Wide-Angle Coronary Bifurcation Stenotic Lesions Treated With One Drug-Eluting Stent and Sequential Balloon Technique: A Better Strategy?

BACKGROUND: Clinically significant bifurcation lesions account for up to 20% of percutaneous coronary intervention (PCI) procedures, and present technical challenges due to the potential for occlusion of the side branch vessel. Percutaneous coronary intervention using final kissing ballooning (FKB) plays a major role in treating bifurcation lesions, but sequential dilatation (SD) is a less complicated PCI technique with a shallower learning curve. Previous studies have shown no benefit of FKB over SD, but wide-angle (>70°) bifurcation lesions may respond differently to narrow-angle bifurcation lesions. METHODS: Retrospective analysis was carried out to compare outcomes of FKB and SD stenting specifically for wide-angle bifurcation lesions: 7,582 PCIs performed at a single medical centre between 1 January 2009 and 31 May 2016 were screened. This yielded 112 SD and 102 FKB cases for comparative analysis, which was conducted with respect to major adverse cardiac event (MACE)-free survival and target lesion revascularisation (TLR)-free survival rates. RESULTS: The comparative analysis was achieved using the log-rank test and presented as Kaplan-Meier curves. All baseline characteristics were balanced among the groups. The mean procedure and fluoroscopy times were significantly longer for patients with FKB than SD. Patients with SD had slightly better MACE and TLR rates than those with FKB in both the drug-eluting stent (DES) and bare metal stent (BMS) groups. In addition, patients with DES had slightly lower MACE and TLR rates than those with BMS in both the FKD and SD groups. Major adverse cardiac event-free survival and TLR-free survival rates were also slightly higher in patients with DES than those with BMS in both the FKD and SD groups. However, these differences were not statistically significant. CONCLUSIONS: These results suggest that the most applicable procedure for PCI of wide-angulated bifurcation stenosis would be a combination of DES and SD.

Platelet MicroRNA 365-3p Expression Correlates with High On-treatment Platelet Reactivity in Coronary Artery Disease Patients.

PURPOSE: The expression level of platelet microRNAs (miRNAs) correlates with heart disease and may be altered by antiplatelet therapy. This study aims to assess whether certain miRNAs are associated with treatment response by platelets in patients who received percutaneous coronary intervention and antiplatelet therapy. The dynamic expression of certain miRNAs in patients receiving different antiplatelet regimens was also investigated. METHODS: Healthy subjects (N = 20) received no-stent or antiplatelet therapy (as control), and patients (N = 155) who underwent stent implant and received treatment regimens that included aspirin plus clopidogrel, ticagrelor, or cilostazol were included. The association of miR-96-5p, miR-495-3p, miR-107, miR-223-3p, miR-15a-5, miR-365-3p, and miR-339-3p levels with treatment response, SYNTAX score, and HTPR was determined. RESULTS: Of the different treatment regimens, ticagrelor was the most efficacious. At 24 h following drug administration, ROC analysis revealed that miR-339-3p and miR-365-3p had the highest sensitivity (74.3% and 90.0%, respectively) and specificity (71.4% and 93.3%) for detecting HTPR compared with the five other miRNAs. The SYNTAX score positively correlated with miR-223-3p and miR-365-3p levels at 24 h (P ≤ 0.006) and with miR-365-3p levels 7 days following drug administration (P = 0.014). The expression of all three miRNAs reached the highest levels in hyperresponsive (P2Y12 reaction unit < 85) followed by hyporesponsive (P2Y12 reaction unit ≥ 208) and then normoreactive. The normoreactive value was very close to that of controls. CONCLUSIONS: Our data suggest that miR-365-3p expression level correlates with the antiplatelet treatment response. CLINICAL TRIAL REGISTRATION: NCT02101437.

DAPT Plus Cilostazol is Better Than Traditional DAPT or Aspirin Plus Ticagrelor as Elective PCI for Intermediate-to-Highly Complex Cases: Prospective, Randomized, PRU-Based Study in Taiwan.

PURPOSE: Current treatment guidelines do not recommend different antiplatelet treatments for patients in different coronary risk categories; nor do they consider ethnic differences in responses to individual drugs. OBJECTIVES: We performed a prospective, single-blind, randomized, comparative study of Taiwanese patients with stable angina and scheduled stent implantation for intermediate-to-highly complex coronary lesions and compared the platelet reactivity unit (PRU) levels and 24-month outcomes of groups receiving three different antiplatelet treatments. METHODS: Patients (N = 334) were randomized into three treatment groups (aspirin + clopidogrel, aspirin + ticagrelor, or aspirin + clopidogrel + cilostazol) for 6 months of treatment and were then switched to aspirin only. PRU levels were determined 24 h, 7 days, and 1 month after stent implantation. Clinical outcomes and adverse events were recorded over 24 months. RESULTS: Clopidogrel treatment reached full effect after 1 month. Ticagrelor decreased PRU levels more than did clopidogrel but often to levels that increased the risk of hemorrhage. The addition of cilostazol to clopidogrel decreased PRU levels earlier and more strongly than clopidogrel alone but not as strongly as did ticagrelor. Ticagrelor treatment caused fewer major adverse cardiovascular events (MACEs) and more episodes of minor bleeding than the other two treatments. CONCLUSIONS: Clopidogrel appears safer than ticagrelor in Taiwanese patients with stable angina after stent implantation for intermediate-to-highly complex coronary lesions. The addition of cilostazol to clopidogrel may provide a more rapid decrease in PRU to therapeutic levels without increasing the risk of hemorrhage. CLINICAL TRIAL REGISTRATION NUMBER: NCT02101411.

Relation between fetuin-A levels and fibroblast growth factor 23 with the severity of coronary artery disease measured by SYNTAX scores.

Diagnosis of coronary artery disease requires invasive procedures that are typically not implemented until clinical warning signs are apparent. The goal of this study was to determine the relation between the severity of coronary artery disease, as measured by the SYNTAX scoring system, with serum levels of fetuin-A and fibroblast growth factor 23 (FGF23) in the general population. We enrolled 165 patients who had stable angina and positive results on treadmill testing or abnormal results on thallium myocardial perfusion scanning showing perfusion defects or who had acute coronary syndromes. Patients were hospitalized for evaluation with angiography, with or without simultaneous percutaneous coronary intervention. SYNTAX Scores were calculated on the basis of the results of coronary angiography using a computer-based questionnaire of sequential and interactive self-guided questions. Univariate analysis was used to assess the significance of fetuin-A and FGF23, as well as gender, age, body mass index, waist circumference, diabetes, hypertension, creatinine, total cholesterol, cholesterol, triglycerides, and high-sensitivity C-reactive protein in relation to cardiovascular disease severity. Multivariate analysis with stepwise regression was used to assess the utility of fetuin-A and FGF23 as predictors of SYNTAX Score. Multivariate analysis showed log fetuin-A to be a significant predictor of SYNTAX Score (p <0.0001) after controlling for the significant factors gender, cholesterol levels, and log high-sensitivity C-reactive protein. Log FGF23 values were also shown by multivariate regression to significantly predict SYNTAX Score (p = 0.0137) after controlling for gender, creatinine, cholesterol, and log high-sensitivity C-reactive protein. In conclusion, fetuin-A and FGF23 can be considered in combination with noninvasive test results as patient selection criteria for performing angiography.

Case-based reasoning support for liver disease diagnosis.

OBJECTIVES: In Taiwan, as well as in the other countries around the world, liver disease has reigned over the list of leading causes of mortality, and its resistance to early detection renders the disease even more threatening. It is therefore crucial to develop an auxiliary system for diagnosing liver disease so as to enhance the efficiency of medical diagnosis and to expedite the delivery of proper medical treatment. METHODS: The study accordingly integrated the case-based reasoning (CBR) model into several common classification methods of data mining techniques, including back-propagation neural network (BPN), classification and regression tree, logistic regression, and discriminatory analysis, in an attempt to develop a more efficient model for early diagnosis of liver disease and to enhance classification accuracy. To minimize possible bias, this study used a ten-fold cross-validation to select a best model for more precise diagnosis results and to reduce problems caused by false diagnosis. RESULTS: Through a comparison of five single models, BPN and CBR emerged to be the top two methods in terms of overall performance. For enhancing diagnosis performance, CBR was integrated with other methods, and the results indicated that the accuracy and sensitivity of each CBR-added hybrid model were higher than those of each single model. Of all the CBR-added hybrid models, the BPN-CBR method took the lead in terms of diagnosis capacity with an accuracy rate of 95%, a sensitivity of 98%, and a specificity of 94%. CONCLUSIONS: After comparing the five single and hybrid models, the study found BPN-CBR the best model capable of helping physicians to determine the existence of liver disease, achieve an accurate diagnosis, diminish the possibility of a false diagnosis being given to sick people, and avoid the delay of clinical treatment.

An efficiency data envelopment analysis model reinforced by classification and regression tree for hospital performance evaluation.

As changes in the medical environment and policies on national health insurance coverage have triggered tremendous impacts on the business performance and financial management of medical institutions, effective management becomes increasingly crucial for hospitals to enhance competitiveness and to strive for sustainable development. The study accordingly aims at evaluating hospital operational efficiency for better resource allocation and cost effectiveness. Several data envelopment analysis (DEA)-based models were first compared, and the DEA-artificial neural network (ANN) model was identified as more capable than the DEA and DEA-assurance region (AR) models of measuring operational efficiency and recognizing the best-performing hospital. The classification and regression tree (CART) efficiency model was then utilized to extract rules for improving resource allocation of medical institutions.

A hybrid diagnosis model for determining the types of the liver disease.

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.