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The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

Anaemia is a common sequela of surgery, although its relationship with patient recovery is unclear. The goal of this investigation was to assess the associations between haemoglobin concentrations at the time of hospital discharge following major surgery and early post-hospitalisation outcomes, with a primary outcome of 30 day unanticipated hospital readmissions. This investigation includes data from two independent population-based observational cohorts of adult surgical patients (aged ≥ 18 years) requiring postoperative intensive care unit admission between 1 January 2010 and 31 December 2019 in hospitals in Olmsted County, Minnesota, and between 1 July 2010 and 30 June 2017 in the Kaiser Permanente Northern California integrated healthcare system, California. Cox proportional hazards models assessed the associations between discharge haemoglobin concentrations (per 10 g.l-1 ) and outcomes, with prespecified multivariable adjustment. A total of 3260 patients were included from Olmsted County hospitals and 29,452 from Kaiser Permanente Northern California. In adjusted analyses, each 10 g.l-1 decrease in haemoglobin at hospital discharge was associated with a 9% (hazard ratio 1.09, 95%CI 1.02-1.18; p = 0.014) and 8% increase (hazard ratio 1.08, 95%CI 1.06-1.11; p

BackgroundThe Pulmonary Embolism (PE) Severity Index identifies emergency department (ED) patients with acute PE that can be safely managed without hospitalization. However, the Index comprises 11 weighted variables, complexity that can impede its integration into contextual workflow.ObjectiveWe designed a computerized version of the PE Severity Index (e-Index) to automatically extract the required variables from discrete fields in the electronic health record (EHR). We tested the e-Index on the study population to determine its accuracy compared with a gold standard generated by physician abstraction of the EHR on manual chart review.MethodsThis retrospective cohort study included adults with objectively-confirmed acute PE in four community EDs from 2010-2012. Outcomes included performance characteristics of the e-Index for individual values, the number of cases requiring physician editing, and the accuracy of the e-Index risk category (low vs. higher).ResultsFor the 593 eligible patients, there were 6,523 values automatically extracted. Fifty one of these needed physician editing, yielding an accuracy at the value-level of 99.2% (95% confidence interval [CI], 99.0%-99.4%). Sensitivity was 96.9% (95% CI, 96.0%-97.9%) and specificity was 99.8% (95% CI, 99.7%-99.9%). The 51 corrected values were distributed among 47 cases: 43 cases required the correction of one variable and four cases required the correction of two. At the risk-category level, the e-Index had an accuracy of 96.8% (95% CI, 95.0%-98.0%), under-classifying 16 higher-risk cases (2.7%) and over-classifying 3 low-risk cases (0.5%).ConclusionOur automated extraction of variables from the EHR for the e-Index demonstrates substantial accuracy, requiring a minimum of physician editing. This should increase user acceptability and implementation success of a computerized clinical decision support system built around the e-Index, and may serve as a model to automate other complex risk stratification instruments.

Objective To test the hypothesis that children and adults with a history of Kawasaki disease (KD) are more likely to have abnormal lipoprotein particle profiles that could place them at increased risk for developing atherosclerosis later in life.

Background Blood conservation strategies have been shown to be effective in decreasing red blood cell (RBC) utilization in specific patient groups. However, few data exist describing the extent of RBC transfusion reduction or their impact on transfusion practice and mortality in a diverse inpatient population.

Seasonal Variations of Arrhythmias and Their Impact on Mortality in Cancer Patients with Health Disparities: A Propensity Score Adjusted Machine Learning Analysis of over 100 Million Hospitalizations Across 3 Years