A Possible Newly Defined and Treatable Secondary Cause of Early Morning Wake-Up Headaches in an Older Hypermobile Woman: Nutcracker Physiology with Spinal Epidural Venous Congestion

Introduction Left renal vein compression (nutcracker physiology) with secondary spinal epidural venous congestion is a newly recognized cause of daily persistent headache. Presently only women with underlying symptomatic hypermobility issues appear to develop headache from this anatomic issue. The hypothesized etiology is an abnormal reset of the patient’s cerebrospinal fluid (CSF) pressure to an elevated state. Headaches that occur during sleep can have a varied differential diagnosis, one of which is elevated CSF pressure. We present the case of an older woman who began to develop severe wake-up headaches at midnight. She was found to have left renal vein compression and spinal epidural venous congestion on imaging. After treatment with lumbar vein coil embolization, which alleviated the spinal cord venous congestion, her headaches alleviated. Case Presentation A 61-year-old woman with a history of hypermobile Ehlers-Danlos syndrome, began to be awakened with severe head pain at midnight at least several times per week. The headache was a holocranial, pressure sensation, which worsened in the supine position. The headaches were mostly eliminated with acetazolamide. Because of her hypermobility issues and pressure-like headache she was investigated for underlying nutcracker physiology and spinal epidural venous congestion. This was confirmed using magnetic resonance (MR) angiography and conventional venography, and after lumbar vein coil embolization her wake-up headaches ceased. Conclusion The case report suggests a possible new underlying and treatable cause for early morning, wake-up, headaches: nutcracker physiology with secondary spinal epidural venous congestion. The case expands on the clinical headache presentation of nutcracker physiology

Safety of Radioembolization in the Setting of Angiographically Apparent Arterioportal Shunting

Purpose: To retrospectively analyze adverse events (AE) in patients with hepatocellular carcinoma (HCC) treated with yttrium-90 radioembolization in the setting of angiographically apparent arterioportal shunts (APSs). Materials and Methods: Thirty-two patients with HCC underwent radioembolization with APSs from January 2011 to September 2016, totaling 34 administrations using resin (6) and glass (28) microspheres. APSs were graded angiographically as segmental (9), ipsilobar (15), contralobar (7), or main portal (2), according to portal perfusion. Tumors were categorized as solitary (9), multifocal (7), or infiltrative (16). Both unilobar (25) and bilobar (7) disease was treated. Child Pugh Score was A (22), B (10), or C (2), with a median Model for End-Stage Liver Disease (MELD)/Na-MELD of 8/8.5. Median procedure dose was 132.6 Gy. AEs were graded using Combined Terminology Criteria for Adverse Events (CTCAE) version 4.0. Tumor response was assessed using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Results: CTCAE grade >= 3 AEs were observed in 22% of patients. Barcelona Clinic Liver Cancer (BCLC) C patients with nonsegmental shunts who received lobar administrations had a grade >= 3 AE rate of 38% compared with the remaining cohort, which was 12% (P = .076). No events were reported in patients with segmental shunts (P = .023). Imaging analysis revealed mRECIST complete response (17), partial response (13), stable disease (3), and progressive disease (1). Overall survival at 6 months and 12 months was 72% and 57%, respectively. Conclusions: Radioembolization in the setting of APS may have a higher AE profile than reported literature when BCLC-C patients with nonsegmental shunts receive lobar administrations. Segmental shunts are generally well tolerated

Evaluating the Performance of a Commercially Available Artificial Intelligence Algorithm for Automated Detection of Pulmonary Embolism on Contrast-Enhanced Computed Tomography and Computed Tomography Pulmonary Angiography in Patients With Coronavirus Disease 2019

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P<.001), with an optimal Hounsfield unit of 362 (P=.048). Conclusion: The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast-enhanced CT scans in patients with COVID-19 regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known