In recent years, serum uric acid (SUA) as a determinant of cardiovascular (CV) risk has gained interest. Epidemiological, experimental and clinical data show that patients with hyperuricaemia SUA are at increased risk of cardiac, renal and vascular damage and CV events. There is now some evidence to suggest that urate-lowering treatment may reduce CV risk in this group and, thus, may represent a new strategy in risk reduction.

Objective: To assess the prognostic cut-off values of serum uric acid (SUA) in predicting fatal and morbid heart failure in a large Italian cohort in the frame of the Working Group on Uric Acid and Cardiovascular Risk of the Italian Society of Hypertension. Methods: The URic acid Right for heArt Health (URRAH) study is a nationwide, multicentre, cohort study involving data on individuals aged 18–95 years, recruited on a community basis from all regions of Italy under the patronage of the Italian Society of Hypertension with a mean follow-up period of 128 ± 65 months. Incident heart failure was defined on the basis of International Classification of Diseases Tenth Revision codes and double-checked with general practitioners and hospital files. Multivariate Cox regression models having fatal and morbid heart failure as dependent variables, adjusted for sex, age, SBP, diabetes, estimated glomerular filtration rate, smoking habit, ethanol intake, BMI, haematocrit, LDL cholesterol, previous diagnosis of heart failure and use of diuretics as possible confounders, were used to search for an association between SUA as a continuous variable and heart failure. By means of receiver operating characteristic curves, two prognostic cut-off values (one for all heart failure and one for fatal heart failure) were identified as able to discriminate between individuals doomed to develop the event. These cut-off values were used as independent predictors to divide individuals according to prognostic cut-off values in a multivariate Cox models, adjusted for confounders. Results: A total of 21 386 individuals were included in the analysis. In Cox analyses, SUA as a continuous variable was a significant predictor of all [hazard ratio 1.29 (1.23–1.359), P < 0.0001] and fatal [hazard ratio 1.268 (1.121–1.35), P < 0.0001] incident heart failure. Cut-off values of SUA able to discriminate all and fatal heart failure status were identified by mean of receiver operating characteristic curves in the whole database: SUA more than 5.34 mg/dl (confidence interval 4.37–5.6, sensitivity 52.32, specificity 63.96, P < 0.0001) was the univariate prognostic cut-off value for all heart failure, whereas SUA more than 4.89 mg/dl (confidence interval 4.78–5.78, sensitivity 68.29, specificity 49.11, P < 0.0001) for fatal heart failure. The cut-off for all heart failure and the cut-off value for fatal heart failure were accepted as independent predictors in the Cox analysis models, the hazard ratios being 1.645 (1.284–2.109, P < 0.0001) for all heart failure and 1.645 (1.284–2.109, P < 0.0001) for fatal heart failure, respectively. Conclusion: The results of the current study confirm that SUA is an independent risk factor for all heart failure and fatal heart failure, after adjusting for potential confounding variables and demonstrate that a prognostic cut-off value can be identified for all heart failure (>5.34 mg/dl) and for fatal heart failure (>4.89 mg/dl).

The global rate of intensive care unit (ICU) admission during the COVID-19 pandemic varies within countries and is among the main challenges for health care systems worldwide. Conflicting results have been reported about the response to coronavirus infection and COVID-19 outcomes in men and women. Understanding predictors of intensive care unit admission might be of help for future planning and management of the disease. We designed a cross-sectional observational multicenter nationwide survey in Italy to understand gender-related clinical predictors of ICU admission in patients with COVID-19. We analyzed information from 2378 charts of Italian patients certified for COVID-19 admitted in 26 hospitals. Three hundred ninety-five patients (16.6%) required ICU admission due to COVID19 infection, more frequently men (74%), with a higher prevalence of comorbidities (1,78±0,06 vs 1,54±0,03 p<0.05). In multivariable regression model main predictors of admission to ICU are male gender (OR 1,74 95% CI 1,36–2,22 p<0.0001) and presence of obesity (OR 2,88 95% CI 2,03–4,07 p<0.0001), chronic kidney disease (OR: 1,588; 95%, 1,036–2,434 p<0,05) and hypertension (OR: 1,314; 95% 1,039–1,662; p<0,05). In gender specific analysis, obesity, chronic kidney disease and hypertension are associated with higher rate of admission to ICU among men, whereas in women, obesity (OR: 2,564; 95% CI 1,336–4.920 p<0.0001) and heart failure (OR: 1,775 95% CI: 1,030–3,057) are associated with higher rate of ICU admission. Our study demonstrates that gender is the primary determinant of the disease’s severity among COVID-19. Obesity is the condition more often observed among those admitted to ICU within both genders. Clinicaltrials.gov: NCT04331574.

Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity – 0.629 specificity) compared with medical protocols (0.906 sensitivity – 0.230 specificity). Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models.