < .01) in the test sets without any significant changes in the sensitivity. The later improvement was not observed in the external validation sets. CONCLUSION: ML techniques have the potential to enhance the prediction of intracranial aneurysm rupture compared with traditional approaches, like the PHASES score. Incorporating hemodynamic parameters may further enhance the accuracy of ML models. Feature prospective studies are required to validate the utility of ML models for clinical integration.

Abstract Background Radiofrequency ablation (RFA) of neuromas has emerged as a potential treatment option for refractory neuroma‐associated postamputation pain (PAP). Objective To evaluate the feasibility of using internally cooled radiofrequency ablation (C‐RFA) for treating neuroma‐associated PAP. Design Prospective single‐arm pilot study. Setting Tertiary academic medical center. Participants Eight participants with neuroma‐associated PAP (mean age 56.5 [ interquartile range, 50.5–68.5] years; mean body mass index 30.2 [ interquartile range, 26.3–34.8] kg/m 2 ). Intervention Participants were recruited and treated with C‐RFA between 2019 and 2023. Main Outcome Measures Pain and functional outcomes were assessed at 1, 3, 6, and 12 months after C‐RFA using Numeric Rating Scale (NRS), Groningen Activity Restriction Scale (GARS), Patient Global Impression of Change (PGIC), and Medication Quantification Scale III (MQS‐III). Responder definitions for each outcome measure were ≥50% NRS reduction, ≥30% GARS improvement, ≥6 on PGIC, and ≥6.8‐point MQS‐III reduction. Results NRS responder rates at 1, 3, 6, and 12 months were 25.0% (95% CI, 7.0%–59.1%), 50.0% (95% CI, 21.5%–78.5%), 37.5% (95% CI, 13.7%–69.4%), and 50.0% (95% CI, 21.5%–78.5%), respectively. Responder rates for GARS were 25.0% (95% CI, 7.1%–59.1%) at 1 month and 12.5% (95% CI, 2.2%–47.1%) at 3, 6, and 12 months. The proportion of PGIC responders was 37.5% (95% CI, 13.7%–69.4%) at 1 and 3 months and 12.5% (95% CI, 2.2%–47.1%) at 6 and 12 months. MQS‐III responder rates were 25.0% (95% CI, 7.1%–59.1%) at 1 month, 12.5% (95% CI, 2.2%–47.1%) at 3 months, and 37.5% (95% CI, 13.7%–69.4%) at 6 and 12 months. There were no complications associated with the C‐RFA procedure. Conclusions These findings indicate the feasibility of neuroma C‐RFA as a treatment for refractory neuroma‐associated PAP but also suggest that further patient selection and C‐RFA technique optimization are warranted before investigating this treatment paradigm in a larger prospective study.

Abstract Objectives/Background Post‐traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post‐mTBI. However, that approach may overlook individual differences in how headaches evolve over time. This study aimed to identify individual subgroups based on their headache trajectories and to develop machine‐learning (ML) models for early prediction of headache evolution. Methods Participants with acute PTH completed a daily electronic headache diary over 3 months, recording their headache‐related symptoms. Tensor decomposition was utilized to extract latent factors underlying the time‐varying symptoms. We applied clustering techniques on the latent factors to identify patient subgroups with varying headache improvement trajectory. Next, we developed an ML method to classify each individual into a headache trajectory subgroup as early as possible within the 3‐month interval. Results Seventy‐three individuals with acute PTH (mean age = 44.8 years, SD = 14.0; 50 females/23 males) were enrolled between 0 and 59 days post‐mTBI. Data from 54 individuals were used as the training cohort for model training, and 19 individuals were used as the test cohort for model evaluation. Tensor decomposition extracted two latent factors: one factor representing the overall state of PTH severity and disability and the other representing the improvement state of these symptoms over 3 months. Clustering identified four patient subgroups with distinct headache evolution trajectories: (1) severe symptoms without improvement, (2) severe symptoms with mild improvement, (3) milder symptoms with substantial improvement, and (4) mildest symptoms with mild improvement. The proposed ML model achieved 0.80 cross‐validation accuracy in classifying individuals with PTH into subgroups for the training cohort and 0.84 accuracy for the test cohort. Notably, the model required only the first 2 weeks of headache data to accurately identify the subgroup with the mildest headaches, 3 additional weeks to identify the subgroup with the most severe headaches and no improvement in 3 months, and 2 additional weeks to distinguish the remaining subgroups. Conclusion This study identified subgroups of individuals with acute PTH with distinct headache improvement trajectories. The proposed ML method accurately classified individuals into these subgroups using the minimally necessary early headache data for each person, including detecting the subgroup with the mildest headaches at 2 weeks. This approach could offer an estimated forecast of headache burden over time and could assist clinicians with determining treatment needs and eligibility for PTH clinical trials.

Myocardial deformation imaging has emerged as a valuable clinical tool for assessing right ventricular (RV) systolic function, providing additional diagnostic and prognostic insights compared to traditional indices of RV function. Two-dimensional speckle-tracking echocardiography is currently the standardized method of choice for measuring RV longitudinal strain (RVLS) in clinical practice. RVLS provides a more sensitive indicator of subtle myocardial dysfunction than conventional parameters for RV function assessment (i.e., tricuspid annular plane systolic excursion, tissue Doppler systolic velocity, fractional area change, or RV ejection fraction), with utility for the risk stratification and surveillance of conditions affecting the right heart. However, accurate interpretation of RVLS requires a comprehensive understanding of RV mechanics, pathology, and loading conditions across various cardiovascular conditions, as well as the effects of image quality and technical aspects of image acquisition and tracking in RV strain measurements. This review provides an updated overview of current practical guidelines for RV strain analysis, current clinical applications, and future directions for its potential use in clinical practice.

ObjectiveTo determine whether a machine learning model of voxel level [18f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.MethodsOne hundred and thirty‐seven autopsied patients with PSP (n = 42) and other neurodegenerative diseases (n = 95) who underwent antemortem [18f]fluorodeoxyglucose PET and 3.0 Tesla magnetic resonance imaging (MRI) scans were analyzed. A linear support vector machine was applied to differentiate pathological groups with sensitivity analyses performed to assess the influence of voxel size and region removal. A radial basis function was also prepared to create a secondary model using the most important voxels. The models were optimized on the main dataset (n = 104), and their performance was compared with the magnetic resonance parkinsonism index measured on MRI in the independent test dataset (n = 33).ResultsThe model had the highest accuracy (0.91) and F‐score (0.86) when voxel size was 6mm. In this optimized model, important voxels for differentiating the groups were observed in the thalamus, midbrain, and cerebellar dentate. The secondary models found the combination of thalamus and dentate to have the highest accuracy (0.89) and F‐score (0.81). The optimized secondary model showed the highest accuracy (0.91) and F‐scores (0.86) in the test dataset and outperformed the magnetic resonance parkinsonism index (0.81 and 0.70, respectively).InterpretationThe results suggest that glucose hypometabolism in the thalamus and cerebellar dentate have the highest potential for predicting PSP pathology. Our optimized machine learning model outperformed the best currently available biomarker to predict PSP pathology. ANN NEUROL 2025;98:410–420

Purpose Spine-related pain disorders commonly require interventions such as spinal injections, which are often image-guided and increase in demand as populations age. This study investigates the impact of lifestyle changes following retirement on the incidence of these procedures, hypothesizing that retirement significantly influences the requirement for such treatments. Methods A review of medical records from XXX County between 2000 and 2023 was conducted, focusing on patients who received spinal injections, including epidural steroid, facet, and sacroiliac joint injections. Data were stratified by age, employment status pre- and post-procedure, and type of injection. Statistical analysis, including segmental linear regression and t-tests, was used to assess changes in incidence rates over time and across demographic subsets. Results Out of 134,318 patients analyzed, a total of 359,224 spinal injections were performed. There was a significant ( p -value <.01) increase in the frequency of procedures among individuals around the age of 60, which coincides with the typical retirement age in the United States. The study also highlighted a subsequent decline in the incidence of these procedures between the ages of 70 and 74, suggesting potential adaptation to chronic conditions or the effectiveness of earlier treatments. Conclusion The findings of this study confirm the hypothesis that retirement significantly impacts the demand for spinal injections, as evidenced by the marked increase in procedures at typical retirement ages. This underscores the importance of integrating preventive care and personalized medical guidance into healthcare strategies for individuals nearing retirement to effectively manage spine-related pain disorders and reduce the necessity for invasive treatments.

AbstractBackgroundStimulation‐induced dyskinesias (SID) from deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) are uncommon; however, they are increasingly recognized. Once considered transient and indicative of effective neuromodulation, SID are now seen as potential therapy‐limiting side effects, akin to internal capsule activation. The mechanism and anatomical basis for SID remain poorly understood.MethodsWe conducted a retrospective study of individuals with Parkinson's disease with STN or GPi DBS who experienced SID in the dopaminergic medication OFF state during the monopolar review 1‐month post‐implantation.ResultsWe analyzed 137 monopolar stimulation observations (105 GPi, 32 STN). In the GPi cohort, discriminative fiber tract analysis showed a strong association between SID and the modulation of subthalamo‐pallidal fibers. This correlation was confirmed using leave‐one‐out and five‐fold cross‐validation. We further validated this model by predicting SID in independent STN and GPi cohorts, with the GPi‐based model accounting for significant variance in SID occurrence in both cohorts.ConclusionsSID from STN or GPi DBS likely shares a common pathway via subthalamo‐pallidal connectivity. DBS modulation of these fibers correlates with SID, as confirmed by multiple cross‐validation methods. These findings suggest that the fibers are part of a more extensive and yet‐to‐be‐fully‐characterized dyskinesia network. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.