Outcomes in patients with acute and stable coronary syndromes: insights from the prospective NOBORI-2 study

BACKGROUND: Contemporary data remains limited regarding mortality and major adverse cardiac events (MACE) outcomes in patients undergoing PCI for different manifestations of coronary artery disease. OBJECTIVES: We evaluated mortality and MACE outcomes in patients treated with PCI for STEMI (ST-elevation myocardial infarction), NSTEMI (non ST-elevation myocardial infarction) and stable angina through analysis of data derived from the Nobori-2 study. METHODS: Clinical endpoints were cardiac mortality and MACE (a composite of cardiac death, myocardial infarction and target vessel revascularization). RESULTS: 1909 patients who underwent PCI were studied; 1332 with stable angina, 248 with STEMI and 329 with NSTEMI. Age-adjusted Charlson co-morbidity index was greatest in the NSTEMI cohort (3.78±1.91) and lowest in the stable angina cohort (3.00±1.69); P<0.0001. Following Cox multivariate analysis cardiac mortality was independently worse in the NSTEMI vs the stable angina cohort (HR 2.31 (1.10-4.87), p = 0.028) but not significantly different for STEMI vs stable angina cohort (HR 0.72 (0.16-3.19), p = 0.67). Similar observations were recorded for MACE (<180 days) (NSTEMI vs stable angina: HR 2.34 (1.21-4.55), p = 0.012; STEMI vs stable angina: HR 2.19 (0.97-4.98), p = 0.061. CONCLUSIONS: The longer-term Cardiac mortality and MACE were significantly worse for patients following PCI for NSTEMI even after adjustment of clinical demographics and Charlson co-morbidity index whilst the longer-term prognosis of patients following PCI STEMI was favorable, with similar outcomes as those patients with stable angina following PCI

Coronary optical frequency domain imaging (OFDI) for in vivo evaluation of stent healing: comparison with light and electron microscopy

Aims Coronary late stent thrombosis, a rare but devastating complication, remains an important concern in particular with the increasing use of drug-eluting stents. Notably, pathological studies have indicated that the proportion of uncovered coronary stent struts represents the best morphometric predictor of late stent thrombosis. Intracoronary optical frequency domain imaging (OFDI), a novel second-generation optical coherence tomography (OCT)-derived imaging method, may allow rapid imaging for the detection of coronary stent strut coverage with a markedly higher precision when compared with intravascular ultrasound, due to a microscopic resolution (axial ∼10-20 µm), and at a substantially increased speed of image acquisition when compared with first-generation time-domain OCT. However, a histological validation of coronary OFDI for the evaluation of stent strut coverage in vivo is urgently needed. Hence, the present study was designed to evaluate the capacity of coronary OFDI by electron (SEM) and light microscopy (LM) analysis to detect and evaluate stent strut coverage in a porcine model. Methods and results Twenty stents were implanted into 10 pigs and coronary OFDI was performed after 1, 3, 10, 14, and 28 days. Neointimal thickness as detected by OFDI correlated closely with neointimal thickness as measured by LM (r = 0.90, P < 0.01). The comparison of stent strut coverage as detected by OFDI and SEM analysis revealed an excellent agreement (r = 0.96, P < 0.01). In particular, stents completely covered by OFDI analysis were also completely covered by SEM analysis. All incompletely covered stents by OFDI were also incompletely covered by SEM. Analyses of fibrin-covered stent struts suggested that these may rarely be detected as uncovered stent struts by OFDI. Importantly, optical density measurements revealed a significant difference between fibrin- and neointima-covered coronary stent struts [0.395 (0.35-0.43) vs. 0.53 (0.47-0.57); P < 0.001], suggesting that differences in optical density provide information on the type of stent strut coverage. The sensitivity and specificity for detection of fibrin vs. neointimal coverage was evaluated using receiver-operating characteristic analysis. Conclusion The present study demonstrates that OFDI is a highly promising tool for accurate evaluation of coronary stent strut coverage, as supported by a high agreement between OFDI and light and electron microscopic analysis. Furthermore, our data indicate that optical density measurements can provide additional information with respect to the type of stent strut coverage, i.e. fibrin vs. neointimal coverage. Therefore, coronary OFDI analysis will provide important information on the biocompatibility of coronary stent

Coronary optical frequency domain imaging (OFDI) for in vivo evaluation of stent healing: comparison with light and electron microscopy

The present study demonstrates that OFDI is a highly promising tool for accurate evaluation of coronary stent strut coverage, as supported by a high agreement between OFDI and light and electron microscopic analysis. Furthermore, our data indicate that optical density measurements can provide additional information with respect to the type of stent strut coverage, i.e. fibrin vs. neointimal coverage. Therefore, coronary OFDI analysis will provide important information on the biocompatibility of coronary stents

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

AIMS: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.METHODS AND RESULTS: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.CONCLUSION: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.REGISTRATION: Clinicaltrial.gov identifier is NCT02188355.</p

Predicting Target Lesion Failure following Percutaneous Coronary Intervention through Machine Learning Risk Assessment Models.

AimsCentral to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.Methods and resultsFive different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69–0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.ConclusionMachine learning–derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.RegistrationClinicaltrial.gov identifier is NCT02188355

Lesion data (data presented per lesion.

<p>Lesion data (data presented per lesion.</p

Clinical outcomes.

<p>Clinical outcomes.</p

Kaplan- Meier curves for MACE.

<p>Kaplan- Meier curves for MACE.</p

Procedural Demographics.

<p>Procedural Demographics.</p