Tafamidis improves outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). However, it is not yet known whether tafamidis affects cardiac amyloid deposition and structural changes in the myocardium. We aimed to determine disease-modifying effects on myocardial amyloid progression and to identify imaging parameters that could be applied for specific therapy monitoring. ATTR-CM patients underwent serial cardiac magnetic resonance (CMR) imaging using T1 mapping techniques to derive extracellular volume (ECV). Patients receiving tafamidis 61 mg (n = 35) or 20 mg (n = 15) once daily showed stable measurements at follow-up (FU) {61 mg: 9.0 [interquartile range (IQR) 7.0–11.0] months, 20 mg: 11.0 (IQR 8.0–18.0) months} in left ventricular (LV) ejection fraction (LVEF; 61 mg: 47.6% vs. 47.5%, P = 0.935; 20 mg: 52.4% vs. 52.1%, P = 0.930), LV mass index (LVMI; 61 mg: 110.2 vs. 106.2 g/m², P = 0.304; 20 mg: 114.5 vs. 115.4 g/m², P = 0.900), and ECV (61 mg: 47.5% vs. 47.7%, P = 0.861; 20 mg: 56.7% vs. 57.5%, P = 0.759), whereas treatment-naïve ATTR-CM patients (n = 19) had clear signs of disease progression at the end of the observation period [12.0 (IQR 10.0–21.0) months; LVEF: 53.3% vs. 45.7%, P = 0.031; LVMI: 98.9 vs. 106.9 g/m², P = 0.027; ECV: 49.3% vs. 54.6%, P = 0.023]. Between-group comparison at FU revealed positive effects in tafamidis 61 mg-treated compared to treatment-naïve patients (LVEF: P = 0.035, LVMI: P = 0.036, ECV: P = 0.030), while those treated with 20 mg showed no difference in the above LV measurements when compared with treatment-naïve (P = 0.120, P = 0.287, P = 0.158). However, both treatment groups showed clinically beneficial effects compared to the natural course [61 mg, 6-min walk distance (6-MWD): P = 0.005, N-terminal prohormone of brain natriuretic peptide (NT-proBNP): P = 0.002; 20 mg, 6-MWD: P = 0.023, NT-proBNP: P = 0.003]. Tafamidis delays myocardial amyloid progression in ATTR-CM patients, resulting in structural, functional, and clinical benefits compared to the natural course. Serial CMR including measurement of ECV may be appropriate for disease-specific therapy monitoring.

Tafamidis treatment positively affects left ventricular (LV) structure and function and improves outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). We aimed to investigate the relationship between treatment response and cardiac amyloid burden identified by serial quantitative ^99mTc-DPD SPECT/CT. We furthermore aimed to identify nuclear imaging biomarkers that could be used to quantify and monitor response to tafamidis therapy. Forty wild-type ATTR-CM patients who underwent ^99mTc-DPD scintigraphy and SPECT/CT imaging at baseline and after treatment with tafamidis 61 mg once daily [median, 9.0 months (interquartile range 7.0–10.0)] were divided into two cohorts based on the median (−32.3%) of the longitudinal percent change in standardized uptake value (SUV) retention index. ATTR-CM patients with a reduction greater than or equal to the median (n = 20) had a significant decrease in SUV retention index (P < 0.001) at follow-up, which translated into significant benefits in serum N-terminal prohormone of brain natriuretic peptide levels (P = 0.006), left atrial volume index (P = 0.038), as well as LV [LV global longitudinal strain: P = 0.028, LV ejection fraction (EF): P = 0.027, LV cardiac index (CI): P = 0.034] and right ventricular (RV) [RVEF: P = 0.025, RVCI: P = 0.048] functions compared with patients with a decrease less than the median (n = 20). Treatment with tafamidis in ATTR-CM patients results in a significant reduction in SUV retention index, associated with significant benefits for LV and RV function and cardiac biomarkers. Serial quantitative ^99mTc-DPD SPECT/CT imaging with SUV may be a valid tool to quantify and monitor response to tafamidis treatment in affected patients. ^99mTc-DPD SPECT/CT imaging with determination of SUV retention index as part of a routine annual examination can provide evidence of treatment response in ATTR-CM patients receiving disease-modifying therapy. Further long-term studies with ^99mTc-DPD SPECT/CT imaging may help to evaluate the relationship between tafamidis-induced reduction in SUV retention index and outcome in patients with ATTR-CM and will demonstrate whether highly disease-specific ^99mTc-DPD SPECT/CT imaging is more sensitive than routine diagnostic monitoring. Response to tafamidis treatment and effects on cardiac function and biomarkers. Serial quantitative ^99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid (DPD) single-photon emission computed tomography/computed tomography (SPECT/CT) imaging with standardized uptake value (SUV) of representative patients with wild-type transthyretin amyloid cardiomyopathy treated with tafamidis 61 mg once daily. Patients were divided into two cohorts based on the median (−32.3%) of the longitudinal percent change in SUV retention index. Cohort A patients had a longitudinal percent reduction in SUV retention index greater than or equal to the median (upper panels), which was associated with significant benefits for left ventricular (LV) and right ventricular (RV) function and serum levels of N-terminal prohormone of brain natriuretic peptide (NT-proBNP). Cohort B patients had either a percent decrease less than the median or even a percent increase from baseline (lower panels), without significant benefits for LV and RV function and cardiac biomarkers.

Background: Extracorporeal membrane oxygenation (ECMO) represents a valuable and rapidly evolving therapeutic option in patients with severe heart or lung failure following cardiovascular surgery. However, despite significant advances in ECMO techniques and management, prognosis remains poor and accurate risk stratification challenging. We therefore evaluated the predictive value of liver function variables on all-cause mortality in patients undergoing venoarterial ECMO support after cardiovascular surgery. Methods: We included into our single-center registry a total of 240 patients undergoing venoarterial ECMO therapy following cardiovascular surgery at a university-affiliated tertiary care center. Results: The median follow-up was 37 months (interquartile range 19–67 months), and a total of 156 patients (65 %) died. Alkaline phosphatase and total bilirubin were the strongest predictors for 30-day mortality, with adjusted hazard ratios (HRs) per 1–standard deviation increase of 1.36 (95 % confidence interval [CI] 1.10–1.68; P = 0.004) and 1.22 (95 % CI 1.07–1.40; P = 0.004), respectively. The observed associations persisted for long-term mortality, with adjusted HRs of 1.27 (95 % CI 1.03–1.56; P = 0.023) for alkaline phosphatase and 1.22 (95 % CI 1.07–1.39; P = 0.003) for total bilirubin. Conclusions: The present study demonstrates that elevated values of alkaline phosphatase and total bilirubin are sensitive parameters for predicting the short-term and long-term outcomes of ECMO patients.

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.

Background Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. Objective The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis. Methods We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. Findings Areas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1–V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training. Interpretation Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.

(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.