Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

Self-care behaviors are essential for the effective treatment of heart failure (HF), and poor self-care may lead to adverse clinical events in patients with HF. A growing body of literature addresses the need to analyze the characteristics of both patient and caregiver since they are in mutual, long-term interaction, and their reactions to events are dependent on each other. One of the most common approaches for analyzing data on HF self-care dyads is the Actor–Partner Interdependence Model (APIM). The purpose of this study was to conduct a scoping review to answer the following question: what did we learn from HF dyadic studies based on the APIM approach? Medline, Academic Search Ultimate, and CINAHL Complete databases were searched, using the terms “dyad,” “dyadic,” and “heart failure,” for studies published between 2009 and April 2021. Fifteen studies were reviewed from a pool of 106 papers. Studies using the APIM approach revealed interrelated patient and caregiver characteristics that influence self-care and explain many complex dyadic behaviors. Our analysis provided evidence that (1) APIM is a useful analytical approach; (2) a family-oriented approach can improve the functioning of a patient with HF; and (3) social support from caregivers significantly enhances patients’ adaptation to illness.

Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment. We evaluated data from 381 AHF patients. Sixty-three clinical and biochemical features were assessed at the admission of the patients and were included in the analysis after the preprocessing. The K-medoids algorithm was implemented to create the clusters, and optimization, based on the Davies-Bouldin index, was used. The clustering was performed while blinded to the outcome. The outcome associations were evaluated using the Kaplan-Meier curves and Cox proportional-hazards regressions. The algorithm distinguished six clusters that differed significantly in 58 variables concerning i.e., etiology, clinical status, comorbidities, laboratory parameters and lifestyle factors. The clusters differed in terms of the one-year mortality (p = 0.002). Using the clustering techniques, we extracted six phenotypes from AHF patients with distinct clinical characteristics and outcomes. Our results can be valuable for future trial constructions and customized treatment.

Background Increased peripheral chemosensitivity (PChS) has been proposed as mechanism underlying obesity-related sympathoactivation, with insulin and/or leptin as possible mediators. However, human data on PChS in obesity are scarce. Therefore, we explored this issue in a sample of 41 healthy men aged 30–59 years, divided according to body fat percentage (fat %) into two groups: <25 and ≥25 %. Methods PChS was assessed using transient hypoxia method [respiratory (PChS-MV), heart rate (PChS-HR), and blood pressure (PChS-SBP) responses were calculated]. Baroreflex sensitivity (BRS-Seq) was assessed using sequence method. Fasting plasma insulin and leptin levels were measured. Homeostatic model assessment (HOMA) was used to assess insulin sensitivity/resistance. Results Individuals with ≥25 % body fat demonstrated increased PChS-SBP (p < 0.01), but unchanged PChS-MV and PChS-HR (both p > 0.4). PChS-SBP was related positively with anthropometric characteristics (e.g. waist circumference, fat %), plasma insulin and HOMA (all p < 0.05), and negatively with BRS-Seq (p = 0.001), but not with plasma leptin (p = 0.27). Conclusions In healthy men, overweight/obesity is accompanied by augmented blood pressure response from peripheral chemoreceptors, while respiratory and heart rate responses remain unaltered. Hyperinsulinaemia and insulin resistance (but not hyperleptinaemia) are associated with augmented pressure response from chemoreceptors.

Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment.