Genetic markers of cardiac autonomic neuropathy in the Kazakh population.

BACKGROUND: Cardiac autonomic neuropathy (CAN) is a complication of diabetes mellitus (DM) that increases the risk of morbidity and mortality by disrupting cardiac innervation. Recent evidence suggests that CAN may manifest even before the onset of DM, with prediabetes and metabolic syndrome potentially serving as precursors. This study aims to identify genetic markers associated with CAN development in the Kazakh population by investigating the SNPs of specific genes. MATERIALS AND METHODS: A case-control study involved 82 patients with CAN (cases) and 100 patients without CAN (controls). A total of 182 individuals of Kazakh nationality were enrolled from a hospital affiliated with the RSE "Medical Center Hospital of the President's Affairs Administration of the Republic of Kazakhstan". 7 SNPs of genes FTO, PPARG, SNCA, XRCC1, FLACC1/CASP8 were studied. Statistical analysis was performed using Chi-square methods, calculation of odds ratios (OR) with 95% confidence intervals (CI), and logistic regression in SPSS 26.0. RESULTS: Among the SNCA gene polymorphisms, rs2737029 was significantly associated with CAN, almost doubling the risk of CAN (OR 2.03(1.09-3.77), p = 0.03). However, no statistically significant association with CAN was detected with the rs2736990 of the SNCA gene (OR 1.00 CI (0.63-1.59), p = 0.99). rs12149832 of the FTO gene increased the risk of CAN threefold (OR 3.22(1.04-9.95), p = 0.04), while rs1801282 of the PPARG gene and rs13016963 of the FLACC1 gene increased the risk twofold (OR 2.56(1.19-5.49), p = 0.02) and (OR 2.34(1.00-5.46), p = 0.05) respectively. rs1108775 and rs1799782 of the XRCC1 gene were associated with reduced chances of developing CAN both before and after adjustment (OR 0.24, CI (0.09-0.68), p = 0.007, and OR 0.43, CI (0.22-0.84), p = 0.02, respectively). CONCLUSION: The study suggests that rs2737029 (SNCA gene), rs12149832 (FTO gene), rs1801282 (PPARG gene), and rs13016963 (FLACC1 gene) may be predisposing factors for CAN development. Additionally, SNPs rs1108775 and rs1799782 (XRCC1 gene) may confer resistance to CAN. Only one polymorphism rs2736990 of the SNCA gene was not associated with CAN.

Natural history of cardiac involvement in myotonic dystrophy type 1 - Emphasis on the need for lifelong follow-up.

BACKGROUND: Cardiac involvement represents a major cause of morbidity and mortality in patients with myotonic dystrophy type 1 (DM1) and prevention of sudden cardiac death (SCD) is a central part of patient care. We investigated the natural history of cardiac involvement in patients with DM1 to provide an evidence-based foundation for adjustment of follow-up protocols. METHODS: Patients with genetically confirmed DM1 were identified. Data on patient characteristics, performed investigations (12 lead ECG, Holter monitoring and echocardiography), and clinical outcomes were retrospectively collected from electronic health records. RESULTS: We included 195 patients (52% men) with a mean age at baseline evaluation of 41 years (range 14-79). The overall prevalence of cardiac involvement increased from 42% to 66% after a median follow-up of 10.5 years. There was a male predominance for cardiac involvement at end of follow-up (74 vs. 44%, p < 0.001). The most common types of cardiac involvement were conduction abnormalities (48%), arrhythmias (35%), and left ventricular systolic dysfunction (21%). Only 17% of patients reported cardiac symptoms. The standard 12­lead ECG was the most sensitive diagnostic modality and documented cardiac involvement in 24% at baseline and in 49% at latest follow-up. However, addition of Holter monitoring and echocardiography significantly increased the diagnostic yield with 18 and 13% points at baseline and latest follow-up, respectively. Despite surveillance 35 patients (18%) died during follow-up; seven due to SCD. CONCLUSIONS: In patients with DM1 cardiac involvement was highly prevalent and developed during follow-up. These findings justify lifelong follow-up with ECG, Holter, and echocardiography. CLINICAL PERSPECTIVE: What is new? What are the clinical implications?

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Comprehensive evaluation and performance analysis of machine learning in heart disease prediction.

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes.

Clinical suspicion, diagnosis and management of cardiac amyloidosis: update document and executive summary.

In recent years, the interest in cardiac amyloidosis has grown exponentially. However, there is a need to improve our understanding of amyloidosis in order to optimise early detection systems. Therefore, it is crucial to incorporate solutions to improve the suspicion, diagnosis and follow-up of cardiac amyloidosis. In this sense, we designed a tool following the different phases to reach the diagnosis of cardiac amyloidosis, as well as an optimal follow-up: a) clinical suspicion, where the importance of the "red flags" to suspect it and activate the diagnostic process is highlighted; 2) diagnosis, where the diagnostic algorithm is mainly outlined; and 3) follow-up of confirmed patients. This is a practical resource that will be of great use to all professionals caring for patients with suspected or confirmed cardiac amyloidosis, to improve its early detection, as well as to optimise its accurate diagnosis and optimal follow-up.

Non-Coding Ribonucleic Acids as Diagnostic and Therapeutic Targets in Cardiac Fibrosis.

PURPOSE OF REVIEW: Cardiac fibrosis is a crucial juncture following cardiac injury and a precursor for many clinical heart disease manifestations. Epigenetic modulators, particularly non-coding RNAs (ncRNAs), are gaining prominence as diagnostic and therapeutic tools. RECENT FINDINGS: miRNAs are short linear RNA molecules involved in post-transcriptional regulation; lncRNAs and circRNAs are RNA sequences greater than 200 nucleotides that also play roles in regulating gene expression through a variety of mechanisms including miRNA sponging, direct interaction with mRNA, providing protein scaffolding, and encoding their own products. NcRNAs have the capacity to regulate one another and form sophisticated regulatory networks. The individual roles and disease relevance of miRNAs, lncRNAs, and circRNAs to cardiac fibrosis have been increasingly well described, though the complexity of their interrelationships, regulatory dynamics, and context-specific roles needs further elucidation. This review provides an overview of select ncRNAs relevant in cardiac fibrosis as a surrogate for many cardiac disease states with a focus on crosstalk and regulatory networks, variable actions among different disease states, and the clinical implications thereof. Further, the clinical feasibility of diagnostic and therapeutic applications as well as the strategies underway to advance ncRNA theranostics is explored.

Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases.

BACKGROUND: Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team. METHODS: All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion. RESULTS: Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT. CONCLUSIONS: The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Exertional breathlessness related to medical conditions in middle-aged people: the population-based SCAPIS study of more than 25,000 men and women.

BACKGROUND: Breathlessness is common in the population and can be related to a range of medical conditions. We aimed to evaluate the burden of breathlessness related to different medical conditions in a middle-aged population. METHODS: Cross-sectional analysis of the population-based Swedish CArdioPulmonary bioImage Study of adults aged 50-64 years. Breathlessness (modified Medical Research Council [mMRC] ≥ 2) was evaluated in relation to self-reported symptoms, stress, depression; physician-diagnosed conditions; measured body mass index (BMI), spirometry, venous haemoglobin concentration, coronary artery calcification and stenosis [computer tomography (CT) angiography], and pulmonary emphysema (high-resolution CT). For each condition, the prevalence and breathlessness population attributable fraction (PAF) were calculated, overall and by sex, smoking history, and presence/absence of self-reported cardiorespiratory disease. RESULTS: We included 25,948 people aged 57.5 ± [SD] 4.4; 51% women; 37% former and 12% current smokers; 43% overweight (BMI 25.0-29.9), 21% obese (BMI ≥ 30); 25% with respiratory disease, 14% depression, 9% cardiac disease, and 3% anemia. Breathlessness was present in 3.7%. Medical conditions most strongly related to the breathlessness prevalence were (PAF 95%CI): overweight and obesity (59.6-66.0%), stress (31.6-76.8%), respiratory disease (20.1-37.1%), depression (17.1-26.6%), cardiac disease (6.3-12.7%), anemia (0.8-3.3%), and peripheral arterial disease (0.3-0.8%). Stress was the main factor in women and current smokers. CONCLUSION: Breathlessness mainly relates to overweight/obesity and stress and to a lesser extent to comorbidities like respiratory, depressive, and cardiac disorders among middle-aged people in a high-income setting-supporting the importance of lifestyle interventions to reduce the burden of breathlessness in the population.

Obstetrical and neonatal outcomes in patients with surgically repaired heart disease.

BACKGROUND: Congenital and acquired heart disease complicate 1% to 4% of pregnancies in the United States. Beyond the risks of the underlying maternal congenital heart disease, cardiac surgery and its sequelae, such as surgical scarring resulting in higher rates of arrhythmias and implanted valves altering anticoagulation status, have potential implications that could affect gestation and delivery. OBJECTIVE: This study aimed to investigate whether history of maternal cardiac surgery is associated with adverse obstetrical or neonatal outcomes compared with patients without a history of cardiac disease or surgery, considered "healthy controls." STUDY DESIGN: This is a secondary analysis of retrospective cohort studies performed at a tertiary care facility in the United States comparing obstetrical outcomes in patients with a history of open cardiac surgery who delivered from January 2007 to December 2018 with healthy controls, who delivered from April 2020 to July 2020. There were 74 pregnancies in 61 patients with a history of open cardiac surgery that were compared with pregnancies in healthy controls. Of the 74 pregnancies, 65 were successfully matched based on gestational age to controls at a 1:3 (case-to-control) ratio. The remainder of cases were matched at a 1:2 or 1:1 ratio; therefore, a total of 219 control pregnancies were included in the analysis. Our primary outcome was the incidence of hypertensive disorders of pregnancy, as well as cesarean delivery, in patients with a history of open cardiac surgery compared with healthy controls. Our secondary outcome was the incidence of low-birthweight neonates in patients with a history of open cardiac surgery compared with healthy controls. RESULTS: Patients with a history of cardiac surgery were not more likely to have any hypertensive disorder diagnosed than healthy controls. Patients with a history of cardiac surgery were more likely to have an operative delivery (P<.0001) but equally likely to have a cesarean delivery (P=.528) compared with healthy controls. Birthweight was not statistically different of 2655±808 g in neonates born to patients with a history of cardiac surgery vs 2844±830 g born to healthy controls (P=.092). CONCLUSION: Patients with a history of cardiac surgery may not be at higher risk of hypertensive disorder diagnosis during pregnancy. Similarly, most patients with a history of cardiac surgery are also likely not at higher risk of cesarean delivery or low-birthweight neonates.

Can we screen for heart disease in children at public health centres? A multicentre observational study of screening for heart disease with a risk of sudden death in children.

Sudden cardiac death in children is a rare event, but of great social significance. Generally, it is related to heart disease with a risk of sudden cardiac death (SCD), which may occur with cardiovascular symptoms and/or electrocardiographic markers; thus, a primary care paediatrician (PCP) could detect them. Therefore, we proposed a study that assesses how to put into practice and conduct a cardiovascular assessment within the routine healthy-child check-ups at six and twelve years of age; that reflects cardiovascular signs and symptoms, as well as the electrocardiographic alterations that children with a risk of SCD in the selected population present; and that assesses the PCP's skill at electrocardiogram (ECG) interpretation. In collaboration with PCPs, primary care nurses, and paediatric cardiologists, an observational, descriptive, multicentre, cross-sectional study was carried out in the Balearic Islands (Spain), from April 2021 to January 2022, inclusive. The PCPs gathered patient data through forms (medical record, electrocardiogram, and physical examination) and sent them to the investigator, together with the informed consent document and electrocardiogram. The investigator passed the electrocardiogram on to the paediatric cardiologists for reading, in an identical form to those the paediatricians had filled in. The variables were collected, and a descriptive analysis performed. Three paediatric cardiologists, twelve PCPs, and nine nurses from seven public health centres took part. They collected the data from 641 patients, but 233 patients did not participate (in 81.11% due to the PCP's workload). Therefore, the study coverage was around 64%, representing the quotient of the total number of patients who participated, divided by the total number of patients who were eligible for the study. We detected 30 patients with electrocardiographic alterations compatible with SCD risk. Nine of these had been examined by a paediatric cardiologist at some time (functional murmur in 8/9), five had reported shortness of breath with exercise, and four had reported a family history of sudden death. The physical examination of all the patients whose ECG was compatible with a risk of SCD was normal. Upon analysing to what extent the ECG results of the PCP and the paediatric cardiologist agreed, the percentage of agreement in the final interpretation (normal/altered) was 91.9%, while Cohen's kappa coefficient was 31.2% (CI 95%: 13.8-48.6%). The sensitivity of the ECG interpretation by the PCP to detect an ECG compatible with a risk of SCD was 29% and the positive predictive value 45%.     Conclusions: This study lays the foundations for future SCD risk screening in children, performed by PCPs. However, previously, it would be important to optimise their training in reading and interpreting paediatric ECGs. What is Known: • In Spain at present, there is a programme in place to detect heart disease with a risk of sudden death [1], but it targets only children who are starting on or are doing a physical activity as a federated sport. Implementing such screening programmes has proven effective in several countries [2]. However, several studies showed that the incidence of sudden cardiac death is no higher in children competing in sport activities than in those who do not do any sport [3]. This poses an ethical conflict, because at present, children who do not do any federated sport are excluded from screening. According to the revised literature, so far, only in two studies did they screen the child population at schools, and in both, they successfully detected patients with heart disease associated to the risk of sudden death [4, 5]. We have found no studies where the screening of these features was included within the routine healthy-child check-ups by primary care paediatricians. What is New: • We did not know whether-in our setting, at present-the primary care paediatrician could perform a screening method within the routine healthy-child check-ups, in order to detect presumably healthy children at risk of sudden cardiac death, as they present one of the SCD risks. In this regard, we proposed our project: to assess how to put into practice and conduct a cardiovascular assessment via SCD risk screening in the healthy child population by primary care paediatricians and appraise primary care paediatricians' skills in identifying the electrocardiographic alterations associated with SCD risk. The ultimate intention of this pilot study was to make it possible, in the future, to design and justify a study aimed at universalising cardiovascular screening and achieving a long-term decrease in sudden cardiac death events in children.

Artificial intelligence framework for heart disease classification from audio signals.

As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model's performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

Enhancing heart disease prediction using a self-attention-based transformer model.

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Cardiac involvement and its clinical significance in patients with anorexia nervosa.

Cardiac complications are a major concern in patients with anorexia nervosa (AN) which contribute to morbidity and mortality. However, limited information exists regarding risk factors for the development of these complications. Our objective was to investigate the prevalence and associated risk factors of cardiac involvement among children and adolescents with AN admitted to a tertiary pediatric hospital. We collected demographic, clinical, and laboratory data from individuals with AN hospitalized between 2011 and 2020 in Schneider Children's Medical Center in Israel. Diagnosis was based on established criteria (DSM-5). Patients with other co-morbidities were excluded. Cardiac investigations included electrocardiograms (ECG) and echocardiograms. We conducted correlation tests between cardiac findings and clinical and laboratory indicators. A total of 403 AN patients (81.4% were females) with a median age of 15 ± 2 years were included in the study. Sinus bradycardia was the most common abnormality, observed in 155 (38%) participants. Echocardiogram was performed in 170 (42.2%) patients, of whom 37 (22%) demonstrated mild cardiac aberrations. Among those aberrations, 94.6% could be attributed to the current metabolic state, including pericardial effusion (15.3%) and valve dysfunction (8.8%). Systolic or diastolic cardiac dysfunction, tachyarrhythmias, or conduction disorders were not observed. Patients with new echocardiographic aberration had significantly lower body mass index (BMI) at admission, and the prevalence of amenorrhea and hypotension was higher in this group. CONCLUSIONS: The prevalence of cardiac involvement, except for sinus bradycardia, was notably low in our cohort. The presence of cardiac aberrations is correlated with several clinical variables: lower body mass index (BMI) and the presence of amenorrhea and hypotension at admission. Patients presenting with these variables may be at high risk for cardiac findings per echocardiography. Dividing the patients into high and low risk groups may enable targeted evaluation, while avoiding unnecessary cardiac investigations in low-risk patients. WHAT IS KNOWN: • Cardiac involvement in anorexia nervosa (AN) patients is a major concern, which contributes to morbidity and mortality. • It is unknown which patients are prone to develop this complication. WHAT IS NEW: • Cardiac complications in our cohort are less frequent compared to previous studies, and it is correlated with lower body mass index (BMI) at admission, and the prevalence of amenorrhea and hypotension.

Assessment of anthracycline-induced cardiotoxicity in childhood cancer survivors during long-term follow-up using strain analysis and intraventricular pressure gradient measurements.

BACKGROUND: Cardiac dysfunction due to cardiotoxicity from anthracycline chemotherapy is a leading cause of morbidity and mortality in childhood cancer survivors (CCS), and the cumulative incidence of cardiac events has continued to increase. This study identifies an adequate indicator of cardiac dysfunction during long-term follow-up. PROCEDURE: In total, 116 patients (median age: 15.5 [range: 4.7-40.2] years) with childhood cancer who were treated with anthracycline were divided into three age groups for analysis (C1: 4-12 years of age, C2: 13-18 years of age, C3: 19-40 years of age), and 116 control patients of similar ages were divided into three corresponding groups (N1, N2, and N3). Layer-specific strains were assessed for longitudinal strain (LS) and circumferential strain (CS). The total and segmental intraventricular pressure gradients (IVPG) were also calculated based on Doppler imaging of the mitral inflow using Euler's equation. RESULTS: Conventional echocardiographic parameters were not significantly different between the patients and controls. All layers of the LS and inner and middle layers of the basal and papillary CS in all ages and all IVPGs in C2 and C3 decreased compared to those of corresponding age groups. Interestingly, basal CS and basal IVPG in CCS showed moderate correlation and both tended to rapidly decrease with aging. Furthermore, basal IVPG and anthracycline dose showed significant correlations. CONCLUSIONS: Basal CS and total and basal IVPGs may be particularly useful indicators of cardiotoxicity in long-term follow-up.