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

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R2 = 0.48). The uncertainty of these predictions is demonstrated

Rationale and design of SAVI-AoS:A physiologic study of patients with symptomatic moderate aortic valve stenosis and preserved left ventricular ejection fraction

Background: Moderate aortic valve stenosis occurs twice as often as severe aortic stenosis (AS) and carries a similarly poor prognosis. Current European and American guidelines offer limited insight into moderate AS (MAS) patients with unexplained symptoms. Measuring valve physiology at rest while most patients experience symptoms during exertion might represent a conceptual limitation in the current grading of AS severity. The stress aortic valve index (SAVI) may delineate hemodynamically significant AS among patients with MAS. Objectives: To investigate the diagnostic value of SAVI in symptomatic MAS patients with normal left ventricular ejection fraction (LVEF ≥ 50%): aortic valve area (AVA) > 1 cm2 plus either mean valve gradient (MG) 15–39 mmHg or maximal aortic valve velocity (AOV max) 2.5–3.9 m/s. Short-term objectives include associations with symptom burden, functional capacity, and cardiac biomarkers. Long-term objectives include clinical outcomes. Methods and results: Multicenter, non-blinded, observational cohort. AS severity will be graded invasively (aortic valve pressure measurements with dobutamine stress testing for SAVI) and non-invasively (echocardiography during dobutamine and exercise stress). Computed tomography (CT) of the aortic valve will be scored for calcium, and hemodynamics simulated using computational fluid dynamics. Cardiac biomarkers and functional parameters will be serially monitored. The primary objective is to see how SAVI and conventional measures (MG, AVA and Vmax) correlate with clinical parameters (quality of life survey, 6-minute walk test [6MWT], and biomarkers). Conclusions: The SAVI-AoS study will extensively evaluate patients with unexplained, symptomatic MAS to determine any added value of SAVI versus traditional, resting valve parameters

Recovery of Absolute Coronary Blood Flow and Microvascular Resistance After Chronic Total Occlusion Percutaneous Coronary Intervention: An Exploratory Study

Background: This study aimed to investigate longitudinal physiological changes in the recanalized coronary chronic total occlusion (CTO) vessel and its dependent myocardium after successful percutaneous coronary intervention (PCI). Methods and Results: In this pilot study, 25 patients scheduled for elective CTO PCI with viable myocardium and angiographically visible collaterals were included. Absolute coronary blood flow and absolute microvascular resistance were measured invasively using continuous thermodilution. Measurements were performed immediately after successful CTO PCI and at short‐term follow‐up. In a subgroup of patients, physiological measurements were performed at the predominant donor vessel before CTO PCI, immediately afterwards, and at follow‐up. Absolute coronary blood flow in the recanalized CTO artery increased from 148±53 mL/min immediately after PCI to 221±77 mL/min at follow‐up (P<0.001). In agreement, absolute resistance in the myocardial territory perfused by the CTO artery, decreased from 545±255 Wood units immediately after the procedure to 387±128 Wood units at follow‐up (P=0.014). There were no significant changes in the absolute coronary blood flow and resistance in the predominant donor between baseline and follow‐up. Positive remodeling of the distal CTO vessel with an increase in lumen diameter was observed. Conclusions: After successful CTO PCI, blood flow in the recanalized artery and microvascular function of the dependent myocardium are not immediately normal but recover over time

Giant coronary aneurysm exposed on routine echocardiogram

Not available

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

Survival and quality of life after transcatheter aortic valve implantation relative to the general population

Background Little is known about survival and quality of life (QoL) of patients treated by transcatheter aortic valve implantation (TAVI) compared to the age- and sex-matched general population. In this study we compared subgroups of the National Heart Registration TAVI cohort to the Dutch age- and sex-matched population at the level of survival and QoL. Methods and results From the Netherlands Heart Registration (NHR) the TAVI cohort (5489 patients, period 2013–2017) was extracted. These data were compared to the national Dutch population data collected from the national statistics office, Statistics Netherlands (CBS). Subgroups were defined according to sex and age (80). For QoL analyses the age subgroups 75 were used. Long term survival was significantly higher in the general population compared to the TAVI population. Elderly TAVI patients (>80 years) had the same survival as the age-matched general population (46vs43% at 5 years, respectively). Survival in women was better than in men in both the general population and the TAVI cohort. Patients treated by TAVI, aged 65 years and older had a comparable QoL to that of the general population. Conclusions This study shows that TAVI patients aged 80 years and older have a similar long-term survival as an age-matched general population. However, because of lower survival in under 80 TAVI patients, the overall long term survival of all TAVI patients is worse than that of the general population in the Netherlands. This study also suggests that QoL after TAVI treatment is comparable to QoL in the general population

Gradient boosting on decision trees for mortality prediction in transcatheter aortic valve implantation

Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI. We adopt a modern gradient boosting on decision trees algorithm, specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling to identify 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 TAVI cases, reaching an AUC of 0.83. Our approach outperforms several widespread prognostic risk scores, such as logistic EuroSCORE II, the STS risk score and the TAVI2-score, which are broadly adopted by cardiologists worldwide