Shelly, Shahar

Abstract Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.

ABSTRACT Background Myasthenia gravis (MG) is strongly associated with thymic tumors, but whether it is also associated with extrathymic cancers is debatable or whether MG can be considered a paraneoplastic disorder for extrathymic cancers. Methods This is a retrospective analysis of the MG cohort for 23 years' time (January 2000 to May 2023), extracting cancer rates with clinical, electrophysiological, and biochemical cancer associations and the effect of chronic medications. Results We identified 436 patients with MG and 3924 controls. The median age at symptom onset was 64 (5–93 years) for males and 54 (1–87 years) for females. MG symptoms at onset were recorded as ocular (60%), strictly bulbar (10%), or generalized (23%). Extrathymic cancer was found in 32% of MG patients. In 3%, thymic and extrathymic cancers co‐occurred. Compared to controls, neurology (12.2%, 159/1308), internal medicine (24.4%, 319/1308), or rheumatology (12%, 157/1308), MG patients had significantly higher rates of extrathymic cancers ( p < 0.001). Compared to the rheumatology group, the cancer relative risk of 2.97, CI = 2.5–3.4. Furthermore, the prevalence of extrathymic cancers was significantly increased within the paraneoplastic time window, defined as ±5 years from cancer diagnosis to myasthenia onset ( p < 0.01). Conclusion MG was significantly associated with an increased risk of extrathymic cancers, particularly within the paraneoplastic time window. These findings suggest that MG might potentially behave as a paraneoplastic disorder.

ABSTRACTBackground and AimsChronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical‐electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.MethodsUsing the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4‐nodopathies; (2) POEMS (polyneuropathy–organomegaly–endocrinopathy–monoclonal protein‐skin changes); (3) anti‐myelin‐associated‐glycoprotein; (4) paraneoplastic; (5) Waldenström B‐cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot–Marie–Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic‐inflammatory‐myopathies.ResultsWe analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area‐under‐curve (95% CI 91–95): progression over 8 weeks (OR 40.66, 95% CI 5.31–311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93–108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27–84.73), proximal weakness (OR 3.63, 95% CI 1.58–8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13–12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47–72.40). A web‐based probability calculator (https://news.mayocliniclabs.com/cidp‐calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering “red flags,” electrophysiologic criteria, and laboratory testing.InterpretationA probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical “red flags,” electrophysiologic demyelination, and laboratory testing.

Abstract Traumatic brain injury (TBI) annually impacts 69 million individuals worldwide. Mild TBI constitutes approximately 90% of all TBIs. Chronic pain post-mTBI occurs in 29% to 58% of patients. This study aims to introduce a predictive model for chronic pain development in individuals diagnosed with mild traumatic brain injury (mTBI) immediately postinjury. We included individuals who had sustained mTBI in motor vehicle accident (MVA). All patients had initial assessments within the first 72 hours (representing the subacute period) after the injury and performed follow-ups for 1 year. Machine learning model was applied to the integrated measures of clinical pain, pain-related psychological parameters, mTBI clinical signs, and sociodemographic information. This study included 203 patients experiencing acute head or neck pain attributable to mTBI post-MVA. We categorized these patients into 2 groups: patients who progressed to develop chronic head or neck pain (n = 89, 43.8%) and patients who recovered (low/mild pain) (n = 114, 56.2%). Severity of the subacute neck pain, number of painful body areas, and education years were identified as the most significant factors predicting chronic pain. The optimized predictive model demonstrated high efficacy, with an accuracy of 83%, a sensitivity of 92%, and an area under the receiver operating characteristic curve of 0.8. Our findings indicate feasibility in predicting chronic post-MVA pain within the critical 72-hour window postinjury using simple bedside metrics. This approach offers a promising avenue for the early detection of individuals at increased risk for chronic pain, enabling the implementation of targeted early interventions.

Background: The incidence of lymphomatous involvement of the central nervous system (CNS) has been increasing in recent years. However, the rarity of the disease has resulted in a scarcity of available data regarding its clinical presentation, natural history, and prognosis. We aimed to investigate the neurological characteristics of uncommon lymphomatous involvements confined to the CNS and to identify key variables that could serve as predictive biomarkers for treatment outcomes. Methods: We identified patients presenting with neurological symptoms and diagnosed with CNS-restricted lymphomatous involvement between 2005 and 2023. Results: We identified 44 cases, 93% of which were diagnosed with primary central nervous system lymphoma (PCNSL) and 7% with intravascular lymphoma. The median time from symptom onset to diagnosis was 47 days (range: 6–573 days), with no statistically significant difference between patients older and younger than 60 years (p = 0.22). The median follow-up time was 1144 days (range: 27–3501 days). Cognitive deterioration was the most common presenting symptom, occurring in 19 out of 44 patients (43%). Brain MRI revealed that lobar lesions were the most frequent location of lesions, found in 24 out of 44 patients (55%). By the end of the study period, 30 patients (68%) had died, with a median survival of 666 days (range: 17–3291 days). Death was significantly more common in patients who experienced relapses (p = 0.04; 95% CI: 0.99–0.03), with these patients having a four times higher chance of death (HR = 4.1; 95% CI: 1.01–16.09). The time to diagnosis significantly correlated with survival (p = 0.02; 95% CI: 0.005–0.54), as did the Eastern Cooperative Oncology Group (ECOG) performance status at the last follow-up (p = 0.006; 95% CI: 0.0012–0.62). Patients aged over 60 years did not exhibit a higher likelihood of death (p = 0.19; HR = 2.3; 95% CI: 0.63–8.61); however, the threshold age at diagnosis for the maximally predicted mortality was 64 years (ROC = 0.73; p = 0.03). Conclusions: Patients had significant delays in diagnosis, affecting patient outcomes. Cognitive deterioration and lobar lesions were prominent clinical and radiological features. Mortality was notably higher in patients with relapses and those who had a longer time to diagnosis.