Positional shunt assist for slit ventricle syndrome.

PURPOSE: Hydrocephalus is one of the most common pathologies in pediatric neurosurgery. One of the causes of recurring events of headaches among shunted children is "slit ventricle syndrome" (SVS). Several potential treatments have been proposed, yet SVS often represents a treatment challenge. The goal of the current series is to present our experience with adding a positional shunt-assist (SA) (Miethke, Aesculap) for the treatment of SVS. METHODS: Clinical data was retrospectively collected from all consecutive children with SVS that were treated with SA (Miethke, Aesculap) at our center. Surgical and clinical outcomes as expressed by hospital visits, or need for additional surgery, were evaluated. RESULTS: Nine cases were included. Hydrocephalus etiology included IVH (6), postinfectious (1), and congenital syndromes (2). Average age at first shunt was 4 months. Primary shunt type was differential-pressure-valve in all. Average age at SVS onset was 4 years. Average age at SA placement was 5.5 years. There were no perioperative complications besides a single stich abscess. A 6-month follow-up period after SA was compared to a 6-month period prior to the SA: average hospital visits decreased from 2.4 to 0.6 per patient (p < 0.0002). 4/9 patients needed an LP or shunt revision before the SA surgery, while no procedure was indicated during the immediate 6-month follow-up. At the last follow-up, there was a significant reduction in the rate of ER visits compared to prior to surgery; however, the number of neurosurgical procedures did not significantly differ. CONCLUSION: Using a SA for SVS was associated with a short-term improvement of symptoms in the majority of cases, reduction in hospital visits, and reduced need for SVS-related procedures.

Differentiating spinal pathologies by deep learning approach.

BACKGROUND CONTEXT: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clinical judgment incorporating lab findings and radiological studies. When the diagnosis is uncertain, a biopsy is almost always mandatory since the treatment is dictated by the type of pathology. This is an invasive, timely, and costly process. PURPOSE: The aim of this study was to develop a deep learning (DL) algorithm, based on preoperative MRI and post-operative pathological results, to differentiate between leading spinal pathologies. STUDY DESIGN: We retrospectively collected and analyzed clinical, radiological, and pathological data of patients who underwent spinal surgery or biopsy for various spinal pathologies between 2008 and 2022 at a tertiary center. The patients were stratified according to their pathological reports (the threshold for inclusion was set to 25 patients per diagnosis). METHODS: Preoperative MRI, clinical data, and pathological results were processed by a deep learning model built on the Fast.ai framework on top of the PyTorch environment. RESULTS: A total of 231 patients diagnosed with carcinoma (80), infection (57), meningioma (52), or schwannoma (42), were included in our model. The mean overall accuracy was 0.78±0.06 for the validation, and 0.93±0.03 for the test dataset. CONCLUSION: Deep learning algorithm for differentiation between the aforementioned spinal pathologies, based solely on clinical MRI, proves as a feasible primary diagnostic modality. Larger studies should be performed to validate and improve this algorithm for clinical use. CLINICAL SIGNIFICANCE: This study provides a proof-of-concept for predicting spinal pathologies solely by MRI based DL technology, allowing for a rapid, targeted, and cost-effective work-up and subsequent treatment.

Diagnostic delays among COVID-19 patients with a second concurrent diagnosis.

BACKGROUND: Little is known about the effect of a new pandemic on diagnostic errors. OBJECTIVE: We aimed to identify delayed second diagnoses among patients presenting to the emergency department (ED) with COVID-19. DESIGNS: An observational cohort Study. SETTINGS AND PARTICIPANTS: Consecutive hospitalized adult patients presenting to the ED of a tertiary referral center with COVID-19 during the Delta and Omicron variant surges. Included patients had evidence of a second diagnosis during their ED stay. MAIN OUTCOME AND MEASURES: The primary outcome was delayed diagnosis (without documentation or treatment in the ED). Contributing factors were assessed using two logistic regression models. RESULTS: Among 1249 hospitalized COVID-19 patients, 216 (17%) had evidence of a second diagnosis in the ED. The second diagnosis of 73 patients (34%) was delayed, with a mean (SD) delay of 1.5 (0.8) days. Medical treatment was deferred in 63 patients (86%) and interventional therapy in 26 (36%). The probability of an ED diagnosis was the lowest for Infection-related diagnoses (56%) and highest for surgical-related diagnoses (89%). Evidence for the second diagnosis by physical examination (adjusted odds ratios [AOR] 2.35, 95% confidence interval [CI] 1.20-4.68) or by imaging (AOR 2.10, 95% CI 1.16-3.79) were predictors for ED diagnosis. Low oxygen saturation (AOR 0.38, 95% CI 0.18-0.79) and cough or dyspnea (AOR 0.48, 95% CI 0.25-0.94) in the ED were predictors of a delayed second diagnosis.