Mortality risk in dilated cardiomyopathy: the accuracy of heart failure prognostic models and dilated cardiomyopathy-tailored prognostic model

Predicting survival in patients with severe heart failure: Risk score validation in the HELP-HF cohort

AIMS

Accurate selection of patients with severe heart failure (HF) who might benefit from advanced therapies is crucial. The present study investigates the performance of the available risk scores aimed at predicting the risk of mortality in patients with severe HF.

METHODS AND RESULTS

The risk of 1-year mortality was estimated in patients with severe HF enrolled in the HELP-HF cohort according to the MAGGIC, 3-CHF, ADHF/NT-proBNP, and GWTG-HF risk scores, the number of criteria of the 2018 HFA-ESC definition of advanced HF, I NEED HELP markers, domains fulfilled of the 2019 HFA-ESC definition of frailty, the frailty index, and the INTERMACS profile. In addition, we tested the performance of different machine learning (ML)-based models to predict 1-year mortality. At 1-year follow-up, 265 patients (23.1%) died. The prognostic accuracy, tested in the subgroup of patients with completeness of all data regarding the variables included in the scores (497/1149 patients), resulted moderate for MAGGIC, GWTG-HF, and ADHF/NT-proBNP scores (area under the curve [AUC] ≥0.70) and only poor for the other tools. All the scores lost accuracy in estimating the rate of 1-year mortality in patients at the highest risk. Support vector machine-based model had the best AUC among ML-based models, slightly outperforming most of the tested risk scores.

CONCLUSION

Most of the scores used to predict the risk of mortality in HF performed poorly in real-world patients with severe HF and provided inaccurate estimate of the risk of 1-year mortality in patients at the highest risk. ML-based models did not significantly outperform the currently available risk scores and their use must be validated in large cohort of patients.

Left ventricular diastolic dysfunction worsens prognosis in patients with heart failure due to dilated cardiomyopathy

AIMS

The prognostic significance of left ventricular (LV) diastolic dysfunction (LVDD) severity in patients with dilated cardiomyopathy (DCM) remains uncertain. This study aimed to evaluate the association of LVDD severity and elevated left atrial pressure (eLAP) with patient outcomes in stable, non-acutely decompensated patients with DCM.

METHODS

This single-centre, retrospective, observational study involved 740 DCM patients (either inpatients or outpatients) managed at our tertiary cardiac centre between 2010 and 2021. Due to incomplete data, 96 patients were excluded. LVDD and eLAP were assessed using echocardiography according to the 2016 guidelines of the European Association of Cardiovascular Imaging (EACVI). The primary outcomes were all-cause mortality and heart failure (HF)-related mortality.

RESULTS

The final cohort comprised of 644 DCM patients [mean age: 52 ± 12 years, LV ejection fraction (LVEF): 26 ± 10%]. Over a median follow-up period of 41 (18.5-66.7) months, 105 (16.3%) patients died: 8 (5.3%) patients in the normal left atrial pressure (nLAP) group and 97 (19.6%) patients in the eLAP group. eLAP was identified as an independent prognostic factor for both all-cause mortality [hazard ratio (HR) 2.0; 95% confidence interval (CI) 1.1-3.7; P = 0.01] and HF-related mortality (HR 2.5; 95% CI 1.01-6.5; P = 0.04), even after adjusting for LVEF and atrial fibrillation (AF) presence. Additionally, HF-related mortality rates were significantly higher in patients with moderate to severe LVDD compared with those with mild LVDD [5 (3.3%) vs. 67 (13.6%), P < 0.05].

CONCLUSIONS

This study's findings highlight the importance of assessing the severity of LVDD in patients with DCM, which provides incremental prognostic information over LVEF.

Nexilin mutations, a cause of chronic heart failure: A state-of-the-art review starting from a clinical case

Heart failure (HF) is a medical condition associated with high morbidity and mortality, despite ongoing advances in diagnosis and treatment. Among the various causes of HF, cardiomyopathies are particularly significant and must be thoroughly diagnosed and characterized from the outset. In this review, we aim to present a brief overview of cardiomyopathies as a driver of HF, with a specific focus on the genetic causes, particularly nexilin (NEXN) cardiomyopathy, illustrated by a clinical case. The case involves a 63-year-old male who presented with HF symptoms at moderate exertion. Six months prior, he had been asymptomatic, and a routine transthoracic echocardiography had shown a preserved left ventricular ejection fraction (LVEF). However, during the current evaluation, transthoracic echocardiography revealed a dilated left ventricle with a severely reduced LVEF of 30%. Subsequent coronary angiography ruled out ischemic heart disease, while cardiac magnetic resonance imaging indicated a non-inflammatory, non-infiltrative dilated cardiomyopathy with extensive LV fibrosis. Genetic testing identified a heterozygous in-frame deletion variant in the NEXN gene [c.1949_1951del, p.(Gly650del)], classified as likely pathogenic. State-of-the-art HF treatment was initiated, including cardiac resynchronization therapy with defibrillator support. Following treatment, the patient's symptoms resolved, and LVEF improved to 42%. Interestingly, this patient experienced the onset of symptoms and left ventricular dysfunction within just six months, a much faster progression compared to previously documented cases where the G650del NEXN variant is typically linked to a more gradual development of dilated cardiomyopathy. Current literature offers limited data on patients with NEXN mutations, and the connection between this gene and both dilated and hypertrophic cardiomyopathies remains an area of active research.

One-year mortality and re-admission rate by disease etiology in National Heart Failure Registry of India

Survival outcomes of patients with heart failure (HF) based on their disease etiology are not well described. Here, we provide one-year mortality outcomes of 10850 patients with HF (mean age = 59.9 years, 31% women) in India. Ischemic heart disease (71.9%), dilated cardiomyopathy (17.3), rheumatic heart disease (5.4), non-rheumatic valvular heart disease (1.9), hypertrophic cardiomyopathy (0.8), congenital heart disease (0.7), peri-partum cardiomyopathy (0.5), restrictive cardiomyopathy (0.4), and infective endocarditis (0.1) were the main disease etiologies. Mortality rate per 100-person years of follow-up varied from 13.8 (95% CI: 6.2-30.7) in peri-partum cardiomyopathy to 92.9 (46.5-185.9) in infective endocarditis. Compared to ischemic heart disease, the mortality was two to five times higher in rheumatic heart disease (HR = 2.0; 95% CI: 1.6-2.4), congenital heart disease (2.9; 1.9-4.2), and infective endocarditis (4.8; 2.4-9.8). The wide variations in mortality rate in HF patients may bring possible clinical applicability of risk stratification.

Comprehensive analysis of clinical characteristics, management, and prognosis in patients with dilated cardiomyopathy discharged from Spanish hospitals

OBJECTIVE

Dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF) characterized by left ventricular dilatation and systolic dysfunction not explained by abnormal loading conditions. Despite its prevalence, DCM's epidemiology and prognosis remain poorly studied in our country.

METHODS

A retrospective observational study encompassed patients discharged from all Spanish public hospitals between 2016 and 2021 diagnosed with DCM. Data were extracted from the Minimum Basic Data Set. The study focused on hospital admissions, comorbidities, in-hospital mortality, and readmission rates for circulatory system diseases at 30 and 365 days.

RESULTS

Among 27,402 index episodes, DCM was the primary diagnosis in 12.4%, predominantly affecting men (72.5%). In-hospital mortality was 8.7%, with significant predictors including cardiogenic shock (OR: 12.4, 95% CI: 9.6-15.9), advanced or metastatic cancer (OR: 4.3, 95% CI: 3.8-5.0), renal failure (OR: 2.4, 95% CI: 2.2-2.7), and chronic liver disease (OR: 2.4, 95% CI: 2.1-2.8). Readmission rates were 7.9% at 30 days and 25.5% at 365 days, predominantly due to HF. Multivariate analysis identified age (IRR: 1.02, 95% CI: 1.01-1.02), female sex (IRR: 0.87, 95% CI: 0.79-0.96), severe hematological diseases (IRR: 2.12, 95% CI: 1.45-3.10), and metastatic cancer (IRR: 1.65, 95% CI: 1.31-2.07) as predictors of 30-day readmissions. At 365 days, predictors included age (IRR: 1.02, 95% CI: 1.01-1.02), female sex (IRR: 0.80, 95% CI: 0.74-0.86), severe hematological diseases (IRR: 2.43, 95% CI: 1.66-3.56), and renal failure (IRR: 1.42, 95% CI: 1.31-1.55).

CONCLUSION

This study highlights the substantial hospitalization burden and mortality risk among DCM patients, emphasizing the necessity for advanced management strategies and specialized cardiac care.

Relation between cardiac magnetic resonance-assessed interstitial fibrosis and diastolic dysfunction in heart failure due to dilated cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is distinguished by left ventricle (LV) dilation accompanied by systolic dysfunction. However, some studies suggested also a high prevalence of LV diastolic dysfunction (LVDD), similar to a general cohort of heart failure (HF) with reduced ejection fraction (LVEF). The bulk of evidence, mostly arising from basic studies, suggests a causative link between cardiac fibrosis (CF) and LVDD. However, still, there remains a scarcity of data on LVDD and CF. Therefore, the aim of the study was to investigate the association between CF and LVDD in DCM patients.

Methods

The study population was composed of 102 DCM patients. Replacement CF was evaluated qualitatively (late gadolinium enhancement - LGE) and quantitively (LGE extent); interstitial cardiac fibrosis was assessed via extracellular volume (ECV). Based on echocardiography patients were divided into normal and elevated left atrial pressure (nLAP, eLAP) groups.

Results

42 % of patients had eLAP. They displayed higher troponin and NT-proBNP. Both groups did not differ in terms of LGE presence and extent; however, eLAP patients had larger ECV: 30.1 ± 5.6 % vs. 27.8 ± 3.9 %, p = 0.03. Moreover, ECV itself was found to be an independent predictor of LVDD (OR = 0.901; 95 %CI 0.810-0.999; p = 0.047; normalised for LVEF and RVOT diameter).

Conclusions

More than two-in-five DCM patients had at least moderate LVDD. The mere presence or extent of replacement cardiac fibrosis is similar in patients with nLAP and eLAP. On the other hand, interstitial cardiac fibrosis is more pronounced in those with a higher grade of LVDD. ECV was found to be an independent predictor of LVDD in DCM.

Applicability and performance of heart failure prognostic scores in dilated cardiomyopathy: the real-world experience of an Italian referral center for cardiomyopathies

BACKGROUND

The performance of heart failure (HF) risk models is validated in the general population with HF but in specific aetiological settings, and specifically in dilated cardiomyopathy (DCM), has scarcely been explored. We tested eight of the main prognostic scores used in HF in a large real-world population of patients with DCM.

METHODS

We included 784 consecutive DCM patients enrolled, both inpatients and outpatients, enrolled between January 2000 and December 2017. The risk of 1 and/or 3-year all-cause mortality/heart transplantation/durable left ventricular assist device (LVAD) implantation (D/HTx/LVAD) was estimated in our cohort according to the following risk scores SHFM, 3-CHF, CHARM, MAGGIC, GISSI-HF, MECKI, Barcelona Bio-HF, Krakow score and their accuracy calculated through the receiver operator characteristic (ROC) curve analysis.

RESULTS

During a median follow-up of 5.8 years (Interquartile Range 3.2-7.6 years), 191 patients (20%) died or underwent HTx/LVAD (158 deaths, 30 heart transplantations, and 3 LVAD implantations). The high missing rate allowed to calculated only four prognostic models (MAGGIC, CHARM, 3-CHF and SHFM). All the scores overestimated the rate of D/HTx/LVAD. The prognostic accuracy was suboptimal for MAGGIC (AUC 0.754) and CHARM (AUC 0.720) scores and only modest for 3-CHF (AUC 0.677) and SHFM (AUC 0.667).

CONCLUSIONS

Main prognostic scores for the risk stratification of HF are only partially applicable to real-world patients with DCM. MAGGIC and CHARM scores showed the best accuracy, despite the overestimation of risk. Our findings corroborate the need of specific risk scores for the prognostic stratification of DCM.

CLINICAL PERSPECTIVE

What is new? The present study is the largest analysis in literature which investigate how the main existing heart failure prognostic risk scores performed in a real-world of dilated cardiomyopathy population, both in- and outpatients. What are the clinical implications? DCM is a stand-alone model of heart failure, where the performance of multiple heart failure prognostic scores for the risk stratification is quite limited. The need for contemporary, dedicated prognostic scores in this disease is increasingly evident.

Circulating biomarker- and magnetic resonance-based nomogram predicting long-term outcomes in dilated cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) has a high mortality rate and is the most common indication for heart transplantation. Our study sought to develop a multiparametric nomogram to assess individualized all-cause mortality or heart transplantation (ACM/HTx) risk in DCM patients.

METHODS

The present study is a retrospective cohort study. The demographic, clinical, blood test, and cardiac magnetic resonance imaging (CMRI) data of DCM patients in the tertiary center (Fuwai Hospital) were collected. The primary endpoint was ACM/HTx. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for variable selection. Multivariable Cox regression was used to develop a nomogram. The concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

RESULTS

A total of 218 patients were included in the present study. They were randomly divided into a training cohort and a validation cohort. The nomogram was established based on eight variables, including mid-wall late gadolinium enhancement, systolic blood pressure, diastolic blood pressure, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-diastolic volume index, free triiodothyronine, and N-terminal pro-B type natriuretic peptide. The AUCs regarding 1-year, 3-year, and 5-year ACM/HTx events were 0.859, 0.831, and 0.840 in the training cohort and 0.770, 0.789, and 0.819 in the validation cohort, respectively. The calibration curve and DCA showed good accuracy and clinical utility of the nomogram.

CONCLUSIONS

We established and validated a circulating biomarker- and CMRI-based nomogram that could provide a personalized prediction of ACM/HTx for DCM patients, which might help risk stratification and decision-making in clinical practice.

Potential Prognostic Relevance of Left-Ventricular Global Longitudinal Strain and of the Summation of the Mitral and Tricuspid Regurgitation Volume in Patients with Non-Ischemic Dilated Cardiomyopathy

BACKGROUND

The aim of this pilot study was to determine the potential prognostic relevance of novel multidirectional myocardial and volumetric echocardiographic parameters in patients with non-ischemic dilated cardiomyopathy (NIDCM).

METHODS

Multidirectional myocardial parameters (longitudinal, radial, and circumferential left-ventricular (LV) strain using speckle tracking echocardiography) and a new volumetric parameter (the sum of the mitral and tricuspid regurgitation volume (mitral-tricuspid regurgitation volume) were assessed. The cardiovascular (CV) outcome was a composite of cardiac death and hospitalization for heart failure (HF) at 1 year.

RESULTS

Approximately 102 patients were included in this pilot study. The mean LV ejection fraction (LVEF) was 28.4 ± 8.9%. During a follow-up of 1 year, the CV outcome occurred in 39 patients (10 HF deaths, and 36 hospitalizations for HF). The LV global longitudinal systolic strain (GLS) and mitral-tricuspid regurgitation volume were the main parameters that were seen to be significantly altered in the comparison of patients with events vs. those without events (GLS (absolute values) 7.4 ± 2.7% vs. 10.3 ± 2.6%; mitral-tricuspid regurgitation volume 61.1 ± 20.4 mL vs. 40.9 ± 22.9 mL, respectively; p-value < 0.01). In line with these findings, in a multivariate continuous logistic regression analysis, the GLS and mitral-tricuspid regurgitation volume were the main parameters associated with worse CV outcomes (GLS: OR 0.77 (95%CI 0.65-0.92); mitral-tricuspid regurgitation volume OR 1.09 (95%CI 1.01-1.25)), whereas the radial and circumferential LV global strain and mitral regurgitation volume and tricuspid regurgitation volume were not linked to the CV outcome. Furthermore, in a receiver operating characteristic curve analysis, a GLS cutoff of <7.5% and mitral-tricuspid regurgitation volume > 60 mL were the identified values for the parameters associated with worse CV outcomes.

CONCLUSIONS

The findings of this pilot study suggest that the GLS and a novel volumetric parameter (the sum of the mitral and tricuspid regurgitation volume) are linked to worse CV outcomes in patients with non-ischemic dilated cardiomyopathy. Hence, these promising results warrant further validation in larger studies.

Clinical characteristics and one-year all-cause mortality outcomes in Africans with dilated cardiomyopathy

AIMS

Dilated cardiomyopathy (DCM) is a common cause of heart failure in sub-Saharan Africa (SSA). The affected individuals present with new-onset heart failure with reduced ejection fraction and no identifiable primary or secondary aetiology. We aim to describe the clinical characteristics of participants with heart failure of unknown origin.

METHODS

We screened 161 participants with heart failure of unknown origin and prospectively excluded primary and secondary causes of DCM. All study participants were subjected to laboratory biochemical testing, echocardiography, cardiovascular magnetic resonance (CMR) imaging and invasive coronary angiography.

RESULTS

The study comprised 93 participants with a mean age of 47.5 SD 13.1 years. Forty-six (56.1%) participants had evidence of late gadolinium enhancement (LGE) on imaging, and LGE was visualised in the mid wall in 28 (61.0%) of these participants. After a median duration of 13.4 months [interquartile range (IQR): 8.8-28.9 months], 18 (19%) participants died. Non-survivors had a higher median left atrial volume index (44.9 mL/m2 (IQR: 34.4-58.7) compared to survivors [32.9 mL/m2 (IQR: 24.5-47.0), p = 0.017)]. The rate of all-cause rehospitalisation was 29.3%, of which 17 of the 22 re-hospitalisations were heart failure related.

CONCLUSION

Dilated cardiomyopathy in Africans primarily affects young males. In our cohort, this disease was associated with an all-cause mortality of 19% in one year. In SSA, large multicenter studies are required to investigate this disease's pathogenesis and outcomes.

DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients

Aims

Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management.

Methods and results

Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated.

Conclusion

We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.

Comparison of Clinical Course and Outcomes between Dilated and Hypokinetic Non-Dilated Cardiomyopathy

BACKGROUND

By definition, dilated cardiomyopathy (DCM) is characterized by enlargement of the left ventricular (LV) cavity, and systolic dysfunction. However, in 2016 ESC introduced a new clinical entity - hypokinetic non-dilated cardiomyopathy (HNDC). HNDC is defined as LV systolic dysfunction without LV dilatation. However, the diagnosis of HNDC has so far rarely been made by a cardiologist, and it is unknown whether "classic" DCM differs from HNDC in terms of clinical course and outcomes.

OBJECTIVES

Comparison of heart failure profiles and outcomes between patients with "classic" dilated (DCM) and HNDCs.

METHOD

We retrospectively analysed 785 DCM patients, defined as impaired left ventricle (LV) systolic function (ejection fraction [LVEF] <45%) in the absence of coronary artery disease, valve disease, congenital heart disease, and severe arterial hypertension. "Classic" DCM was diagnosed when LV dilatation was present (LV end-diastolic diameter >52 mm/58 mm in women/men); otherwise, HNDC was diagnosed. After 47 ± 31 months, the all-cause mortality and composite endpoint (all-cause mortality, heart transplant - HTX, left ventricle assist device implantation - LVAD) were assessed.

RESULTS

There were 617 (79%) patients with LV dilatation. Patients with "classic" DCM differed from HNDC in terms of clinically relevant parameters [hypertension (47% vs. 64%, p = 0.008), ventricular tachyarrhythmias (29% vs. 15%, p = 0.007), NYHA class (2.5 ± 0.9 vs. 2.2 ± 0.8, p = 0.003)], had lower cholesterol (LDL: 2.9 ± 1.0 vs. 3.2 ± 1.1 mmol/L, p = 0.049), and higher N-terminal pro-brain natriuretic peptide (3,351 ± 5,415 vs. 2,563 ± 8584 pg/mL, p = 0.0001) and required higher diuretics dosages (57.8 ± 89.5 vs. 33.7 ± 48.7 mg/day, p ≤ 0.0001). All of their chambers were larger (LVEDd: 68.3 ± 4.5 vs. 52.7 ± 3.5 mm, p < 0.0001) and they had lower LVEF (25.2 ± 9.4 vs. 36.6 ± 11.7%, p < 0.0001). During the follow-up, there were 145 (18%) composite endpoints ("classic" DCM vs. HNDC: 122 [20%] vs. 26 [18%], p = 0.22): deaths (97 [16%] vs. 24 [14%], p = 0.67), HTX (17 [4%] vs. 4 [4%], p = 0.97) and LVAD (19 [5%] vs. 0 [0%], p = 0.03). Both groups did not differ in terms of all-cause mortality (p = 0.70), cardiovascular (CV) mortality (p = 0.37) and composite endpoint (p = 0.26).

CONCLUSIONS

LV dilatation was absent in more than one-fifth of DCM patients. HNDC patients had less severe heart failure symptoms, less advanced cardiac remodelling, and required lower diuretics dosages. On the other hand, "classic" DCM and HNDC patients did not differ in terms of all-cause mortality, CV mortality, and composite endpoint.

Prognostic value of summed motion score assessed by gated SPECT myocardial perfusion imaging in patients with dilated cardiomyopathy

Background

The prognosis of patients with dilated cardiomyopathy (DCM) is poor and new indicators are urgently needed to predict lethal cardiac events. This study aimed to investigate the value of summed motion score (SMS) in predicting cardiac death of DCM patients using gated single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).

Methods and results

Eighty-one patients with DCM who underwent 99mTc-MIBI gated SPECT MPI were retrospectively enrolled and were divided into cardiac death and survivor groups. The functional parameters of left ventricle including SMS were measured using quantitative gated SPECT software. During the follow-up period of 44 (25, 54) months, 14 (17.28%) cardiac deaths were observed. Compared with the survivor group, SMS was significantly higher in the cardiac death group. Multivariate cox regression analysis showed that SMS was an independent predictor for cardiac death (HR 1.34, 95% CI 1.02–1.77, P = 0.034). SMS also provided incremental prognostic value over other variables in the multivariate model as determined by likelihood ratio global chi-squared test. In the Kaplan-Meier survival analysis, the event-free survival rate was significantly lower in the high-SMS (HSMS) group than the low-SMS (LSMS) (log-rank P &lt; 0.001). Furthermore, the area under curve (AUC) of SMS was larger than that of LVEF at the 12th month of follow-up (0.85 vs. 0.80, P = 0.045).

Conclusion

SMS is an independent predictor of cardiac death in DCM patients and provides incremental prognostic value. SMS might have higher predictive value than LVEF for early cardiac death.

Functional assays reveal the pathogenic mechanism of a de novo tropomyosin variant identified in patient with dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and a major indicator for heart transplant. Human genetic studies have identified over a thousand causal mutations for DCM in genes involved in a variety of cellular processes, including sarcomeric contraction. A substantial clinical challenge is determining the pathogenicity of novel variants in disease-associated genes. This challenge of connecting genotype and phenotype has frustrated attempts to develop effective, mechanism-based treatments for patients. Here, we identified a de novo mutation (T237S) in TPM1, the gene that encodes the thin filament protein tropomyosin, in a patient with DCM and conducted in vitro experiments to characterize the pathogenicity of this novel variant. We expressed recombinant mutant protein, reconstituted it into thin filaments, and examined the effects of the mutation on thin filament function. We show that the mutation reduces the calcium sensitivity of thin filament activation, as previously seen for known pathogenic mutations. Mechanistically, this shift is due to mutation-induced changes in tropomyosin positioning along the thin filament. We demonstrate that the thin filament activator omecamtiv mecarbil restores the calcium sensitivity of thin filaments regulated by the mutant tropomyosin, which lays the foundation for additional experiments to explore the therapeutic potential of this drug for patients harboring the T237S mutation. Taken together, our results suggest that the TPM1 T237S mutation is likely pathogenic and demonstrate how functional in vitro characterization of pathogenic protein variants in the lab might guide precision medicine in the clinic.

Outcomes after transvenous defibrillator implantation in cardiac sarcoidosis: A systematic review

Introduction

Sarcoidosis is a systemic inflammatory disorder associated with ventricular arrhythmias (VAs) and sudden death in the context of cardiac involvement. Guidelines advocate implantable cardioverter-defibrillator (ICD) implantation in specific subcohorts, but there is a paucity of data on outcomes.

Methods and Results

A systematic review was performed to assess outcomes in patients with definite or probable cardiac sarcoidosis (CS) treated with ICD. Observational studies were identified from multiple databases from inception to 21st May 2021. Outcomes of interest included appropriate and inappropriate ICD therapies in addition to all-cause mortality. Study quality was assessed individually using the Newcastle Ottawa Scale (NOS).Eight studies were identified comprising 530 patients, with follow-up period of 24-66 months (weighted average 40 months). Mean age was 53.9 years with ejection fraction of 41.3%. Overall incidence of appropriate therapy was 38.1% during follow-up. Left ventricular systolic dysfunction (LVSD) with ejection fraction <40% was a predictor of appropriate therapy in the majority of studies, as were sustained VAs during electrophysiological testing (EP) in one study. There was no interaction with device indication (i.e. primary or secondary). Where documented, inappropriate therapy was primarily driven by atrial arrhythmias. All-cause mortality was 6.0% over a median follow-up period of 42 months. Only three studies achieved good quality in the comparability domain of NOS.

Conclusions

Appropriate ICD therapy in patients with CS is commonly associated with LVSD, which can act as a surrogate for scar burden. The utility of EP testing in this setting remains unclear.

Development and validation of a nomogram to predict the risk of death within 1 year in patients with non-ischemic dilated cardiomyopathy: a retrospective cohort study

Predicting the chances mortality within 1 year in non-ischemic dilated cardiomyopathy patients can be very useful in clinical decision-making. This study has developed and validated a risk-prediction model for identifying factors contributing to mortality within 1 year in such patients. The predictive nomogram was constructed using a retrospective cohort study, with 615 of patients hospitalized in the First Affiliated Hospital of Guangxi Medical University between October 2012 and May 2020. A variety of factors, including presence of comorbidities, demographics, results of laboratory tests, echocardiography data, medication strategies, and instances of heart transplant or death were collected from electronic medical records and follow-up telephonic consultations. The least absolute shrinkage and selection operator and logistic regression analyses were used to identify the critical clinical factors for constructing the nomogram. Calibration, discrimination, and clinical usefulness of the predictive model were assessed using the calibration plot, C-index and decision curve analysis. Internal validation was assessed with bootstrapping validation. Among the patients from whom follow-up data were obtained, the incidence of an end event (deaths or heart transplantation within 1 year) was 171 cases per 1000 person-years (105 out of 615). The main predictors included in the nomogram were pulse pressure, red blood cell count, left ventricular end-diastolic dimension, levels of N-terminal pro b-type natriuretic peptide, medical history, in-hospital worsening heart failure, and use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers. The model showed excellent discrimination with a C-index of 0.839 (95% CI 0.799-0.879), and the calibration curve demonstrated good agreement. The C-index of internal validation was 0.826, which demonstrated that the model was quite efficacious. A decision curve analysis confirmed that our nomogram was clinically useful. In this study, we have developed a nomogram that can predict the risk of death within 1 year in patients with non-ischemic dilated cardiomyopathy. This will be useful in the early identification of patients in the terminal stages for better individualized clinical decisions.

Clinical Utility and Validation of the Krakow DCM Risk Score—A Prognostic Model Dedicated to Dilated Cardiomyopathy

Background: One of the most common causes of heart failure is dilated cardiomyopathy (DCM). In DCM, the mortality risk is high and reaches approximately 20% in 5 years. A patient’s prognosis should be established for appropriate HF management. However, so far, no validated tools have been available for the DCM population. Methods: The study population consisted of 735 DCM patients: 406 from the derivation cohort (previously described) and 329 from the validation cohort (from 2009 to 2020, with outcome data after a mean of 42 months). For each DCM patient, the individual mortality risk was calculated based on the Krakow DCM Risk Score. Results: During follow-up, 49 (15%) patients of the validation cohort died. They had shown significantly higher calculated 1-to-5-year mortality risks. The Krakow DCM Risk Score yielded good discrimination in terms of overall mortality risk, with an AUC of 0.704–0.765. Based on a 2-year mortality risk, patients were divided into non-high (≤6%) and high (>6%) mortality risk groups. The observed mortality rates were 8.3% (n = 44) vs. 42.6% (n = 75), respectively (HR 3.37; 95%CI 1.88–6.05; p < 0.0001). Conclusions: The Krakow DCM Risk Score was found to have good predictive accuracy. The 2-year mortality risk > 6% has good discrimination for the identification of high-risk patients and can be applied in everyday practice.

What can machines learn about heart failure? A systematic literature review

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

Inflammation-Related Biomarkers Are Associated with Heart Failure Severity and Poor Clinical Outcomes in Patients with Non-Ischemic Dilated Cardiomyopathy

Inflammation-related biomarkers are associated with clinical outcomes in mixed-etiology chronic heart failure populations. Inflammation-related markers tend to be higher in ischemic than in non-ischemic dilated cardiomyopathy (NI-DCM) patients, which might impact their prognostic performance in NI-DCM patients. Therefore, we aimed to assess the association of inflammation-related biomarkers with heart failure severity parameters and adverse cardiac events in a pure NI-DCM patient cohort. Fifty-seven patients with NI-DCM underwent endomyocardial biopsy. Biopsies were evaluated by immunohistochemistry for CD3+, CD45ro+, CD68+, CD4+, CD54+, and HLA-DR+ cells. Blood samples were tested for high-sensitivity C-reactive protein (hs-CRP), interleukin-6, tumor necrosis factor-α (TNF-α), soluble urokinase-type plasminogen activator receptor and adiponectin. During a five-year follow-up, twenty-seven patients experienced at least one composite adverse cardiac event: left ventricle assist device implantation, heart transplantation or death. Interleukin-6, TNF-α and adiponectin correlated with heart failure severity parameters. Patients with higher levels of interleukin-6, TNF-α, adiponectin or hs-CRP, or a higher number of CD3+ or CD45ro+ cells, had lower survival rates. Interleukin-6, adiponectin, and CD45ro+ cells were independently associated with poor clinical outcomes. All patients who had interleukin-6, TNF-α and adiponectin concentrations above the threshold experienced an adverse cardiac event. Therefore, a combination of these cytokines can identify high-risk NI-DCM patients.

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.

Mortality risk in dilated cardiomyopathy: the accuracy of heart failure prognostic models and dilated cardiomyopathy-tailored prognostic model

Aims

The aims of this paper were to investigate the analytical performance of the nine prognostic scales commonly used in heart failure (HF), in patients with dilated cardiomyopathy (DCM), and to develop a unique prognostic model tailored to DCM patients.

Methods and results

The hospital and outpatient records of 406 DCM patients were retrospectively analysed. The information on patient status was gathered after 48.2 ± 32.0 months. Tests were carried out to ascertain the prognostic accuracy in DCM using some of the most frequently applied HF prognostic scales (Barcelona Bio‐Heart Failure, Candesartan in Heart Failure‐Assessment of Reduction in Mortality and Morbidity, Studio della Streptochinasi nell'Infarto Miocardico‐Heart Failure, Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure, Meta‐Analysis Global Group in Chronic Heart Failure, MUerte Subita en Insuficiencia Cardiaca, Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure, Seattle Heart Failure Model) and one dedicated to DCM, that of Miura et al. At follow‐up, 70 DCM patients (17.2%) died. Most analysed scores substantially overestimated the mortality risk, especially in survivors. The prognostic accuracy of the scales were suboptimal, varying between 60% and 80%, with the best performance from Barcelona Bio‐Heart Failure and Seattle Heart Failure Model for 1–5 year mortality [areas under the receiver operating curve 0.792–0.890 (95% confidence interval 0.725–0.918) and 0.764–0.808 (95% confidence interval 0.682–0.934), respectively].Based on our accumulated data, a self‐developed DCM prognostic model was constructed. The model consists of age, gender, body mass index, symptoms duration, New York Heart Association class, diabetes mellitus, prior stroke, abnormal liver function, dyslipidaemia, left bundle branch block, left ventricle end‐diastolic diameter, ejection fraction, N terminal pro brain natriuretic peptide, haemoglobin, estimated glomerular filtration rate, and pharmacological and resynchronisation therapy. This newly created prognostic model outperformed the analysed HF scales.

Conclusions

An analysis of various HF prognostic models found them to be suboptimal for DCM patients. A self‐developed DCM prognostic model showed improved performance over the nine other models studied. However, further validation of the prognostic model in different DCM populations is required.