Citations

Background: Assessment of heterogeneity in meta-analyses is critical to ensure the consistency of pooled results. Therefore, we sought to assess the evaluation and reporting of heterogeneity in heart failure (HF) meta-analyses. Methods: Study level meta-analyses pertaining to HF were selected from January 2009 to July 2019, published in 11 high impact factor journals. We tabulated the overall proportion of the meta-analyses reporting statistical heterogeneity and specific metrics and methods employed to quantify and explore heterogeneity. Results: Of 126 HF meta-analyses (612 outcomes), heterogeneity was reported for 422 outcomes (68.9 %) in 108 meta-analyses. Out of the 422 outcomes reporting statistical heterogeneity, 137 outcomes (32.5%) had no observable heterogeneity: ( I 2 =0%), 40 outcomes (9.5%) had low heterogeneity ( I 2 <25%), 76 outcomes (18%) had moderate heterogeneity ( I 2 =25%–50%), and 169 outcomes (40%) had high heterogeneity ( I 2 >50%). Reporting of statistical heterogeneity was not significantly associated with year of publication, funding source, disclosure information, or the type of studies pooled. Sensitivity analysis (n=68) was the most common statistical technique employed to evaluate the source of heterogeneity followed by subgroup analyses (n=59) and meta-regression (n=40). Conclusions: Despite being an essential component of meta-analyses, heterogeneity was not reported for nearly 30% of outcomes and variably handled in contemporary HF meta-analyses. As meta-analyses increase across HF science, interpreting and handling of heterogeneity should be standardized.

Summary: Social interactions are largely dependent on the interpretation of information conveyed through facial expressions. Although facial reanimation seeks restoration of the facial expression of emotion, outcome measures have not addressed this directly. This study evaluates the use of a machine learning technology to directly measure facial expression before and after facial reanimation surgery. Fifteen study subjects with facial palsy were evaluated both before and after undergoing cross-facial nerve grafting and free gracilis muscle transfer. Eight healthy volunteers were assessed for control comparison. Video footage of subjects with their face in repose and with a posed, closed-lip smile was obtained. The video data were then analyzed using the Noldus FaceReader software application to measure the relative proportions of seven cardinal facial expressions detected within each clip. The facial expression recognition application detected a far greater happy signal in postoperative (42 percent) versus preoperative (13 percent) smile videos (p < 0.0001), compared to 53 percent in videos of control faces smiling. This increase in postoperative happy signal was achieved in exchange for a reduction in the sad signal (15 percent to 9 percent; p = 0.092) and the neutral signal (57 percent to 37 percent; p = 0.0012). For video clips of patients in repose, no significant difference in happy emotion was detected between preoperative (3.1 percent) and postoperative (1.4 percent) states (p = 0.5). This study provides the first proof of concept for the use of a machine learning software application to objectively quantify facial expression before and after surgical reanimation. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.

Abstract ZUMA-1 demonstrated a high rate of durable response and a manageable safety profile with axicabtagene ciloleucel (axi-cel), an anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, in patients with refractory large B-cell lymphoma. As previously reported, prespecified clinical covariates for secondary end point analysis were not clearly predictive of efficacy; these included Eastern Cooperative Oncology Group performance status (0 vs 1), age, disease subtype, disease stage, and International Prognostic Index score. We interrogated covariates included in the statistical analysis plan and an extensive panel of biomarkers according to an expanded translational biomarker plan. Univariable and multivariable analyses indicated that rapid CAR T-cell expansion commensurate with pretreatment tumor burden (influenced by product T-cell fitness), the number of CD8 and CCR7+CD45RA+ T cells infused, and host systemic inflammation, were the most significant determining factors for durable response. Key parameters differentially associated with clinical efficacy and toxicities, with both theoretical and practical implications for optimizing CAR T-cell therapy. This trial was registered at www.clinicaltrials.gov as #NCT02348216.

Abstract Introduction The cytoprotective PTEN‐induced kinase 1 (PINK1)‐parkin RBR E3 ubiquitin protein ligase (PRKN) pathway selectively labels damaged mitochondria with phosphorylated ubiquitin (pS65‐Ub) for their autophagic removal (mitophagy). Because dysfunctions of mitochondria and degradation pathways are early features of Alzheimer's disease (AD), mitophagy impairments may contribute to the pathogenesis. Methods Morphology, levels, and distribution of the mitophagy tag pS65‐Ub were evaluated by biochemical analyses combined with tissue and single cell imaging in AD autopsy brain and in transgenic mouse models. Results Analyses revealed significant increases of pS65‐Ub levels in AD brain, which strongly correlated with granulovacuolar degeneration (GVD) and early phospho‐tau deposits, but were independent of amyloid beta pathology. Single cell analyses revealed predominant co‐localization of pS65‐Ub with mitochondria, GVD bodies, and/or lysosomes depending on the brain region analyzed. Discussion Our study highlights mitophagy alterations in AD that are associated with early tau pathology, and suggests that distinct mitochondrial, autophagic, and/or lysosomal failure may contribute to the selective vulnerability in disease.

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.