Forns index and fatty liver index, but not FIB-4, are associated with indices of glycaemia, pre-diabetes and type 2 diabetes: analysis of The Maastricht Study.

Objective: Glucose metabolism status (GMS) is linked to non-alcoholic fatty liver disease (NAFLD). Higher levels of advanced glycation end products (AGEs) are observed in people with type 2 diabetes mellitus (T2DM) and NAFLD. We examined the association between GMS, non-invasive tests and AGEs, with liver steatosis and fibrosis.

Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

Background: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study developed a framework for assessing the impact of biological and analytical variation on the prediction uncertainty of categorical prediction models.

Enhancing Patient Understanding of Laboratory Test Results: Systematic Review of Presentation Formats and Their Impact on Perception, Decision, Action, and Memory.

Background: Direct access of patients to their web-based patient portal, including laboratory test results, has become increasingly common. Numeric laboratory results can be challenging to interpret for patients, which may lead to anxiety, confusion, and unnecessary doctor consultations. Laboratory results can be presented in different formats, but there is limited evidence regarding how these presentation formats impact patients' processing of the information.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

Background: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study.

Background: Risk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the workup and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly.

Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study.

Introduction: Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients.

Methods: In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients.

Branched-chain ketoacid co-ingestion with protein lowers amino acid oxidation during hemodialysis: A randomized controlled cross-over trial.

Background & Aims: Hemodialysis removes amino acids from the circulation, thereby stimulating muscle proteolysis. Protein ingestion during hemodialysis can compensate for amino acid removal but may also increase uremic toxin production. Branched-chain ketoacid (BCKA) co-ingestion may provide an additional anabolic stimulus without adding to uremic toxin accumulation.

Associations between plasma sulfur amino acids and specific fat depots in two independent cohorts: CODAM and The Maastricht Study.

Purpose: Sulfur amino acids (SAAs) have been associated with obesity and obesity-related metabolic diseases. We investigated whether plasma SAAs (methionine, total cysteine (tCys), total homocysteine, cystathionine and total glutathione) are related to specific fat depots.

Methods: We examined cross-sectional subsets from the CODAM cohort (n = 470, 61.

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort.

Objectives: Predicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only.

Intradialytic Protein Ingestion and Exercise do Not Compromise Uremic Toxin Removal Throughout Hemodialysis.

Objective: Dietary protein and physical activity interventions are increasingly implemented during hemodialysis to support muscle maintenance in patients with end-stage renal disease (ESRD). Although muscle maintenance is important, adequate removal of uremic toxins throughout hemodialysis is the primary concern for patients. It remains to be established whether intradialytic protein ingestion and/or exercise modulate uremic toxin removal during hemodialysis.

Metformin and high-sensitivity cardiac troponin I and T trajectories in type 2 diabetes patients: a post-hoc analysis of a randomized controlled trial.

Background: Metformin has favorable effects on cardiovascular outcomes in both newly onset and advanced type 2 diabetes, as previously reported findings from the UK Prospective Diabetes Study and the HOME trial have demonstrated. Patients with type 2 diabetes present with chronically elevated circulating cardiac troponin levels, an established predictor of cardiovascular endpoints and prognostic marker of subclinical myocardial injury. It is unknown whether metformin affects cardiac troponin levels.

Fructose Intake From Fruit Juice and Sugar-Sweetened Beverages Is Associated With Higher Intrahepatic Lipid Content: The Maastricht Study.

Objective: Epidemiological evidence regarding the relationship between fructose intake and intrahepatic lipid (IHL) content is inconclusive. We, therefore, assessed the relationship between different sources of fructose and IHL at the population level.

Research Design And Methods: We used cross-sectional data from The Maastricht Study, a population-based cohort study (n = 3,981; mean ± SD age: 60 ± 9 years; 50% women).

Sex Differences in Cardiac Troponin I and T and the Prediction of Cardiovascular Events in the General Population.

Background: Cardiac troponin concentrations differ in women and men, but how this influences risk prediction and whether a sex-specific approach is required is unclear. We evaluated whether sex influences the predictive ability of cardiac troponin I and T for cardiovascular events in the general population.

Methods: High-sensitivity cardiac troponin (hs-cTn) I and T were measured in the Generation Scotland Scottish Family Health Study of randomly selected volunteers drawn from the general population between 2006 and 2011.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

Background: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

Biological variation of cardiac myosin-binding protein C in healthy individuals.

Objectives: Cardiac myosin-binding protein C (cMyC) is a novel biomarker of myocardial injury, with a promising role in the triage and risk stratification of patients presenting with acute cardiac disease. In this study, we assess the weekly biological variation of cMyC, to examine its potential in monitoring chronic myocardial injury, and to suggest analytical quality specification for routine use of the test in clinical practice.

Methods: Thirty healthy volunteers were included.

Sex differences in investigations and outcomes among patients with type 2 myocardial infarction.

Objectives: Type 2 myocardial infarction (MI) is a heterogenous condition and whether there are differences between women and men is unknown. We evaluated sex differences in clinical characteristics, investigations and outcomes in patients with type 2 MI.

Methods: In the Swedish Web based system for Enhancement and Development of Evidence based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) registry, we compared patients admitted to coronary care units with a diagnosis of type 1 or type 2 MI.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

Introduction: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.

Decreased serial scores of severe organ failure assessments are associated with survival in mechanically ventilated patients; the prospective Maastricht Intensive Care COVID cohort.

Background: The majority of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are admitted to the Intensive Care Unit (ICU) for mechanical ventilation. The role of multi-organ failure during ICU admission as driver for outcome remains to be investigated yet.

Design And Setting: Prospective cohort of mechanically ventilated critically ill with SARS-CoV-2 infection.

Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course: design of the Maastricht Intensive Care COVID cohort (MaastrICCht).

Introduction: The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information.