Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm.

AIMS: Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF. METHODS AND RESULTS: This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex. CONCLUSION: An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.

DeltaMSI: artificial intelligence-based modeling of microsatellite instability scoring on next-generation sequencing data.

BACKGROUND: DNA mismatch repair deficiency (dMMR) testing is crucial for detection of microsatellite unstable (MSI) tumors. MSI is detected by aberrant indel length distributions of microsatellite markers, either by visual inspection of PCR-fragment length profiles or by automated bioinformatic scoring on next-generation sequencing (NGS) data. The former is time-consuming and low-throughput while the latter typically relies on simplified binary scoring of a single parameter of the indel distribution. The purpose of this study was to use machine learning to process the full complexity of indel distributions and integrate it into a robust script for screening of dMMR on small gene panel-based NGS data of clinical tumor samples without paired normal tissue. METHODS: Scikit-learn was used to train 7 models on normalized read depth data of 36 microsatellite loci in a cohort of 133 MMR proficient (pMMR) and 46 dMMR tumor samples, taking loss of MLH1/MSH2/PMS2/MSH6 protein expression as reference method. After selection of the optimal model and microsatellite panel the two top-performing models per locus (logistic regression and support vector machine) were integrated into a novel script (DeltaMSI) for combined prediction of MSI status on 28 marker loci at sample level. Diagnostic performance of DeltaMSI was compared to that of mSINGS, a widely used script for MSI detection on unpaired tumor samples. The robustness of DeltaMSI was evaluated on 1072 unselected, consecutive solid tumor samples in a real-world setting sequenced using capture chemistry, and 116 solid tumor samples sequenced by amplicon chemistry. Likelihood ratios were used to select result intervals with clinical validity. RESULTS: DeltaMSI achieved higher robustness at equal diagnostic power (AUC = 0.950; 95% CI 0.910-0.975) as compared to mSINGS (AUC = 0.876; 95% CI 0.823-0.918). Its sensitivity of 90% at 100% specificity indicated its clinical potential for high-throughput MSI screening in all tumor types. Clinical Trial Number/IRB B1172020000040, Ethical Committee, AZ Delta General Hospital.

Cycle Threshold Probability Score for Immediate and Sensitive Detection of B.1.351 SARS-CoV-2 Lineage.

BACKGROUND: Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern associated with immune escape is important to safeguard vaccination efficacy. We describe the potential of delayed N gene amplification in the Allplex SARS-CoV-2 Assay (Seegene) for screening of the B.1.351 (20H/501.V2, variant of concern 2 [VOC.V2], South African SARS-CoV-2 variant) lineage. METHODS: In a study cohort of 397 consecutive polymerase chain reaction-positive samples genotyped by whole-genome sequencing, amplification curves of E/N/S-RdRP targets indicated delayedN vs E gene amplification characteristic of B.1.351. Logistic regression was used to calculate a VOC.V2 probability score that was evaluated as a separate screening test in an independent validation cohort vs sequencing. RESULTS: B.1.351 showed a proportionally delayed amplification of the  N vs E gene. In logistic regression, only N and E gene cycle thresholds independently contributed to B.1.351 prediction, allowing calculation of a VOC.V2 probability score with an area under the curve of 0.94. At an optimal dichotomous cutoff point of 0.12, the VOC.V2 probability score achieved 98.7% sensitivity at 79.9% specificity, resulting in a negative predictive value (NPV) of 99.6% and a positive predictive value of 54.6%. The probability of B.1.351 increased with an increasing VOC.V2 probability score, achieving a likelihood ratio of 12.01 above 0.5. A near-maximal NPV was confirmed in 153 consecutive validation samples. CONCLUSIONS: Delayed N vs E gene amplification in the Allplex SARS-CoV-2 Assay can be used for fast and highly sensitive screening of B.1.351.

A Discussion on the Interpretation of the Darcy Equation in Case of Open-Cell Metal Foam Based on Numerical Simulations.

It is long known that for high-velocity fluid flow in porous media, the relation between the pressure drop and the superficial velocity is not linear. Indeed, the classical Darcy law for shear stress dominated flow needs to be extended with a quadratic term, resulting in the empirical Darcy-Forchheimer model. Another approach is to simulate the foam numerically through the volume averaging technique. This leads to a natural separation of the total drag force into the contribution of the shear forces and the contribution of the pressure forces. Both representations of the total drag lead to the same result. The physical correspondence between both approaches is investigated in this work. The contribution of the viscous and pressure forces on the total drag is investigated using direct numerical simulations. Special attention is paid to the dependency on the velocity of these forces. The separation of the drag into its constituent terms on experimental grounds and for the volume average approach is unified. It is shown that the common approach to identify the linear term with the viscous forces and the quadratic term with the pressure forces is not correct.

How to Study Thermal Applications of Open-Cell Metal Foam: Experiments and Computational Fluid Dynamics.

This paper reviews the available methods to study thermal applications with open-cell metal foam. Both experimental and numerical work are discussed. For experimental research, the focus of this review is on the repeatability of the results. This is a major concern, as most studies only report the dependence of thermal properties on porosity and a number of pores per linear inch (PPI-value). A different approach, which is studied in this paper, is to characterize the foam using micro tomography scans with small voxel sizes. The results of these scans are compared to correlations from the open literature. Large differences are observed. For the numerical work, the focus is on studies using computational fluid dynamics. A novel way of determining the closure terms is proposed in this work. This is done through a numerical foam model based on micro tomography scan data. With this foam model, the closure terms are determined numerically.

Effect of clopidogrel discontinuation at 1 year after drug eluting stent placement on soluble CD40L, P-selectin and C-reactive protein levels: DECADES (Discontinuation Effect of Clopidogrel After Drug Eluting Stent): a multicenter, open-label study.

Antiplatelet therapy with clopidogrel has been shown to reduce major adverse cardiac events in acute coronary syndromes and after percutaneous interventions. This effect is not only due to its anti-platelet effect but also possibly due to an anti-inflammatory effect. The effect of clopidogrel cessation after one year of therapy on markers of inflammation has been investigated in diabetics and showed an increase in platelet aggregation as well as hsCRP and surface P-selectin levels. This was an exploratory multicenter prospective open-label single arm study of 98 non-diabetic patients who had received one or more drug eluting stents and were coming to the end of their 12 months course of clopidogrel therapy. The effect of clopidogrel cessation on expression of biomarkers: sCD40L, soluble P-selectin and hsCRP was measured right before clopidogrel cessation (day 0), and subsequently at 1, 2, 3 and 4 weeks after drug withdrawal. A median increase in sCD40L expression from 224 to 324.5 pg/ml was observed between baseline and 4 weeks after clopidogrel cessation, which corresponded to a 39% mean percent change based on an ANCOVA model (P < 0.001). Over the 4 weeks observation period the change in sCD40L expression correlated weakly with soluble P-selectin levels (at 4 weeks Spearman's correlation coefficient = 0.32; P = 0.0024). Increase in P-selectin expression from baseline was statistically significant at week 1 and 2. Conversely, hsCRP level decreased by 21% at 1 week (P = 0.008) and was still reduced by 18% by 4 weeks (P = 0.062). The change in sCD40L expression appeared to vary with the type of drug eluting stent. Patients treated with drug eluting stents at 1 year after implantation display significant increase in sCD40L and decrease in hsCRP after clopidogrel cessation. Further studies should elucidate if this increase in sCD40L levels reflects solely the removal of the inhibitory effects of clopidogrel on platelet activity or rather an increase in pro-inflammatory state. The latter hypothesis may be less likely given decrease in hsCRP levels. Randomized studies are urgently needed to establish potential link of clopidogrel discontinuation and vascular outcomes.