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BackgroundWith the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. ObjectiveOur study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. MethodsFor this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. ResultsOf the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (

To investigate the prognostic value of fibrinogen-to-albumin ratio (FAR) in the adverse outcomes of patients with coronary three-vessel disease (TVD).Methods: A total of 4061 patients with TVD between 2013 and 2018 were analyzed in this retrospective cohort study. The best cut‑off value of the FAR determined by receiver operating characteristic (ROC) curve analysis was 0.084. 2782 (68.5%) patients were in the low FAR group (FAR <  0.084) and 1279 (31.5%) patients were in the high FAR group (FAR ≥  0.084), respectively. Three multivariate Cox proportional hazards models were applied to determine the associations of FAR with clinical outcomes. The concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to assess the incremental predictive value of the FAR and baseline models with respect to the additive effects of the established traditional risk factors on the discrimination of clinical outcomes. The primary endpoint was all-cause mortality. The secondary endpoint was major adverse cardiac and cerebrovascular events (MACCEs).Results: The median follow-up duration was 2.4 years (range 1.1– 4.1 years). Multivariate Cox regression analyses showed that the incidence of all-cause mortality (4.7% vs 2.2%, adjusted hazard ratio [HR] 1.68, 95% confidence interval [CI] 1.12– 2.52, p=0.011) and MACCE (34.6% vs 27.3%, HR 1.28, 95% CI 1.13– 1.46, p< 0.001) were significantly higher in the high FAR group compared to the low FAR group. The C-index was 0.72 (p < 0.001), the value of NRI was 0.3778 (p < 0.001), and the value of IDI was 0.0098 (p < 0.001) for those with FAR. After FAR was added to the traditional model, the discrimination and risk reclassification ability can be significantly improved for all-cause mortality. The similar results were found for MACCE.Conclusion: Higher level of FAR was associated with all-cause mortality and MACCE among patients with TVD. FAR could help to improve the prognostic performance of the traditional risk factors for TVD patients. [ABSTRACT FROM AUTHOR]

Background: With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. Objective: Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. Methods: For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. Results: Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×109/L). Conclusions: ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making. J Med Internet Res 2021;23(4):e24996 doi:10.2196/24996 [ABSTRACT FROM AUTHOR]

The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f1 in torque and (1 ± 2s)f1 in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity. [ABSTRACT FROM AUTHOR]

In clinical studies, platelet aggregation and risk of thrombosis are increased in patients after doxorubicin treatment. However, the exact role of doxorubicin in platelet functions and thrombus formation in vivo remain unclear. The present study is to investigate the role of doxorubicin in platelet function in relation to thrombus formation and vascular toxicity, as well as the efficacy of antiplatelet therapy. Mice were treated with doxorubicin or vehicle (5 mg/kg iv, 4 wk), and the following parameters were determined: platelet count and size, platelet surface adhesive receptors by flow cytometry, density of granules by electron microscopy, platelet aggregation and degranulation at resting or agonist-stimulated state, platelet adhesion on fibrinogen or endothelial cells, and thrombus formation on collagen matrix. The efficacy of clopidogrel (15 mg·kg -1 ·day -1 , followed by 5 mg·kg -1 ·day -1 ) on doxorubicin-induced changes in the aforementioned parameters as well as vascular injury were also determined. Whereas platelet count and size were similar between doxorubicin-treated and vehicle-treated mice, doxorubicin promoted thrombus formation evidenced by greater platelet aggregation, degranulation, and adhesion to endothelial cells evoked by agonists. Clopidogrel treatment attenuated the enhanced platelet activity and thrombus formation by doxorubicin, as well as vascular platelet infiltration and reactive oxygen species generation. Collectively, this study demonstrates that platelet functions are enhanced after long-term doxorubicin administration, which leads to thrombus formation and vascular toxicity, and that doxorubicin-induced changes in the functionality of platelets can be effectively inhibited by antiplatelet drugs. NEW & NOTEWORTHY Doxorubicin therapy in mice (antitumor dosage) markedly enhanced platelet functions measured as agonist-induced platelet aggregation, degranulation, and adhesion to endothelial cells, actions leading to thrombus formation and thrombosis-independent vascular injury. Clopidogrel treatment ameliorated thrombus formation and vascular toxicity induced by doxorubicin via inhibiting platelet activity.