Cancer Risk in the Heart Failure Population: Epidemiology, Mechanisms, and Clinical Implications.

PURPOSE OF REVIEW: Along with population aging, the incidence of both heart failure (HF) and cancer is increasing. However, little is known about new-onset cancer in HF patients. This review aims at showing recent discoveries concerning this subset of patients. RECENT FINDINGS: Not only cancer and HF share similar risk factors but also HF itself can stimulate cancer development. Some cytokines produced by the failing heart induce mild inflammation promoting carcinogenesis, as it has been recently suggested by an experimental model of HF in mice. The incidence of new-onset cancer is higher in HF patients compared to the general population, and it significantly worsens their prognosis. Moreover, the management of HF patients developing new-onset cancer is challenging, especially due to the limited therapeutic options for patients affected by both cancer and HF and the higher risk of cardiotoxicity from anticancer drugs.

Current applications of big data and machine learning in cardiology.

Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.

Time trend prevalence of artificial nutrition counselling in a university hospital.

OBJECTIVES: The negative effects of malnutrition on the prognosis of hospitalized patients are well documented; however, less known is the awareness and knowledge of health care professionals about this complication. The aim of this study was to evaluate the trend of the requests for nutritional consultation in years and the prescription of artificial nutrition (AN), for adult patients at a university hospital in southern Italy in the years 2004, 2008, 2012, and 2016 to assess the progress of medical teams concerning awareness of hospital malnutrition. METHODS: This was a retrospective study that evaluated the time trend of nutritional consultation requests and related prescription of AN, for adult patients at a university hospital in southern Italy in the years 2004, 2008, 2012, and 2016. Of 112 233 inpatients, 2505 received a nutritional consultation with the prescription of AN. RESULTS: The number of patients on AN increased from 507 of 33 240 (1.52%) in 2004 to 730 of 29 195 (2.5%) in 2008 (P < 0.001), remaining almost stable in 2012 and 2016. The request for AN was quite equally distributed between surgical (51.5%) and medical wards (48.5%), with a prevalence among patients with oncologic diseases (806 patients [65.6%]). As for nononcologic diseases, 20.4% involved the gastrointestinal tract and 6.3% the nervous system. Throughout the 12 y of observation, parenteral nutrition was the main prescribed support (59.8%) followed by oral nutritional supplements (26.1%) and enteral nutrition (9.3%). Mean nutritional intervention duration was 11 d (±10.8 d). CONCLUSIONS: The request of AN for hospitalized patients increased over time, probably owing to improved medical consciousness of the potential risks for malnutrition and the availability of a specialized clinical nutrition team.

Preliminary evaluation of the prevalence of sarcopenia in obese patients from Southern Italy.

OBJECTIVES: Sarcopenic obesity has not yet been widely defined. The aim of this study was to evaluate the prevalence of sarcopenia in a group of severely obese adults from southern Italy by using two different indexes: percentage of skeletal muscle mass (SMP) and skeletal muscle mass normalized for height (SMI); and to determine SMP and SMI cutoff points in a southern Italy reference population. METHODS: Skeletal muscle mass of 131 consecutive obese adult outpatients (51 men and 80 women; ages 45-67 y; body mass index 44.6 ± 7.7 kg/m(2)), was assessed by bioimpedance analysis. SMP and SMI cutoff points to identify moderate and severe sarcopenia were calculated in a reference group of 500 young southern Italy adults (100 men and 400 women; ages 18-40 y; body mass index 25.2 ± 5.6 kg/m(2)) and applied to assess the prevalence of sarcopenia in the study population. RESULTS: SMP cutoff points to identify moderate and severe sarcopenia were, 28.8% to 35.6% and ≤ 28.7% in men and 23.1% to 28.4% and ≤ 23% in women, respectively. The corresponding values for SMI were 8.44 to 9.53 kg/m(2) and ≤ 8.43 kg/m(2) in men, 6.49 to 7.32 kg/m(2) and ≤ 6.48 kg/m(2) in women. According to SMP, 23 of 51 (45.1%) men and 19 of 80 (23.8%) women were moderately sarcopenic; 28 of 51 (54.9%) men and 61 of 80 (76.3%) women met the definition of severe sarcopenia. Based on SMI, only 2 of 51 (3.9%) men were moderately sarcopenic. CONCLUSIONS: This study confirms that sarcopenia rates vary widely in obese patients depending on the criteria used. SMP as a screening tool to identify a sarcopenia at-risk population.