Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization.

Background: In France an average of 4% of hospitalized patients die during their hospital stay. To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential.

Methods: We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at ≥ 2 and ≤ 30 days after admission).

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018.

Purpose: In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes.

The burden of chronic pain for patients with osteoarthritis in Germany: a retrospective cohort study of claims data.

Background: Osteoarthritis (OA) is a common condition that is often associated with chronic pain. Pain often leads patients to seek healthcare advice and treatment. In this retrospective cohort analysis of German longitudinal healthcare claims data, we aimed to explore the healthcare resource utilisation (HRU) and related healthcare costs for patients with OA who develop chronic pain.

Carotid IMT and Stiffness in the KiGGS 2 National Survey: Third-Generation Measurement, Quality Algorithms and Determinants of Completeness.

Carotid intima-media thickness (cIMT) and carotid stiffness (CS) are important markers of atherosclerotic risk in the young. We assessed a novel third-generation method for its applicability in large population-based epidemiologic studies to determine strengths, limitations, completeness and predictors of unsuccessful measurement. Four thousand seven hundred ninety-eight 14- to 31-y-old participants of the German KiGGS cohort, which is based on a nationally representative sample with 11-y follow-up, underwent B-mode ultrasound examinations of the left and right common carotid artery with semi-automatic edge detection and automatic electrocardiogram-gated real-time quality control based on a sophisticated snake algorithm and subpixel interpolation.

New data for action. Data collection for KiGGS Wave 2 has been completed.

The fieldwork of the second follow-up to the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) was completed in August 2017. KiGGS is part of the Robert Koch Institute's Federal Health Monitoring. The study consists of the KiGGS cross-sectional component (a nationally representative, periodic cross-sectional survey of children and adolescents aged between 0 and 17) and the KiGGS cohort (the follow-up into adulthood of participants who took part in the KiGGS baseline study).