The role of patient-oriented mHealth interventions in improving heart failure outcomes: A systematic review of the literature.

Heart failure (HF) is a debilitating disease with 26 million patients worldwide. Consistent and complex self-care is required on the part of patients to adequately adhere to medication and to the lifestyle changes that the disease necessitates. Mobile health (mHealth) is being increasingly incorporated in patient interventions in HF, as smartphones prove to be ideal platforms for patient education and self-help assistance. This systematic review aims to summarize and report on all studies that have tested the effect of mHealth on HF patient outcomes. Our search yielded 17 studies, namely 11 randomized controlled trials and six non-randomized prospective studies. In these, patients with the assistance of an mHealth intervention regularly measured their blood pressure and/or body weight and assessed their symptoms. The outcomes were mostly related to hospitalizations, clinical biomarkers, patients' knowledge about HF, quality of life (QoL) and quality of self-care. QoL consistently increased in patients who received mHealth interventions, while study results on all other outcomes were not as ubiquitously positive. The first mHealth interventions in HF were not universally successful in improving patient outcomes but provided valuable insights for patient-oriented application development. Future trials are expected to build on these insights and deploy applications that measurably assist HF patients.

Heart Failure and Cardiorenal Syndrome: A Narrative Review on Pathophysiology, Diagnostic and Therapeutic Regimens-From a Cardiologist's View.

In cardiorenal syndrome (CRS), heart failure and renal failure are pathophysiologically closely intertwined by the reciprocal relationship between cardiac and renal injury. Type 1 CRS is most common and associated with acute heart failure. A preexistent chronic kidney disease (CKD) is common and contributes to acute kidney injury (AKI) in CRS type 1 patients (acute cardiorenal syndrome). The remaining CRS types are found in patients with chronic heart failure (type 2), acute and chronic kidney diseases (types 3 and 4), and systemic diseases that affect both the heart and the kidney (type 5). Establishing the diagnosis of CRS requires various tools based on the type of CRS, including non-invasive imaging modalities such as TTE, CT, and MRI, adjuvant volume measurement techniques, invasive hemodynamic monitoring, and biomarkers. Albuminuria and Cystatin C (CysC) are biomarkers of glomerular filtration and integrity in CRS and have a prognostic impact. Comprehensive "all-in-one" magnetic resonance imaging (MRI) approaches, including cardiac magnetic resonance imaging (CMR) combined with functional MRI of the kidneys and with brain MRI are proposed for CRS. Hospitalizations due to CRS and mortality are high. Timely diagnosis and initiation of effective adequate therapy, as well as multidisciplinary care, are pertinent for the improvement of quality of life and survival. In addition to the standard pharmacological heart failure medication, including SGLT2 inhibitors (SGLT2i), renal aspects must be strongly considered in the context of CRS, including control of the volume overload (diuretics) with special caution on diuretic resistance. Devices involved in the improvement of myocardial function (e.g., cardiac resynchronization treatment in left bundle branch block, mechanical circulatory support in advanced heart failure) have also shown beneficial effects on renal function.

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence (AI) is an evolving cluster of interrelated fields, with machine learning (ML) standing out as one of the most prominent AI fields, with a plethora of applications in almost every aspect of human life. ML could be defined as computer algorithms that learn from past data to predict future data. This review comprehensively reviews the current applications of microRNA-based ML models in healthcare. The majority of the identified studies investigated the role of microRNA-based ML models in the management of cancer and specifically gastric cancer (maximum diagnostic accuracy (Accmax): 94%), pancreatic cancer (Accmax: 93%), colorectal cancer (Accmax: 100%), breast cancer (Accmax: 97%), ovarian cancer, neck squamous cell carcinoma, liver cancer, lung cancer (Accmax: 100%), and melanoma. Except for cancer, microRNA-based ML models have been applied for a plethora of other diseases, including ulcerative colitis (Accmax: 92.8%), endometriosis, gestational diabetes mellitus (Accmax: 86%), hearing loss, ischemic stroke, coronary heart disease (Accmax: 96%), tuberculosis, pulmonary arterial hypertension (Accmax: 83%), dementia (Accmax: 82.9%), major cardiovascular events in end-stage renal disease patients, and alcohol dependence (Accmax: 79.1%). Our findings suggest that the development of microRNA-based ML models could be used to enhance the diagnostic accuracy of a plethora of diseases while at the same time substituting or minimizing the use of more invasive diagnostic means (such as endoscopy). Even not as fast as anticipated, AI will eventually infiltrate the entire healthcare industry. AI is the key to a clinical practice where medicine's inherent complexity is embraced. Therefore, AI will become a reality that physicians should conform with to avoid becoming obsolete.