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BackgroundDirect 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. ObjectiveThis study aims to synthesize the evidence on effective formats for presenting numeric laboratory test results with a focus on outcomes related to patients’ information processing, including affective perception, perceived magnitude, cognitive perception, perception of communication, decision, action, and memory. MethodsThe search was conducted in 3 databases (PubMed, Web of Science, and Embase) from inception until May 31, 2023. We included quantitative, qualitative, and mixed methods articles describing or comparing formats for presenting diagnostic laboratory test results to patients. Two reviewers independently extracted and synthesized the characteristics of the articles and presentation formats used. The quality of the included articles was assessed by 2 independent reviewers using the Mixed Methods Appraisal Tool. ResultsA total of 18 studies were included, which were heterogeneous in terms of study design and primary outcomes used. The quality of the articles ranged from poor to excellent. Most studies (n=16, 89%) used mock test results. The most frequently used presentation formats were numerical values with reference ranges (n=12), horizontal line bars with colored blocks (n=12), or a combination of horizontal line bars with numerical values (n=8). All studies examined perception as an outcome, while action and memory were studied in 1 and 3 articles, respectively. In general, participants’ satisfaction and usability were the highest when test results were presented using horizontal line bars with colored blocks. Adding reference ranges or personalized information (eg, goal ranges) further increased participants’ perception. Additionally, horizontal line bars significantly decreased participants’ tendency to search for information or to contact their physician, compared with numerical values with reference ranges. ConclusionsIn this review, we synthesized available evidence on effective presentation formats for laboratory test results. The use of horizontal line bars with reference ranges or personalized goal ranges increased participants’ cognitive perception and perception of communication while decreasing participants’ tendency to contact their physicians. Action and memory were less frequently studied, so no conclusion could be drawn about a single preferred format regarding these outcomes. Therefore, the use of horizontal line bars with reference ranges or personalized goal ranges is recommended to enhance patients’ information processing of laboratory test results. Further research should focus on real-life settings and diverse presentation formats in combination with outcomes related to patients’ information processing.

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. Still, validation in low-risk patients, which is clinically most relevant, is lacking.Design Retrospective cohort study.Setting Secondary outpatient clinic care in one Dutch academic hospital.Participants We included 696 patients referred from primary care for further testing regarding the presence or absence of CAD. The results were compared with PTP and CAD2 using receiver operating characteristic (ROC) curves (area under the curve (AUC)). CAD was defined by a coronary stenosis >50% in at least one coronary vessel in invasive coronary or CT angiography, or having a coronary event within 6 months.Outcome measures The first cohort validating the memetic pattern-based algorithm (MPA) model developed in two high-risk populations in a low-risk to intermediate-risk cohort to improve risk stratification for non-invasive diagnosis of the presence or absence of CAD.Results The population contained 49% male, average age was 65.6±12.6 years. 16.2% had CAD. The AUCs of the MPA model, the PTP and the CAD2 were 0.87, 0.80, and 0.82, respectively. Applying the MPA model resulted in possible discharge of 67.7% of the patients with an acceptable CAD rate of 4.2%.Conclusions In this low-risk to intermediate-risk population, the MPA model provides a good risk stratification of presence or absence of CAD with a better ROC compared with traditional risk scores. The results are promising but need prospective confirmation.

IntroductionPatients 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.MethodsA single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.ResultsA number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82).ConclusionMachine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

BackgroundClosed-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.MethodsWe used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).ResultsModels trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).ConclusionsMachine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Objective Cardiac biomarkers hold promise for follow-up and management of aortic valve stenosis (AVS). When interpreting serial biomarker measurements of patients with AVS, it can be challenging to distinguish ‘real changes’ from ‘random fluctuation’. Hence, robust estimation of the biological variation of these biomarkers is essential. In the present study we assessed biological variation of B-type natriuretic peptide (BNP), N-terminus pro-brain natriuretic peptide (NT-proBNP), high-sensitivity troponin-T and high-sensitivity troponin-I (hs-TnT and hs-TnI), and ST2 in subjects with stable AVS.Methods Serial blood sampling was performed in 25 subjects with moderate AVS—confirmed by echocardiography—and all free from acute cardiovascular events in the past 6 months. Blood samples were taken on seven standardised occasions during 1 year. Analytical variation (CVA), within-subject biological variation (CVI), between-subject biological variation (CVG), index of individuality (II) and reference change values were calculated for all cardiac biomarkers.Results CVI was highest for BNP (62.0%, 95% CI 52.5 to 75.4) and lowest for hs-TnI (9.2%, 95% CI 2.8 to 13.8). CVG exceeded the CVI for all biomarkers except BNP, and ranged from 19.8% (95% CI 13.8 to 33.4) for ST2 to 57.2% (95% CI 40.4 to 97.3) for hs-TnT. NT-proBNP, hs-TnT and ST2 revealed CVA

Cardiac troponin is the biochemical gold standard to diagnose acute myocardial infarction. Interestingly however, elevated cardiac troponin concentrations are also frequently observed during and after endurance-type exercise. Oxidative stress associated with prolonged exercise has been proposed to contribute to cardiac troponin release. Therefore, the aim of this study was to assess the effect of 4 week astaxanthin supplementation (a potent cartenoid antioxidant) on antioxidant capacity and exercise-induced cardiac troponin release in cyclists.Thirty-two well-trained male cyclists (age 25±5, weight 73±7 kg, maximum O2 uptake 60±5 mL·kg(-1)·min(-1), Wmax 5.4±0.5 W·kg(-1); mean ± SD) were repeatedly subjected to a laboratory based standardized exercise protocol before and after 4 weeks of astaxanthin (20 mg/day), or placebo supplementation in a double-blind randomized manner. Blood samples were obtained at baseline, at 60 min of cycling and immediately post-exercise (≈ 120 min).The pre-supplementation cycling trial induced a significant rise of median cardiac troponin T concentrations from 3.2 (IQR 3.0-4.2) to 4.7 ng/L (IQR 3.7-6.7), immediately post-exercise (p

Abstract 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. The study aimed to evaluate cardiac troponin I and T trajectories in patients with diabetes treated either with metformin or placebo. Methods This study is a post-hoc analysis of a randomized controlled trial (HOME trial) that included 390 patients with advanced type 2 diabetes randomized to 850 mg metformin or placebo up to three times daily concomitant to continued insulin treatment. Cardiac troponin I and T concentrations were measured at baseline and after 4, 17, 30, 43 and 52 months. We evaluated cardiac troponin trajectories by linear mixed-effects modeling, correcting for age, sex, smoking status and history of cardiovascular disease. Results This study enrolled 390 subjects, of which 196 received metformin and 194 received placebo. In the treatment and placebo groups, mean age was 64 and 59 years; with 50% and 58% of subjects of the female sex, respectively. Despite the previously reported reduction of macrovascular disease risk in this cohort by metformin, linear mixed-effects regression modelling did not reveal evidence for an effect on cardiac troponin I and cardiac troponin T levels [− 8.4% (− 18.6, 3.2), p = 0.150, and − 4.6% (− 12, 3.2), p = 0.242, respectively]. A statistically significant time-treatment interaction was found for troponin T [− 1.6% (− 2.9, − 0.2), p = 0.021] but not troponin I concentrations [− 1.5% (− 4.2, 1.2), p = 0.263]. Conclusions In this post-hoc analysis of a 4.3-year randomized controlled trial, metformin did not exert a clinically relevant effect on cardiac troponin I and cardiac troponin T levels when compared to placebo. Cardioprotective effects of the drug observed in clinical studies are not reflected by a reduction in these biomarkers of subclinical myocardial injury. Trial registration ClinicalTrials.gov identifier NCT00375388.

Background Cardiac troponin T (cTnT) is seen in many other conditions besides myocardial infarction, and recent studies demonstrated distinct forms of cTnT. At present, the in vivo formation of these different cTnT forms is incompletely understood. We therefore performed a study on the composition of cTnT during the course of myocardial infarction, including coronary venous system sampling, close to its site of release. Methods and Results Baseline samples were obtained from multiple coronary venous system locations, and a peripheral artery and vein in 71 non–ST‐segment–elevation myocardial infarction patients. Additionally, peripheral blood was drawn at 6‐ and 12‐hours postcatheterization. cTnT concentrations were measured using the high‐sensitivity‐cTnT immunoassay. The cTnT composition was determined via gel filtration chromatography and Western blotting in an early and late presenting patient. High‐sensitivity ‐cTnT concentrations were 28% higher in the coronary venous system than peripherally (n=71, P

Abstract We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model “X-DEC”. We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.

AbstractIntroduction 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. Primary outcome was 31-day mortality, secondary outcomes were 1-day mortality, 7-day mortality, and a composite endpoint of 31-day mortality and admission to intensive care unit (ICU).The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Finally, the prediction models with the highest performance to predict 31-day mortality were selected to further examine calibration and appropriate clinical cut-off points.Results We included 19 prediction models and applied these to 2185 ED patients. Thirty-one-day mortality was 10.6% (231 patients), 1-day mortality was 1.4%, 7-day mortality was 4.4%, and 331 patients (15.1%) met the composite endpoint. The RISE UP and COPE score showed similar and very good discriminatory performance for 31-day mortality (AUC 0.86), 1-day mortality (AUC 0.87), 7-day mortality (AUC 0.86) and for the composite endpoint (AUC 0.81). Both scores were well calibrated. Almost no patients with RISE UP and COPE scores below 5% had an adverse outcome, while those with scores above 20% were at high risk of adverse outcome. Some of the other prediction models (i.e. APACHE II, NEWS, WPSS, MEWS, EWS and SOFA) showed significantly higher discriminatory performance for 1-day and 7-day mortality than for 31-day mortality.Conclusions Head-to-head validation of 19 prediction models in medical ED patients showed that the RISE UP and COPE score outperformed other models regarding 31-day mortality.

ObjectiveCirculating biomarkers are useful in detection and monitoring of cardiovascular diseases. However, their role in aortic valve disease is unclear. Mechanisms are rapidly elucidated and sex differences are suggested to be involved. Therefore, we sought to identify biomarkers involved in aortic valve calcification (AVC) stratified by sex.MethodsBlood samples of 34 patients with AVC (without further overt cardiovascular disease, including absence of hemodynamic consequences of valvular calcification) were compared with 136 patients without AVC. AVC was determined using computed tomography calcium scoring. Circulating biomarkers were quantified using a novel antibody-based method (Olink Proseek Multiplex Cardiovascular Panel I) and 92 biomarkers were compared between patients with and without AVC.ResultsIn the overall population, Interleukin-1 Receptor Antagonist and pappalysin-1 were associated with increased and decreased odds of having AVC. These differences were driven by the male population [IL1RA: OR 2.79 (1.16–6.70), p = 0.022; PAPPA: OR 0.30 (0.11–0.84), p = 0.021]. Furthermore, TNF-related activation-induced cytokine (TRANCE) and fibroblast growth factor-23 were associated decreased odds of having AVC, and monocyte chemotactic protein-1 was associated with increased odds of having AVC [TRANCE: OR 0.32 (0.12–0.80), p = 0.015; FGF23: OR 0.41 (0.170–0.991), p = 0.048; MCP1: OR 2.64 (1.02–6.81), p = 0.045]. In contrast, galanin peptides and ST2 were associated with increased odds of having AVC in females [GAL: OR 12.38 (1.31–116.7), p = 0.028; ST2: OR13.64 (1.21–153.33), p = 0.034].ConclusionIn this exploratory study, we identified biomarkers involved in inflammation, fibrosis and calcification which may be associated with having AVC. Biomarkers involved in fibrosis may show higher expression in females, whilst biomarkers involved in inflammation and calcification could associate with AVC in males.

Abstract Interest in high-sensitivity cardiac troponin I(hs-cTnI) and T(hs-cTnT) has expanded from acute cardiac care to cardiovascular disease(CVD) risk stratification. Whether hs-cTnI and hs-cTnT are interchangeable in the ambulant setting is largely unexplored. Cardiac injury is a mechanism that may underlie the associations between troponin levels and mortality in the general population. In the population-based Maastricht Study, we assessed the correlation and concordance between hs-cTnI and hs-cTnT. Multiple regression analyses were conducted to assess the association of hs-cTnI and hs-cTnT with electrocardiographic (ECG) changes indicative of cardiac abnormalities. In 3016 eligible individuals(mean age,60 ± 8years;50.6%,men) we found a modest correlation between hs-cTnI and hs-cTnT(r = 0.585). After multiple adjustment, the association with ECG changes indicative of cardiac abnormalities was similar for both hs-cTn assays(OR,hs-cTnI:1.72,95%CI:1.40-2.10;OR,hs-cTnT:1.60,95%CI:1.22–2.11). The concordance of dichotomized hs-cTnI and hs-cTnT was κ = 0.397(≥sex-specific 75th percentile). Isolated high levels of hs-cTnI were associated with ECG changes indicative of cardiac abnormalities(OR:1.93,95%CI:1.01–3.68), whereas isolated high levels of hs-cTnT were not(OR:1.07,95%CI:0.49–2.31). In conclusion, there is a moderate correlation and limited concordance between hs-cTnI and hs-cTnT under non-acute conditions. These data suggest that associations of hs-cTnI and hs-cTnT with cardiac injury detected by ECG are driven by different mechanisms. This information may benefit future development of CVD risk stratification algorithms.

Abstract Background Vitamin K antagonists (VKAs) are associated with coronary artery calcification in low-risk populations, but their effect on calcification of large arteries remains uncertain. The effect of non–vitamin K antagonist oral anticoagulants (NOACs) on vascular calcification is unknown. We investigated the influence of use of VKA and NOAC on calcification of the aorta and aortic valve. Methods In patients with atrial fibrillation without a history of major adverse cardiac or cerebrovascular events who underwent computed tomographic angiography, the presence of ascending aorta calcification (AsAC), descending aorta calcification (DAC), and aortic valve calcification (AVC) was determined. Confounders for VKA/NOAC treatment were identified and propensity score adjusted logistic regression explored the association between treatment and calcification (Agatston score > 0). AsAC, DAC, and AVC differences were assessed in propensity score–matched groups. Results Of 236 patients (33% female, age: 58 ± 9 years), 71 (30%) used VKA (median duration: 122 weeks) and 79 (34%) used NOAC (median duration: 16 weeks). Propensity score–adjusted logistic regression revealed that use of VKA was significantly associated with AsAC (odds ratio [OR]: 2.31; 95% confidence interval [CI]: 1.16–4.59; p = 0.017) and DAC (OR: 2.38; 95% CI: 1.22–4.67; p = 0.012) and a trend in AVC (OR: 1.92; 95% CI: 0.98–3.80; p = 0.059) compared with non-anticoagulation. This association was absent in NOAC versus non-anticoagulant (AsAC OR: 0.51; 95% CI: 0.21–1.21; p = 0.127; DAC OR: 0.80; 95% CI: 0.36–1.76; p = 0.577; AVC OR: 0.62; 95% CI: 0.27–1.40; p = 0.248). A total of 178 patients were propensity score matched in three pairwise comparisons. Again, use of VKA was associated with DAC (p = 0.043) and a trend toward more AsAC (p = 0.059), while use of NOAC was not (AsAC p = 0.264; DAC p = 0.154; AVC p = 0.280). Conclusion This cross-sectional study shows that use of VKA seems to contribute to vascular calcification. The calcification effect was not observed in NOAC users.