No abstract

Changes in biomarkers were poorly correlated, supporting a model of stochastic, independent change across systems. DHEAS and IL-6 tracked most closely with function, illustrating that changes in inflammation and sex steroids may play dominant roles in changes of these functional outcomes.

Context Youth with type 1 diabetes (T1D) do not meet hemoglobin A1c (HbA1c) targets. Objective To assess HbA1c outcomes in children with new onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. Method HbA1c levels were compared between the 4T and Historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post-diagnosis was estimated by cohort using a piecewise mixed effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. Setting and Participants We recruited 135 youth with newly diagnosed T1D at Stanford Children’s Health. Intervention Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. Main Outcome Measure HbA1c. Results HbA1c at 6, 9, and 12 months post-diagnosis was lower in the 4T cohort than in the Historic cohort (-0.54%, -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post-diagnosis was lower in those patients with Remote Monitoring than those without (-0.14%, -0.18%, -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (p < 0.001). Conclusions A technology-enabled team-based approach to intensified new onset education involving target setting, CGM initiation, and remote data review significantly decreased HbA1c in youth with T1D 12 months post-diagnosis.

The purpose of the study was to develop an outcome-based NeuroImaging Radiological Interpretation System (NIRIS) for patients with acute traumatic brain injury (TBI) that would standardize the interpretation of noncontrast head computer tomography (CT) scans and consolidate imaging findings into ordinal severity categories that would inform specific patient management actions and that could be used as a clinical decision support tool. We retrospectively identified all patients transported to our emergency department by ambulance or helicopter for whom a trauma alert was triggered per established criteria and who underwent a noncontrast head CT because of suspicion of TBI, between November 2015 and April 2016. Two neuroradiologists reviewed the noncontrast head CTs and assessed the TBI imaging common data elements (CDEs), as defined by the National Institutes of Health (NIH). Using descriptive statistics and receiver operating characteristic curve analyses to identify imaging characteristics and associated thresholds that best distinguished among outcomes, we classified patients into five mutually exclusive categories: 0-discharge from the emergency department; 1-follow-up brain imaging and/or admission; 2-admission to an advanced care unit; 3-neurosurgical procedure; 4-death up to 6 months after TBI. Sensitivity of NIRIS with respect to each patient's true outcome was then evaluated and compared with that of the Marshall and Rotterdam scoring systems for TBI. In our cohort of 542 patients with TBI, NIRIS was developed to predict discharge (182 patients), follow-up brain imaging/admission (187 patients), need for advanced care unit (151 patients), neurosurgical procedures (10 patients), and death (12 patients). NIRIS performed similarly to the Marshall and Rotterdam scoring systems in terms of predicting death. We developed an interpretation system for neuroimaging using the CDEs that informs specific patient management actions and could be used as a clinical decision support tool for patients with TBI. Our NIRIS classification, with evidence-based grouping of the CDEs into actionable categories, will need to be validated in different TBI populations.

The adult with congenital heart disease (CHD) is at risk of developing atherosclerotic cardiovascular disease (ASCVD). We performed a cross-sectional study to describe established ASCVD risk factors and estimate 10-year and lifetime risk of ASCVD in adults over age 18 with CHD of moderate or great complexity using three validated risk assessment tools—the Framingham Study Cardiovascular Disease Risk Assessment, the Reynolds Risk Score, and the Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator. We obtained extensive clinical and survey data on 178 enrolled patients, with average age 37.1±12.6 years, 51% men. At least one modifiable ASCVD risk factor was present in 70%; the two most common were overweight/obesity (53%) and systemic hypertension (24%). Laboratory data was available in 103 of the 178 patients. Abnormal levels of glycated hemoglobin, high-sensitivity C-reactive protein, and high-density lipoprotein (HDL) were each found in around 30% of patients. The 10-year ASCVD predicted risk using all three tools was relatively low (i.e., at least 90% of patients <10% risk), yet the median estimated lifetime risk was 36%. In conclusion, ASCVD risk factors are prevalent in adults with CHD. The risk estimation tools suggest that this population is particularly vulnerable to ASCVD with aging and should undergo guideline-based screening and management of modifiable risk factors.