Minimally Invasive Posterior Facet Decortication and Fusion Using Navigated Robotic Guidance: Feasibility and Workflow Optimization

Minimally invasive spine surgery reduces tissue dissection and retraction, decreasing the morbidity associated with traditional open spine surgery by decreasing blood loss, blood transfusion, complications, and pain. One of the key challenges with a minimally invasive approach is achieving consistent posterior fusion. Although advantageous in all fusion surgeries, solid posterior fusion is particularly important in spinal deformity, revisions, and fusions without anterior column support. A minimally invasive surgical approach accomplished without sacrificing the quality of the posterior fusion has the potential to decrease both short- and long-term complications compared to the traditional open techniques. Innovations in navigated and robotic-assisted spine surgery continue to address this need. In this article, we will outline the feasibility of achieving posterior facet fusion using the Mazor X Stealth Edition Robotic Guidance System

One-year outcomes after low-dose intracoronary alteplase during primary percutaneous coronary intervention. The T-TIME randomized trial

No abstract available

Development and Validation of a Mortality Prediction Model in Extremely Low Gestational Age Neonates

Introduction: Understanding factors that associate with neonatal death may lead to strategies or interventions that can aid clinicians and inform families. Objective: The aim of the study was to develop an early prediction model of neonatal death in extremely low gestational age (ELGA, <28 weeks) neonates. Methods: A predictive cohort study of ELGA neonates was derived from the Swedish Neonatal Quality Register between the years 2011 to May 2021. The goal was to use readily available clinical variables, collected within the first hour of birth, to predict in-hospital death. Data were split into a train cohort (80%) to build the model and tested in 20% of randomly selected neonates. Model performance was assessed via area under the receiver operating characteristic curve (AUC) and compared to validated mortality prediction models and an external cohort of neonates. Results: Among 3,752 live-born extremely preterm infants (46% girls), in-hospital mortality was 18% (n = 685). The median gestational age and birth weight were 25.0 weeks (interquartile range [IQR] 24.0, 27.0) and 780 g (IQR 620, 940), respectively. The proposed model consisted of three variables: birth weight (grams), Apgar score at 5 min of age, and gestational age (weeks). The BAG model had an AUC of 76.9% with a 95% confidence interval (CI) (72.6%, 81.3%), while birth weight and gestational age had an AUC of 73.1% (95% CI: 68.4%,77.9%) and 71.3% (66.3%, 76.2%). In the validation cohort, the BAG model had an AUC of 68.9%. Conclusion: The BAG model is a new mortality prediction model in ELGA neonates that was developed using readily available information

Larger temporal volume in elderly with high versus low beta-amyloid deposition

beta-Amyloid deposition is one of the main hallmarks of Alzheimer's disease thought to eventually cause neuronal death. Post-mortem and neuroimaging studies have consistently reported cases with documented normal cognition despite high beta-amyloid burden. It is of great interest to understand what differentiates these particular subjects from those without beta-amyloid deposition or with both beta-amyloid deposition and cognitive deficits, i.e. what allows these subjects to resist the damage of the pathological lesions. [(11)C]Pittsburgh compound B positron emission tomography and magnetic resonance brain scans were obtained in 149 participants including healthy controls and patients with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease. Magnetic resonance data were compared between high versus low-[11C]Pittsburgh compound B cases, and between high-[(11)C]Pittsburgh compound B cases with versus those without cognitive deficits. Larger temporal (including hippocampal) grey matter volume, associated with better episodic memory performance, was found in high- versus low-[(11)C]Pittsburgh compound B healthy controls. The same finding was obtained using different [(11)C]Pittsburgh compound B thresholds, correcting [(11)C]Pittsburgh compound B data for partial averaging, using age, education, Mini-Mental State Examination, apolipoprotein E4 and sex-matched subsamples, and using manual hippocampal delineation instead of voxel-based analysis. By contrast, in participants with subjective cognitive impairment, significant grey matter atrophy was found in high-[(11)C]Pittsburgh compound B cases compared to low-[(11)C]Pittsburgh compound B cases, as well as in high-[(11)C]Pittsburgh compound B cases with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease compared to high-[(11)C]Pittsburgh compound B healthy controls. Larger grey matter volume in high-[(11)C]Pittsburgh compound B healthy controls may reflect either a tissue reactive response to beta-amyloid or a combination of higher 'brain reserve' and under-representation of subjects with standard/low temporal volume in the high-[(11)C]Pittsburgh compound B healthy controls. Our complementary analyses tend to support the latter hypotheses. Overall, our findings suggest that the deleterious effects of beta-amyloid on cognition may be delayed in those subjects with larger brain (temporal) volume

Low-dose intracoronary alteplase during primary percutaneous coronary intervention in patients with acute myocardial infarction: the T-TIME three-arm RCT

Adjunctive intracoronary alteplase at low-dose (20mg or 10mg) given during the primary percutaneous intervention compared with placebo did not reduce microvascular obstruction

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials

Various neuroimaging measures are being evaluated for tracking Alzheimer’s disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI