Machine learning-based detection of sleep-disordered breathing in hypertrophic cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often concomitant with sleep-disordered breathing (SDB), which can cause adverse cardiovascular events. Although an appropriate approach to SDB prevents cardiac remodelling, detection of concomitant SDB in patients with HCM remains suboptimal. Thus, we aimed to develop a machine learning-based discriminant model for SDB in HCM. METHODS: In the present multicentre study, we consecutively registered patients with HCM and performed nocturnal oximetry. The outcome was a high Oxygen Desaturation Index (ODI), defined as 3% ODI >10, which significantly correlated with the presence of moderate or severe SDB. We randomly divided the whole participants into a training set (80%) and a test set (20%). With data from the training set, we developed a random forest discriminant model for high ODI based on clinical parameters. We tested the ability of the discriminant model on the test set and compared it with a previous logistic regression model for distinguishing SDB in patients with HCM. RESULTS: Among 369 patients with HCM, 228 (61.8%) had high ODI. In the test set, the area under the receiver operating characteristic curve of the discriminant model was 0.86 (95% CI 0.77 to 0.94). The sensitivity was 0.91 (95% CI 0.79 to 0.98) and specificity was 0.68 (95% CI 0.48 to 0.84). When the test set was divided into low-probability and high-probability groups, the high-probability group had a higher prevalence of high ODI than the low-probability group (82.4% vs 17.4%, OR 20.9 (95% CI 5.3 to 105.8), Fisher's exact test p<0.001). The discriminant model significantly outperformed the previous logistic regression model (DeLong test p=0.03). CONCLUSIONS: Our study serves as the first to develop a machine learning-based discriminant model for the concomitance of SDB in patients with HCM. The discriminant model may facilitate cost-effective screening tests and treatments for SDB in the population with HCM.

Model for classification of heart failure severity in patients with hypertrophic cardiomyopathy using a deep neural network algorithm with a 12-lead electrocardiogram.

OBJECTIVES: In hypertrophic cardiomyopathy (HCM), specific ECG abnormalities are observed. Therefore, ECG is a valuable screening tool. Although several studies have reported on estimating the risk of developing fatal arrhythmias from ECG findings, the use of ECG to identify the severity of heart failure (HF) by applying deep learning (DL) methods has not been established. METHODS: We assessed whether data-driven machine-learning methods could effectively identify the severity of HF in patients with HCM. A residual neural network-based model was developed using 12-lead ECG data from 218 patients with HCM and 245 patients with non-HCM, categorised them into two (mild-to-moderate and severe) or three (mild, moderate and severe) severities of HF. These severities were defined according to the New York Heart Association functional class and levels of the N-terminal prohormone of brain natriuretic peptide. In addition, the patients were divided into groups according to Kansas City Cardiomyopathy Questionnaire (KCCQ)-12. A transfer learning method was applied to resolve the issue of the low number of target samples. The model was trained in advance using PTB-XL, which is an open ECG dataset. RESULTS: The model trained with our dataset achieved a weighted average F1 score of 0.745 and precision of 0.750 for the mild-to-moderate class samples. Similar results were obtained for grouping based on KCCQ-12. Through data analyses using the Guided Gradient Weighted-Class Activation Map and Integrated Gradients, QRS waves were intensively highlighted among true-positive mild-to-moderate class cases, while the highlighted part was highly variable among true-positive severe class cases. CONCLUSIONS: We developed a model for classifying HF severity in patients with HCM using a deep neural network algorithm with 12-lead ECG data. Our findings suggest that applications of this DL algorithm for using 12-lead ECG data may be useful to classify the HF status in patients with HCM.

Association of Malnutrition and High Bleeding Risk with Long-Term Prognosis in Patients with Acute Coronary Syndrome following Percutaneous Coronary Intervention.

BACKGROUND: Malnutrition in cardiovascular disease is associated with poor prognosis, especially in patients with heart failure and acute coronary syndrome (ACS). High bleeding risk is also linked to coronary artery disease prognosis, including ACS. However, whether the extent of malnutrition and high bleeding risk have a cumulative impact on the long-term prognosis of patients with ACS who undergo percutaneous coronary intervention remains unclear. METHODS: We analyzed 275 patients with ACS treated with percutaneous coronary intervention. The Controlling Nutritional Status score and Japanese version of the Academic Research Consortium for High Bleeding Risk criteria (J-HBR) were retrospectively evaluated. The primary and secondary outcomes were adjusted using the inverse probability treatment weighting method. RESULTS: The prevalence of moderate or severe malnutrition in this cohort was 16%. Kaplan-Meier analysis showed a significantly higher incidence of major adverse cardiovascular and cerebrovascular events in patients who were moderately or severely malnourished than in those who were not. Notably, the incidence of these major events was similar between severely malnourished patients with J-HBR and those without. CONCLUSION: Moderate or severe malnutrition has a significant impact on the long-term prognosis of patients with ACS who undergo percutaneous coronary intervention.

Elacridar enhances the cytotoxic effects of sunitinib and prevents multidrug resistance in renal carcinoma cells.

Intrinsic drug resistance occurs in many renal carcinomas and is associated with increased expression of multidrug resistant proteins, which inhibits intracellular drug accumulation. Multidrug resistant protein 1, also known as P-glycoprotein, is a membrane drug efflux pump belonging to the ATP-binding cassette (ABC) transporter superfamily. ABC Sub-family B Member 2 (ABCG2) is widely distributed and is involved in the multidrug resistant phenotype. Sunitinib is a tyrosine kinase inhibitor used to treat kidney cancer that disrupts signaling pathways responsible for abnormal cancer cell proliferation and tumor angiogenesis. Multiple drug resistance is important in tyrosine kinase inhibitor-induced resistance. We hypothesized that inhibition of multidrug resistant transporters by elacridar (dual inhibitor of P-glycoprotein and ABCG 2) might overcome sunitinib resistance in experimental renal cell carcinoma. Human renal carcinoma cell lines 786-O, ACHN, and Caki-1 were treated with sunitinib or elacridar alone, or in combination. We showed that elacridar significantly enhanced sunitinib cytotoxicity in 786-O cells. P-glycoprotein activity, confirmed by P-glycoprotein function assay, was found to be inhibited by elacridar. ABCG2 expression was low in all renal carcinoma cell lines, and was suppressed only by combination treatment in 786-O cells. ABCG2 function was inhibited by sunitinib alone or combination with elacridar but not elacridar alone. These findings suggest that sunitinib resistance involves multidrug resistance transporters, and in combination with elacridar, can be reversed in renal carcinoma cells by P-glycoprotein inhibition.