Citations

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.

Background: Addressing social determinants of health in patient care helps hospitals better understand the non-medical factors influencing patients’ health outcomes. Objectives: The objective of this study was to evaluate the correlation between hospital characteristics, county determinants, and the systematic recording of health-related social needs among general and surgical acute care hospitals in the United States. It focused on the hospital’s routine collection of data on patients’ health-related social needs, such as transportation, housing, and food insecurity. Design: A cross-sectional retrospective study design was utilized. Methods: All hospitals that completed the American Hospital Association Annual survey (n = 2254) were included in the study. A series of multinomial logistic analyses were conducted. Results: The relative risk of hospitals routinely collecting health-related social needs data is 67% lower in for-profit hospitals and 90% higher in not-for-profit hospitals compared to government hospitals. Hospitals that are part of a system are 1.5 times more likely to routinely collect data on social needs. In addition, counties with higher household income have a statistically significant higher relative risk of hospitals collecting data on social needs, though the magnitude of the difference is small. The relative risk of hospitals collecting social needs data, but not routinely, is 2 times higher in teaching hospitals and 3 times higher among system hospitals. Conclusion: Our research strongly indicates that understanding and addressing these inherent hospital-related factors are essential for effectively integrating social determinants of health into routine healthcare data collection practices. Establishing more robust guidelines and standardization in these practices may enhance hospitals’ ability to document and utilize health-related social needs information, ultimately driving improved patient outcomes and supporting more equitable care.

SUMMARY Tuberculosis induces diverse lesions, such as necrotic pneumonia, contributing to disease progression and transmission. Despite advances in understanding the role of ATP-gated P2RX7 ion channels in developing severe forms of tuberculosis, the regulation of this important signaling pathway remains unclear. Herein, we show that the ectonucleotidase CD39 plays an essential regulatory role in TB progression by preventing lung tissue damage, bacterial dissemination, and excessive inflammatory responses. Mechanistically, through its enzymatic activity on the cellular surface, CD39 protects infected macrophages from undergoing necrotic death mediated by P2RX7 activation. We proposed that by protecting macrophages from P2RX7-mediated cell death and bacterial dissemination, CD39 prevents the development of necrotic lesions. Altogether, these findings uncover a significant role for CD39 as an essential component of the molecular regulation underlying the development of severe tuberculosis. GRAPHICAL ABSTRACT Brief In tuberculosis, necrotic granuloma-like structures release extracellular ATP (eATP), which triggers P2RX7-mediated immune cell death. CD39 degrades eATP, preventing P2RX7 activation and promoting macrophage survival, thereby limiting inflammation, tissue damage, and bacterial dissemination.