International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Scenario set-up and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6

Comparing two definitions of pediatric complexity among children cared for in general and pediatric emergency departments in a statewide sample

Abstract Objective The number of children cared for in emergency departments (EDs) with medical complexity continues to rise. We sought to identify the concordance between 2 commonly used criteria of medical complexity among children presenting to a statewide sample of EDs. Methods We conducted a retrospective cross‐sectional study of children presenting to a statewide sample of Illinois EDs between 2016 and 2021. We classified patients as having medical complexity when using 2 definitions (≥1 pediatric Complex Chronic Condition [CCC] or complex chronic disease using the Pediatric Medical Complexity Algorithm [PMCA]) and compared their overlap and clinical outcomes. Results Of 6,550,296 pediatric ED encounters, CCC criteria and PMCA criteria were met in 217,609 (3.3%) and 175,708 (2.7%) encounters, respectively. Among patients with complexity, 100,015 (34.1%) met both criteria, with moderate agreement (κ = 0.49). Children with complexity by CCC had similar rates of presentation to a pediatric hospital (16.3% vs 14.8%), admission (28.5% vs 33.7%), ICU stay (10.0% vs 10.1%), and in‐hospital mortality (0.5% vs 0.5%) compared to children with complexity by PMCA. The most common visit diagnoses for children with CCCs were related to sickle cell disease with crisis (3.9%), abdominal pain (3.6%), and non‐specific chest pain (2.7%). The most common diagnoses by PMCA were related to depressive disorders (4.9%), sickle cell disease with crisis (4.8%), and seizures (3.2%). Conclusions and Relevance The CCC and PMCA criteria of multisystem complexity identified different populations, with moderate agreement. Careful selection of operational definitions is required for proper application and interpretation in clinical and health services research

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models

OBJECTIVES: Assess a machine learning method of serially updated mortality risk. DESIGN: Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO). SETTING: Hospitals caring for children in ICUs. PATIENTS: A total of 27,354 admissions cared for in ICUs from 2009 to 2018. INTERVENTIONS: None. MAIN OUTCOME: Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths. MEASUREMENTS AND MAIN RESULTS: The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p \u3c 0.001). CONCLUSIONS: Machine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M\u27s framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time

Medications for children receiving intensive care: A national sample

© 2020 Lippincott Williams and Wilkins. All rights reserved. Objective: To examine medication administration records through electronic health record data to provide a broad description of the pharmaceutical exposure of critically ill children. Design: Retrospective cohort study using the Cerner Health Facts database. Setting: United States. Patients: A total of 43,374 children 7 days old to less than 22 years old receiving intensive care with available pharmacy data. Interventions: None. Measurements and Main Results: A total of 907,440 courses of 1,080 unique medications were prescribed with a median of nine medications (range, 1-99; 25-75th percentile, 5-16) per patient. The most common medications were acetaminophen, ondansetron, and morphine. Only 45 medications (4.2%) were prescribed to more than 5% of patients, and these accounted for 442,067 (48.7%) of the total courses of medications. Each additional medication was associated with increased univariate risk of mortality (odds ratio, 1.05; 95% CI, 1.05-1.06; p \u3c 0.001). Conclusions: Children receiving intensive care receive a median of nine medications per patient and one quarter are prescribed at least than 16 medications. Only 45 medications were prescribed to more than 5% of patients, but these accounted for almost half of all medication courses