Increasing evidence suggests that ion channels not only regulate electric signaling in excitable cells but also play important roles in the development of brain tumor. However, the roles of ion channels in glioma remain controversial. In the present study, we systematically analyzed the expression patterns of ion channel genes in a cohort of Chinese patients with glioma using RNAseq expression profiling. First, a molecular signature comprising three ion channel genes (KCNN4, KCNB1 and KCNJ10) was identified using Univariate Cox regression and two-tailed student's t test conducted in overall survival (OS) and gene expression. We assigned a risk score based on three ion channel genes to each primary Glioblastoma multiforme (pGBM) patient. We demonstrated that pGBM patients who had a high risk of unfavorable outcome were sensitive to chemotherapy. Next, we screened the three ion genes-based signature in different molecular glioma subtypes. The signature showed a Mesenchymal subtype and wild-type IDH1 preference. Gene ontology (GO) analysis for the functional annotation of the signature showed that patients with high-risk scores tended to exhibit the increased expression of proteins associated with apoptosis, immune response, cell adhesion and motion and vasculature development. Gene Set Enrichment Analysis (GSEA) results showed that pathways associated with negative regulation of programmed cell death, cell proliferation and locomotory behavior were highly expressed in the high-risk group. These results suggest that ion channel gene expression could improve the subtype classification in gliomas at the molecular level. The findings in the present study have been validated in two independent cohorts.

Major teaching hospitals are significantly more likely to provide care for minorities and patients requiring transfer from other institutions for advanced care.Both are essential to an equitable and high-quality regional health care system.

Objective To assess the quantity and impact of research publications among US acute care hospitals; to identify hospital characteristics associated with publication volumes; and to estimate the independent association of bibliometric indicators with Hospital Compare quality measures. Data Sources Hospital Compare; American Hospital Association Survey; Magnet Recognition Program; Science Citation Index Expanded. Study Design In cross‐sectional studies using a 40% random sample of US Medicare‐participating hospitals, we estimated associations of hospital characteristics with publication volumes and associations of hospital‐linked bibliometric indicators with 19 Hospital Compare quality metrics. Data Collection/Extraction Methods Using standardized search strategies, we identified all publications attributed to authors from these institutions from January 1, 2015 to December 31, 2016 and their subsequent citations through July 2020. Principal Findings Only 647 of 1604 study hospitals (40.3%) had ≥1 publication. Council of Teaching Hospitals and Health Systems (COTH) hospitals had significantly more publications (average 599 vs. 11 for non‐COTH teaching and 0.6 for nonteaching hospitals), and their publications were cited more frequently (average 22.6/publication) than those from non‐COTH teaching (18.2 citations) or nonteaching hospitals (12.8 citations). In multivariable regression, teaching intensity, hospital beds, New England or Pacific region, and not‐for‐profit or government ownership were significant predictors of higher publication volumes; the percentage of Medicaid admissions was inversely associated. In multivariable linear regression, hospital publications were associated with significantly lower risk‐adjusted mortality rates for acute myocardial infarction (coefficient −0.52, p = 0.01), heart failure (coefficient −0.74, p = 0.004), pneumonia (coefficient −1.02, p = 0.001), chronic obstructive pulmonary disease (coefficient −0.48, p = 0.005), and coronary artery bypass surgery (coefficient −0.73, p < 0.0001); higher overall Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (coefficient 2.37, p = 0.04); and greater patient willingness to recommend (coefficient 3.38, p = 0.01). Conclusions A minority of US hospitals published in the biomedical literature. Publication quantity and impact indicators are independently associated with lower risk‐adjusted mortality and higher HCAHPS scores.

In a large cross-sectional study from a diverse hospital cohort, AHRQ safety culture scores were not associated with AMI mortality. Our study adds to a growing body of investigations that have failed to conclusively demonstrate a safety culture-outcome association in health care, at least with widely used national survey instruments.

BackgroundNational Hospital Quality Measures (NHQM) should accurately reflect quality of care, as they increasingly impact reimbursement and reputation. However, similar to risk adjustment of outcomes measures, NHQM process measures pose unique methodological concerns, including lack of representativeness of the final denominator population after exclusions. This study determines population size and characteristics for each acute myocardial infarction (AMI) measure, reasons for exclusion from the measures, and variation in exclusion rates among hospitals.Methods and results163 144 discharges from 172 University HealthSystem Consortium hospitals between 2008-Q4 and 2013-Q3 were examined, including characteristics and propensity scores of included and excluded groups. Measure exclusions ranged from 17.8% (discharge aspirin) to 90.1% (percutaneous coronary intervention, PCI, within 90 min), with substantial variation across hospitals. Median annual denominator size (IQR) for PCI within 90 min was 28 (20, 44) at major teaching hospitals, versus 10 (0, 25) at non-teaching hospitals. Patients most likely to be excluded (in the 10th vs 1st propensity decile) were older (mean age (SD) of 78.1 (10.8) vs 50.3 (8.6) years), more likely to have Medicare (90.5% vs 0.9%), had more documented comorbidities (15.6 (4.6) vs 6.2 (2.5) hierarchical clinical condition categories) and higher admission mortality risk (Major or Extreme 80.9% vs 7.3%, respectively), and experienced higher inpatient mortality (10.0% vs 1.6%).ConclusionsExclusion from AMI measures varied substantially among hospitals, sample sizes were very small for some measures (PCI and ACE inhibitor measures) and measures often excluded high-risk populations. This has implications for the representativeness and comparability of the measures and provides insight for future measure development.