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Background: Age-related remodelling has the potential to affect the microvascular response to hyperemic stimuli. However, its precise effects on the vasodilatory response to adenosine and contrast medium, as well as its influence on fractional flow reserve (FFR) and contrast fractional flow reserve (cFFR), have not been previously investigated. We investigate the impact of age on these indices. Methods: We extrapolated data from the post-revascularization optimization and physiological evaluation of intermediate lesions using fractional flow reserve (PROPHET-FFR) and The Multi-center Evaluation of the Accuracy of the Contrast MEdium INduced Pd/Pa RaTiO in Predicting (MEMENTO) studies. Only lesions with a relevant vasodilatory response to adenosine and contrast medium were considered of interest. A total of 2080 patients, accounting for 2294 pressure recordings were available for analysis. The cohort was stratified into three age terciles. Age-dependent correlations with FFR, cFFR, distal pressure/aortic pressure (Pd/Pa) and instantaneous wave-free ratio (iFR) were calculated. The vasodilatory response was calculated in 1619 lesions (with both FFR and cFFR) as the difference between resting and hyperaemic pressure ratios and correlated with aging. The prevalence of FFR-cFFR discordance was assessed. Results: Age correlated positively to FFR (r = 0.062, p = 0.006), but not with cFFR (r = 0.024, p = 0.298), Pd/Pa (r = -0.015, p = 0.481) and iFR (r = -0.026, p = 0.648). The hyperemic response to adenosine (r = -0.102, p <= 0.0001) and to contrast medium (r = -0.076, p = 0.0023) showed a negative correlation with age. When adjusted for potential confounders, adenosine induced hyperaemia was negatively associated with age (p = 0.04 vs p = 0.08 for cFFR). Discordance decreased across age terciles (14.64% vs 12.72% vs 10.12%, p = 0.032). Conclusions: As compared to adenosine, contrast induced hyperaemia appeared to be less affected by age. cFFR may be considered a more s

Background Despite evidence supporting use of fractional flow reserve (FFR) and instantaneous waves-free ratio (iFR) to improve outcome of patients undergoing coronary angiography (CA) and percutaneous coronary intervention, such techniques are still underused in clinical practice due to economic and logistic issues.Objectives We aimed to develop an artificial intelligence (AI)-based application to compute FFR and iFR from plain CA.Methods and results Consecutive patients performing FFR or iFR or both were enrolled. A specific multi-task deep network exploiting 2 projections of the coronary of interest from standard CA was appraised. Accuracy of prediction of FFR/iFR of the AI model was the primary endpoint, along with sensitivity and specificity. Prediction was tested both for continuous values and for dichotomous classification (positive/negative) for FFR or iFR. Subgroup analyses were performed for FFR and iFR. A total of 389 patients from 5 centers were enrolled. Mean age was 67.9 +/- 9.6 and 39.2% of patients were admitted for acute coronary syndrome. Overall, the accuracy was 87.3% (81.2-93.4%), with a sensitivity of 82.4% (71.9-96.4%) and a specificity of 92.2% (90.4-93.9%). For FFR, accuracy was 84.8% (77.8-91.8%), with a sensitivity of 81.9% (69.4-94.4%) and a specificity of 87.7% (85.5-89.9%), while for iFR accuracy was 90.2% (86.0-94.6%), with a sensitivity of 87.2% (76.6-97.8%) and a specificity of 93.2% (91.7-94.7%, all confidence intervals 95%).Methods and results Consecutive patients performing FFR or iFR or both were enrolled. A specific multi-task deep network exploiting 2 projections of the coronary of interest from standard CA was appraised. Accuracy of prediction of FFR/iFR of the AI model was the primary endpoint, along with sensitivity and specificity. Prediction was tested both for continuous values and for dichotomous classification (positive/negative) for FFR or iFR. Subgroup analyses were performed for FFR and iFR. A total of 389 patients f

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Background: Diffuse coronary artery disease (CAD) impacts the safety and efficacy of percutaneous coronary intervention (PCI). Pathophysiological CAD patterns can be quantified using fractional flow reserve (FFR) pullbacks incorporating the pullback pressure gradient (PPG) calculation. This study aimed to establish the capacity of PPG to predict optimal revascularisation and procedural outcomes. Methods: This prospective, investigator-initiated, single-arm, multicentre study enrolled patients with at least one epicardial lesion with an FFR ≤ 0.80 scheduled for PCI. Manual FFR pullbacks were employed to calculate PPG. The primary outcome of optimal revascularisation was defined as a post-PCI FFR ≥ 0.88. Results: 993 patients with 1044 vessels were included. The mean FFR was 0.68 ± 0.12, PPG 0.62 ± 0.17, and post-PCI FFR 0.87 ± 0.07. PPG was significantly correlated with the change in FFR after PCI (r=0.65, 95% CI 0.61-0.69, p<0.001) and demonstrated excellent predicted capacity for optimal revascularisation (AUC 0.82, 95% CI 0.79-0.84, p<0.001). Conversely, FFR alone did not predict revascularisation outcomes (AUC 0.54, 95% CI 0.50-0.57). PPG influenced treatment decisions in 14% of patients, redirecting them from PCI to alternative treatment modalities. Periprocedural myocardial infarction occurred more frequently in patients with low PPG (<0.62) compared to those with focal disease (OR 1.71, 95% CI: 1.00-2.97). Conclusions: Pathophysiological CAD patterns distinctly affect the safety and effectiveness of PCI. The PPG showed an excellent predictive capacity for optimal revascularisation and demonstrated added value compared to a FFR measurement.

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BackgroundFractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to carefully characterize coronary plaque morphology and lumen dimensions.ObjectivesWe sought to develop machine learning (ML) models based on clinical, angiographic and OCT variables for predicting FFR.MethodsData from a multicenter, international, pooled analysis of individual patient's level data from published studies assessing FFR and OCT on the same target AICL were collected through a dedicated database to train (n = 351) and validate (n = 151) six two-class supervised ML models employing 25 clinical, angiographic and OCT variables.ResultsA total of 502 coronary lesions in 489 patients were included. The AUC of the six ML models ranged from 0.71 to 0.78, whereas the measured F1 score was from 0.70 to 0.75. The ML algorithms showed moderate sensitivity (range: 0.68-0.77) and specificity (range: 0.59-0.69) in detecting patients with a positive or negative FFR. In the sensitivity analysis, using 0.75 as FFR cut-off, we found a higher AUC (0.78-0.86) and a similar F1 score (range: 0.63-0.76). Specifically, the six ML models showed a higher specificity (0.71-0.84), with a similar sensitivity (0.58-0.80) with respect to 0.80 cut-off.ConclusionsML algorithms derived from clinical, angiographic, and OCT parameters can identify patients with a positive or negative FFR.

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Background and aims: Acetylcholine (ACh) provocation testing can detect vasomotor disorders in patients with ischemia and non-obstructed coronary arteries (INOCA) or myocardial infarction and non-obstructed coronary arteries (MINOCA). We aimed to derive and validate a simple risk score to predict a positive ACh test response. Methods: We prospectively enrolled consecutive INOCA and MINOCA patients undergoing ACh provocation testing. Patients were split in two cohorts (derivation and validation) according to time of enrolment. The score was derived in 386 patients (derivation cohort) and then validated in 165 patients (validation cohort). Results: 551 patients were enrolled, 371 (67.3%) INOCA and 180 (32.7%) MINOCA. ACh test was positive in 288 (52.3%) patients. MINOCA, myocardial bridge (MB), C-reactive protein (CRP) and dyslipidaemia were independent predictors of a positive ACh test in the derivation cohort. The ABCD (Acute presentation, Bridge, CRP, Dyslipidaemia) score was derived: 2 points were assigned to MINOCA, 3 to MB, 1 to elevated CRP and 1 to dyslipidaemia. The ABCD score accurately identified patients with a positive ACh test response with an AUC of 0.703 (CI 95% 0.652-0.754,p < 0.001) in the derivation cohort, and 0.705 (CI 95% 0.626-0.784, p < 0.001) in the validation cohort. In the whole population, an ABCD score ≥4 portended 94.3% risk of a positive ACh test and all patients with an ABCD score ≥6 presented a positive test. Conclusions: The ABCD score could avoid the need of ACh provocation testing in patients with a high score, reducing procedural risks, time, and costs, and allowing the implementation of a tailored treatment strategy. These results are hypothesis generating and further research involving larger cohorts and multicentre trials is needed to validate and refine the ABCD score.

Myocardial infarction with non-obstructive coronary artery disease (MINOCA) is a heterogeneous clinical condition affecting 5% to 8% of patients presenting with acute myocardial infarction. Initially it was considered a favorable clinical diagnosis, nowadays it is known that MINOCA can significantly affect patient quality of life and portends a guarded prognosis. Therefore, it is of utmost importance to identify the specific pathophysiological mechanism underlying this clinical condition in order to set up a targeted pharmacological and non-pharmacological therapy. Coronary angiography is still a mandatory diagnostic test to rule out obstructive coronary artery disease but has limited capability to identify other potential functional and structural etiologies of MINOCA. The purpose of this review is to provide an overview of the invasive diagnostic work-up of patients with MINOCA, highlighting the diagnostic tools warranted beyond coronary angiography inside the cath lab (intracoronary provocation tests, intracoronary imaging and indexes for the assessment of coronary microvascular dysfunction), and the remaining essential knowledge gaps in this field.

Background Infective endocarditis (IE) could be suspected in any febrile patients admitted to the emergency department (ED). This study was aimed at assessing clinical criteria predictive of IE and identifying and prospectively validating a sensible and easy-to-use clinical prediction score for the diagnosis of IE in the ED. Methods and Results We conducted a retrospective observational study, enrolling consecutive patients with fever admitted to the ED between January 2015 and December 2019 and subsequently hospitalized. Several clinical and anamnestic standardized variables were collected and evaluated for the association with IE diagnosis. We derived a multivariate prediction model by logistic regression analysis. The identified predictors were assigned a score point value to obtain the Clinical Rule for Infective Endocarditis in the Emergency Department (CREED) score. To validate the CREED score we conducted a prospective observational study between January 2020 and December 2021, enrolling consecutive febrile patients hospitalized after the ED visit, and evaluating the association between the CREED score values and the IE diagnosis. A total of 15 689 patients (median age, 71 [56-81] years; 54.1% men) were enrolled in the retrospective cohort, and IE was diagnosed in 267 (1.7%). The CREED score included 12 variables: male sex, anemia, dialysis, pacemaker, recent hospitalization, recent stroke, chest pain, specific infective diagnosis, valvular heart disease, valvular prosthesis, previous endocarditis, and clinical signs of suspect endocarditis. The CREED score identified 4 risk groups for IE diagnosis, with an area under the receiver operating characteristic curve of 0.874 (0.849-0.899). The prospective cohort included 13 163 patients, with 130 (1.0%) IE diagnoses. The CREED score had an area under the receiver operating characteristic curve of 0.881 (0.848-0.913) in the validation cohort, not significantly different from the one calculated in the retrospectiv

Aims: Adenosine has been tested in several randomized controlled trials (RCTs) to minimize the incidence of coronary microvascular obstruction (CMVO). The aim of this study was to pool all the RCTs comparing intracoronary or intravenous adenosine versus placebo in patients with acute coronary syndrome (ACS) undergoing myocardial revascularization. Methods and results: PubMed and Scopus electronic databases were scanned for eligible studies up to 5th June 2022. A total of 26 RCTs with 5843 patients were included. Efficacy endpoints were major adverse cardiac events (MACE), all-cause death, non-fatal myocardial infarction, and heart failure. Atrioventricular blocks and ventricular fibrillation/sustained ventricular tachycardia (VF/SVT) were the safety endpoints. Myocardial blush grade, thrombolysis in myocardial infarction (TIMI) flow grade, left ventricular ejection fraction (LVEF), infarct size, and ST-segment resolution were also assessed. Adenosine administration was not associated with any clinical benefit in terms of MACE, all-cause death, non-fatal myocardial infarction, and heart failure. However, adenosine was associated with an increased rate of advanced atrioventricular blocks and of VF/SVT in studies with total mean ischaemic time >3 h, compared to placebo. Remarkably, among patients undergoing percutaneous coronary intervention, adenosine was associated with reduced myocardial blush grade 0-1 and TIMI flow grade 0-2, compared to placebo. Furthermore, adenosine did not show favourable effects on LVEF and infarct size. Conclusion: Adenosine infusion, as adjunctive therapy in ACS, was associated with an increased risk of advanced atrioventricular blocks and increased rates of adenosine-triggered ventricular arrhythmias in patients with long ischaemic time, without providing any clinical benefit compared to placebo.

BackgroundThe combination of coronary imaging assessment and blood flow perturbation estimation has the potential to improve percutaneous coronary intervention (PCI) guidance. ObjectivesWe aimed to evaluate a novel method for fast computation of Murray law-based quantitative flow ratio (mu QFR) from coregistered optical coherence tomography (OCT) and angiography (OCT-modulated mu QFR, OCT-mu QFR) in predicting physiological efficacy of PCI. MethodsPatients treated by OCT-guided PCI in the OCT-arm of the Fractional Flow Reserve versus Optical Coherence Tomography to Guide RevasculariZAtion of Intermediate Coronary Stenoses trial (FORZA, NCT01824030) were included. Based on angiography and OCT before PCI, simulated residual OCT-mu QFR was computed by assuming full stent expansion to the intended-to-treat segment. Plaque composition was automatically characterized using a validated artificial intelligence algorithm. Actual post-PCI OCT-mu QFR pullback was computed based on coregistration of angiography and OCT acquired immediately after PCI. Suboptimal functional stenting result was defined as OCT-mu QFR <= 0.90. ResultsPaired simulated residual OCT-mu QFR and actual post-PCI OCT-mu QFR were obtained in 76 vessels from 74 patients. Simulated residual OCT-mu QFR showed good correlation (r = 0.80, p < 0.001), agreement (mean difference = -0.02 +/- 0.02, p < 0.001), and diagnostic concordance (79%, 95% confidence interval: 70%-88%) with actual post-PCI OCT-mu QFR. Actual post-PCI in-stent OCT-mu QFR had a median value of 0.02 and was associated with left anterior descending artery lesion location (beta = 0.38, p < 0.001), higher baseline total plaque burden (beta = 0.25, p = 0.031), and fibrous plaque volume (beta = 0.24, p = 0.026). ConclusionsThis study based on patients enrolled in a prospective OCT-guidance PCI trial shows that simulated residual OCT-mu QFR had good correlation, agreement, and diagnostic concordance with actual post-PCI OCT-mu QFR. In OCT