Full-endoscopic discectomy for thoracic disc herniations: a single-arm meta-analysis of safety and efficacy outcomes.

INTRODUCTION: Surgical intervention is the treatment of choice in patients with thoracic disc herniation with refractory symptoms and progressive myelopathy. Due to high occurrence of complications from open surgery, minimally invasive approaches are desirable. Nowadays, endoscopic techniques have become increasingly popular and full-endoscopic surgery can be performed in the thoracic spine with low complication rates. METHODS: Cochrane Central, PubMed, and Embase databases were systematically searched for studies that evaluated patients who underwent full-endoscopic spine thoracic surgery. The outcomes of interest were dural tear, myelopathy, epidural hematoma, recurrent disc herniation, and dysesthesia. In the absence of comparative studies, a single-arm meta-analysis was performed. RESULTS: We included 13 studies with a total of 285 patients. Follow-up ranged from 6 to 89 months, age from 17 to 82 years, with 56.5% male. The procedure was performed under local anesthesia with sedation in 222 patients (77.9%). A transforaminal approach was used in 88.1% of the cases. There were no cases of infection or death reported. The data showed a pooled incidence of outcomes as follows, with their respective 95% confidence intervals (CI)-dural tear (1.3%; 95% CI 0-2.6%); dysesthesia (4.7%; 95% CI 2.0-7.3%); recurrent disc herniation (2.9%; 95% CI 0.6-5.2%); myelopathy (2.1%; 95% CI 0.4-3.8%); epidural hematoma (1.1%; 95% CI 0.2-2.5%); and reoperation (1.7%; 95% CI 0.1-3.4%). CONCLUSION: Full-endoscopic discectomy has a low incidence of adverse outcomes in patients with thoracic disc herniations. Controlled studies, ideally randomized, are warranted to establish the comparative efficacy and safety of the endoscopic approach relative to open surgery.

5-year incidence of suicide-risk in youth: A gradient tree boosting and SHAP study.

BACKGROUND: Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. METHODS: Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. RESULTS: The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. LIMITATIONS: Proximal factors associated with the incidence of suicide risk were not assessed. CONCLUSIONS: Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical non-linear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk.

Soluble amyloid-beta isoforms predict downstream Alzheimer's disease pathology.

BACKGROUND: Changes in soluble amyloid-beta (Aß) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aß levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aß isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. METHODS: We used CSF measurements of three soluble Aß peptides (Aß1-38, Aß1-40 and Aß1-42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. RESULTS: Our findings indicate that Aß isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. CONCLUSIONS: Our results demonstrate that, by applying a refined ML analysis, a combination of Aß isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.