Prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome:a systematic review and meta-analysis

OBJECTIVE: To review, inventory and compare available diagnostic tools and investigate which tool has the best performance for prehospital risk assessment in patients suspected of non-ST-segment elevation acute coronary syndrome (NSTE-ACS). METHODS: Systematic review and meta-analysis. Medline and Embase were searched up till 1 April 2021. Prospective studies with patients, suspected of NSTE-ACS, presenting in the primary care setting or by emergency medical services (EMS) were included. The most important exclusion criteria were studies including only patients with ST-elevation myocardial infarction and studies before 1995, the pretroponin era. The primary end point was the final hospital discharge diagnosis of NSTE-ACS or major adverse cardiac events (MACE) within 6 weeks. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies Criteria. MAIN OUTCOME AND MEASURES: Sensitivity, specificity and likelihood ratio of findings for risk stratification in patients suspected of NSTE-ACS. RESULTS: In total, 15 prospective studies were included; these studies reflected in total 26 083 patients. No specific variables related to symptoms, physical examination or risk factors were useful in risk stratification for NSTE-ACS diagnosis. The most useful electrocardiographic finding was ST-segment depression (LR+3.85 (95% CI 2.58 to 5.76)). Point-of-care troponin was found to be a strong predictor for NSTE-ACS in primary care (LR+14.16 (95% CI 4.28 to 46.90) and EMS setting (LR+6.16 (95% CI 5.02 to 7.57)). Combined risk scores were the best for risk assessment in an NSTE-ACS. From the combined risk scores that can be used immediately in a prehospital setting, the PreHEART score, a validated combined risk score for prehospital use, derived from the HEART score (History, ECG, Age, Risk factors, Troponin), was most useful for risk stratification in patients with NSTE-ACS (LR+8.19 (95% CI 5.47 to 12.26)) and for identifying patients without ACS (LR-0.05 (95% CI 0.02 to 0.15)). DISCUSSION: Important study limitations were verification bias and heterogeneity between studies. In the prehospital setting, several diagnostic tools have been reported which could improve risk stratification, triage and early treatment in patients suspected for NSTE-ACS. On-site assessment of troponin and combined risk scores derived from the HEART score are strong predictors. These results support further studies to investigate the impact of these new tools on logistics and clinical outcome. FUNDING: This study is funded by ZonMw, the Dutch Organisation for Health Research and Development. TRIAL REGISTRATION NUMBER: This meta-analysis was published for registration in PROSPERO prior to starting (CRD York, CRD42021254122).</p

Functional SYNTAX Score for Risk Assessment in Multivessel Coronary Artery Disease

ObjectivesThis study was aimed at investigating whether a fractional flow reserve (FFR)-guided SYNTAX score (SS), termed “functional SYNTAX score” (FSS), would predict clinical outcome better than the classic SS in patients with multivessel coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI).BackgroundThe SS is a purely anatomic score based on the coronary angiogram and predicts outcome after PCI in patients with multivessel CAD. FFR-guided PCI improves outcomes by adding functional information to the anatomic information obtained from the angiogram.MethodsThe SS was prospectively collected in 497 patients enrolled in the FAME (Fractional Flow Reserve versus Angiography for Multivessel Evaluation) study. FSS was determined by only counting ischemia-producing lesions (FFR ≤0.80). The ability of each score to predict major adverse cardiac events (MACE) at 1 year was compared.ResultsThe 497 patients were divided into tertiles of risk based on the SS. After determining the FSS for each patient, 32% moved to a lower-risk group as follows. MACE occurred in 9.0%, 11.3%, and 26.7% of patients in the low-, medium-, and high-FSS groups, respectively (p < 0.001). Only FSS and procedure time were independent predictors of 1-year MACE. FSS demonstrated a better predictive accuracy for MACE compared with SS (Harrell's C of FSS, 0.677 vs. SS, 0.630, p = 0.02; integrated discrimination improvement of 1.94%, p < 0.001).ConclusionsRecalculating SS by only incorporating ischemia-producing lesions as determined by FFR decreases the number of higher-risk patients and better discriminates risk for adverse events in patients with multivessel CAD undergoing PCI. (Fractional Flow Reserve versus Angiography for Multivessel Evaluation [FAME]; NCT00267774

Evaluation of models of sequestration flow in coronary arteries—Physiology versus anatomy?

Background: Myocardial ischaemia results from insufficient coronary blood flow. Computed virtual fractional flow reserve (vFFR) allows quantification of proportional flow loss without the need for invasive pressure-wire testing. In the current study, we describe a novel, conductivity model of side branch flow, referred to as ‘leak’. This leak model is a function of taper and local pressure, the latter of which may change radically when focal disease is present. This builds upon previous techniques, which either ignore side branch flow, or rely purely on anatomical factors. This study aimed to describe a new, conductivity model of side branch flow and compare this with established anatomical models. Methods and results: The novel technique was used to quantify vFFR, distal absolute flow (Qd) and microvascular resistance (CMVR) in 325 idealised 1D models of coronary arteries, modelled from invasive clinical data. Outputs were compared to an established anatomical model of flow. The conductivity model correlated and agreed with the reference model for vFFR (r = 0.895, p < 0.0001; ＋0.02, 95% CI 0.00 to ＋ 0.22), Qd (r = 0.959, p < 0.0001; −5.2 mL/min, 95% CI −52.2 to ＋13.0) and CMVR (r = 0.624, p < 0.0001; ＋50 Woods Units, 95% CI −325 to ＋2549). Conclusion: Agreement between the two techniques was closest for vFFR, with greater proportional differences seen for Qd and CMVR. The conductivity function assumes vessel taper was optimised for the healthy state and that CMVR was not affected by local disease. The latter may be addressed with further refinement of the technique or inferred from complementary image data.The conductivity technique may represent a refinement of current techniques for modelling coronary side-branch flow. Further work is needed to validate the technique against invasive clinical data

The crux of maximum hyperemia: the last remaining barrier for routine use of fractional flow reserve

In the decision-making process of revascularization of coronary artery stenoses by percutaneous coronary intervention (PCI) or coronary artery bypass graft surgery (CABG), the presence and extent of reversible ischemia associated with such particular stenoses is of paramount importance (1, 2, 3). A stenosis associated with reversible ischemia (also called functionally significant or hemodynamically significant stenosis) causes symptoms of angina pectoris and has a negative influence on outcome (1, 2). Therefore, the general feeling is that such lesions should be revascularized if technically feasible. On the contrary, functionally nonsignificant stenoses do not cause symptoms by definition and have an excellent outcome with medical therapy (3, 4, 5). Therefore, revascularization of such lesions is generally not indicated

Decision Trees for Predicting Mortality in Transcatheter Aortic Valve Implantation

Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide