Troponins in cardiac amyloidosis: multipurpose markers

A review of cardiac troponin I detection by surface enhanced Raman spectroscopy: Under the spotlight of point-of-care testing

Cardiac troponin I (cTnI) is a biomarker widely related to acute myocardial infarction (AMI), one of the leading causes of death around the world. Point-of-care testing (POCT) of cTnI not only demands a short turnaround time for its detection but the highest accuracy levels to set expeditious and adequate clinical decisions. The analytical technique Surface-enhanced Raman spectroscopy (SERS) possesses several properties that tailor to the POCT format, such as its flexibility to couple with rapid assay platforms like microfluidics and paper-based immunoassays. Here, we analyze the strategies used for the detection of cTnI by SERS considering POCT requirements. From the detection ranges reported in the reviewed literature, we suggest the diseases other than AMI that could be diagnosed with this technique. For this, a section with information about cardiac and non-cardiac diseases with cTnI release, including their release kinetics or cut-off values are presented. Likewise, POCT features, the use of SERS as a POCT technique, and the biochemistry of cTnI are discussed. The information provided in this review allowed the identification of strengths and lacks of the available SERS-based point-of-care tests for cTnI and the disclosing of requirements for future assays design.

Prevalence and Risk Factors of Cardiac Amyloidosis in Heart Failure: A Systematic Review and Meta-Analysis

AIMS

Heart failure (HF) is one of the leading causes of mortality worldwide. Heart failure is also one of the most common presentations of cardiac amyloidosis (CA). Contemporary epidemiological data of CA in HF patients is lacking. Hence, this systematic review and meta-analysis was conducted to determine the prevalence of amyloidosis in HF patients, and to clarify the risk factors of concomitant CA and HF.

METHODS

A systematic review and meta-analysis was performed. Studies were retrieved from Medline, EMBASE, Scopus and Cochrane library. The search was not restricted in time, type or language of publication. The prevalence of CA in HF grouped according to diagnostic techniques and risk factors of CA with HF was analysed.

RESULTS

Eleven (11) studies were included, involving 3,303 patients. The pooled prevalence of CA in HF was 13.7%. The overall prevalence of CA in HF with preserved ejection fraction was 15.1%, and that of HF with reduced ejection fraction was 11.3%. The main factors associated with the diagnosis of CA in HF included older age, males, raised NT pro-BNP, increased interventricular septal thickness in diastole, apical sparing, and reduced left ventricular systolic function.

CONCLUSION

A high index of clinical suspicion is required to identify HF patients with CA. Supportive investigations may be helpful when clinically correlated. A considerable proportion of HF patients have CA and certain risk factors may be helpful in increasing suspicions of CA in HF.

New Advanced Imaging Parameters and Biomarkers-A Step Forward in the Diagnosis and Prognosis of TTR Cardiomyopathy

Transthyretin amyloid cardiomyopathy (ATTR-CM) is an infiltrative disorder characterized by extracellular myocardial deposits of amyloid fibrils, with poor outcome, leading to heart failure and death, with significant treatment expenditure. In the era of a novel therapeutic arsenal of disease-modifying agents that target a myriad of pathophysiological mechanisms, timely and accurate diagnosis of ATTR-CM is crucial. Recent advances in therapeutic strategies shown to be most beneficial in the early stages of the disease have determined a paradigm shift in the screening, diagnostic algorithm, and risk classification of patients with ATTR-CM. The aim of this review is to explore the utility of novel specific non-invasive imaging parameters and biomarkers from screening to diagnosis, prognosis, risk stratification, and monitoring of the response to therapy. We will summarize the knowledge of the most recent advances in diagnostic, prognostic, and treatment tailoring parameters for early recognition, prediction of outcome, and better selection of therapeutic candidates in ATTR-CM. Moreover, we will provide input from different potential pathways involved in the pathophysiology of ATTR-CM, on top of the amyloid deposition, such as inflammation, endothelial dysfunction, reduced nitric oxide bioavailability, oxidative stress, and myocardial fibrosis, and their diagnostic, prognostic, and therapeutic implications.

Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging

Aims: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Methods and Results: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. Conclusions: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.

The Impact of Patients With Cardiac Amyloidosis in HFpEF Trials

Heart failure with preserved ejection fraction (HFpEF) is an increasingly diagnosed condition whose failure to respond to new drugs effective in heart failure with reduced ejection fraction is of great concern. HFpEF is an incompletely understood and markedly heterogeneous syndrome, but cardiac amyloidosis is increasingly recognized as one of its various causes. The specific hemodynamic and pathophysiological features of cardiac amyloidosis result in poor tolerance of heart failure medications and in worse outcomes compared with other causes. Until recently, patients considered for HFpEF trials were not routinely screened for cardiac amyloidosis. This review examines how real-world patients with cardiac amyloidosis met inclusion criteria for 8 major HFpEF clinical trials, including the recent PARAGON (Prospective Comparison of ARNI with ARB Global Outcomes in HF With Preserved Ejection Fraction) trial. This review discusses how the presence in the trial populations of a subset of patients with cardiac amyloidosis might contribute to explain the absence of efficacy of medications for HFpEF in trials so far. A multistep screening strategy is suggested in which patients with red flags for cardiac amyloidosis undergo both a light chain assay and technetium-labeled cardiac scintigraphy (technetium-labeled cardiac scintigraphy scan), which, when negative, rule out cardiac amyloidosis. Using this strategy would allow the testing of new medications for HFpEF in populations containing no patients with cardiac amyloidosis, thus potentially increasing the likelihood of showing therapeutic efficacy, and finally making some effective treatment available.

Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning.