High-Sensitivity Troponin T For The Risk Assessment Of Patients With Acute Atrial Fibrillation

Cortés, Marcia; Arbucci, Rosina; Lambardi, Florencia; Furmento, Juan; Muñoz, Florencia; Viruel, Marcos; Alexander, Bryce; Baranchuk, Adrián; Costabel, Juan Pablo

doi:10.1016/j.cpcardiol.2021.101079

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Studies have demonstrated that SS can predict the prognosis of patients undergoing PCI or CABG, offering a significant theoretical basis for evaluating the severity and prognosis of coronary heart disease [ 33 ]. High-sensitivity Troponin T (HS-TnT) is a highly specific biomarker for myocardial injury assessment [ 34 ]. Troponin testing, as the preferred and essential condition for diagnosing myocardial infarction, reduces the missed diagnosis rate of acute coronary syndrome and myocardial infarction and is widely used in clinical settings [ 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features

Yang,

Xi,

Duan

et al. 2024

BMC Cardiovasc Disord

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Objective The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated. Methods A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC). Results In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21–2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42–4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840–0.946), and in the validation set, it was 0.910 (95% CI: 0.823–0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis. Conclusion The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.

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Section: Discussionmentioning

confidence: 99%

A nomogram model for predicting intramyocardial hemorrhage post-PCI based on SYNTAX score and clinical features

Yang,

Xi,

Duan

et al. 2024

BMC Cardiovasc Disord

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“…metric of severity of cardiac events, but as a measure of cell death (like ferritin) may have predictive value(Bai et al 2018;Cortés et al 2022;Costabel et al 2017;Kaura et al 2020; Lucrecia Maria et al 2020; Sepehri Shamloo et al. 2021) …”

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confidence: 99%

Fibrinaloid Microclots and Atrial Fibrillation

Kell,

Lip,

Pretorius

2024

Preprint

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Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question as to whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known ‘risk factors’ for AF, and in particular exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots are cytotoxic by inducing hypoxia/reperfusion and other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

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