Impact of Age on Antiplatelet Monotherapy in the Chronic Maintenance Period After Percutaneous Coronary Intervention: A Post Hoc Analysis From the HOST-EXAM Extended Study.

BACKGROUNDS: The HOST-EXAM Extended study reported the benefit of clopidogrel monotherapy over aspirin monotherapy in secondary prevention after percutaneous coronary intervention (PCI). This age-specific subgroup analysis of the study aimed to assess the impact of age on antiplatelet monotherapy after PCI. METHODS: We analysed data from the per-protocol population (4717 patients) with a median follow-up of 5.8 years. The old age group comprised 2033 patients (43.1%), defined as those 65 years of age or older. The primary end point was the composite of all-cause death, nonfatal myocardial infarction, stroke, readmission due to acute coronary syndrome (ACS), and Bleeding Academic Research Consortium (BARC) bleeding type 3 or greater. The secondary end points were thrombotic composite outcomes and any bleeding. RESULTS: Age correlated with an elevated risk of adverse events, particularly from age 65. Clopidogrel monotherapy was associated with a reduction of the primary end point in both the old age group (19.4% vs 23.1%, hazard ratio [HR] 0.802, 95% confidence interval [CI] 0.664-0.968; P = 0.022) and the young age group (7.8% vs 11.7%, HR 0.646, 95% CI 0.506-0.825; P < 0.001), without significant interaction (interaction P = 0.167). These findings were consistent for the secondary composite thrombotic end point and any bleeding events (interaction P value of secondary thrombotic end point: 0.786; interaction P value of any bleeding end point: 0.565). Consistent results were observed in analyses with a 75-year age cutoff and in subgroup analyses by 10-year age intervals. CONCLUSIONS: In patients requiring antiplatelet monotherapy after PCI, occurrence of both ischemic and bleeding events dramatically increased from age 65. The beneficial impact of clopidogrel over aspirin monotherapy was consistent regardless of age. CLINICAL TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT02044250.

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

A histomorphologic predictive model for axillary lymph node metastasis in preoperative breast cancer core needle biopsy according to intrinsic subtypes.

The aim of this study is construction of a pathologic nomogram that can predict axillary lymph node metastasis (LNM) for each intrinsic subtype of breast cancer with regard to histologic characteristics in breast core needle biopsy (CNB) for use in routine practice. A total of 534 CNBs with invasive ductal carcinoma classified into 5 intrinsic subtypes were enrolled. Eighteen clinicopathological characteristics and 8 molecular markers used in CNB were evaluated for construction of the best predictive model of LNM. In addition to conventional parameters including tumor multiplicity (P < .001), tumor size (P < .001), high histologic grade (P = .035), and lymphatic invasion (P = .017), micropapillary structure (P < .001), the presence of small cell-like crush artifact (P = .001), and overexpression of HER2 (P = .090) and p53 (P = .087) were proven to be independent predictive factors for LNM. A combination of 8 statistically independent parameters yielded the strongest predictive performance with an area under the curve of 0.760 for LNM. A combination of 6 independent variables, including tumor number, tumor size, histologic grade, lymphatic invasion, micropapillary structure, and small cell-like crush artifact produced the best predictive performance for LNM in luminal A intrinsic subtype (area under the curve, 0.791). Thus, adding these combinations of clinical and morphologic parameters in preoperative CNB is expected to enhance the accuracy of prediction of LNM in breast cancer, which might serve as another valuable tool in determining optimal surgical strategies for breast cancer patients.