Quality of life and adherence to therapy in patients with chronic heart failure who were remotely monitored by chatbot compared to the standard follow-up group for 3 months

BACKGROUND: Chronic heart failure (CHF) is one of the leading causes of death. Telemedicine and remote monitoring (RM) are a way to increase life expectancy and quality of life in patients with CHF. Methods based on messengers familiar to patients promote adherence and do not require additional training. AIM: To compare quality of life and adherence to therapy in patients with CHF who were on RM using a chatbot compared to the standard follow-up (SFU) group for 3 months. METHODS: Patients with CHF on optimal drug therapy discharged from the hospital were included in the study. Comparison groups were formed according to the method of observation, particularly, RM and SFU. A chatbot was set up for patients in the RM group. Monitoring was done using a seven-question survey sent daily. The signs of decompensation (red flags [RF]) were increased edema, dyspnea, body weight 2 kg per week, and changes in individual parameters of heart rate and blood pressure. If a RF was detected, telephone contact was made, and the therapy was corrected if necessary. Quality of life was assessed according to the Minnesota Quality of Life Questionnaire for patients with CHF (highest, 0 points; lowest, 105 points), and adherence was assessed using the Adherence Scale of the National Society for Evidence-based Pharmacotherapy. RESULTS: A total of 60 patients were included in the study; 37 patients completed a 3-month follow-up. The RM group (n=17, 13 men, 76.5%; median age 61 [51; 62]) and comparison group (n=20, 14 men, 70%; mean age 64.98.9) were comparable according to the functional class (New York Heart Association), but differed in ejection fraction (42.813% versus 53.210.4% [p 0.05]). Adherence to the chat-bot was 67.2%. Adherence to therapy was not significantly different between the RM and SFU groups accounting for (17 [100%]) and (18 [90%], respectively, (p=0.62). In the RM group, RF was detected in 7 (41%) patients. Only one patient required correction of therapy. Patients in the RM group required no referral to a medical facility, whereas 2 patients in the SFU group required medical care. Quality of life was statistically significantly higher in the RM group, reaching 28.713.9 points compared to 37.717.9 points in the SFU group (p=0.04). CONCLUSIONS: After 3 months, patients in the RM group were committed to the chatbot, with adherence to therapy comparable to the SFU group. Quality of life was statistically significantly higher in the RM group. Patients in the RM group did not go to medical facilities, in contrast to the SFU group. The limitations of the study were the small sample size and short follow-up period. The results require further research to obtain additional data

Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults

Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p< 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling