Relationship Between Blood Pressure and Heart Rate Circadian Rhythms in Normotensive and Hypertensive Subjects

This paper focuses on the relationship between blood pressure (BP) and heart rate (HR) during 24 hours in 423 normotensive (NO) and 205 hypertensive (HE) subjects. Although considerable knowledge has been gained about BP and HR signals, their relationship over 24 hours has never been completely described. By using a Holter Blood Pressure Monitor, it was possible to record BP and HR for 24 hours. Systolic, Diastolic and Mean BP in both NO and HE subjects showed four different time intervals presenting well-defined trends The results demonstrated that changes in HR present closely parallel changes in BP with a marked reduction of both signals during nocturnal rest. On the contrary, in the period between 15:30 and 19:30, HR and BP showed an inverse relationship with decreasing heart rate and increasing blood pressure

Influence of hypertension and other risk factors on the onset of sublingual varices

Background: Sublingual varices (SV) are dilatations of tortuous veins that increased with age. Previous studies showed that this pathology could be correlated to some risk factors such as hypertension, age, gender and diabetes mellitus. In this study we evaluated, on a large number of subjects, the relationship between SV and different grades of hypertension as well as some risk factors extending the analysis to new risk factors such as dyslipidemia, obesity and antihypertensive therapy, modelling a possible dependence of SV on all these factors.Methods: In the study 1008 subjects, 284 with and 724 without SV, were examined. The blood pressure was measured in office condition and, to exclude subjects with white coat syndrome or masked hypertension, also using a 24 h Holter pressure monitor. Hypertensive subjects were divided in resistant, drugs controlled (compensated) and patients with prior unknown hypertension (new diagnosed) groups. The presence or absence of SV as well as of the risk factors was assessed clinically. We tested the influence of age on the presence of SV by using the chi-square test and the relation between each risk factor and SV by the Cochran-Mantel-Haenszel test. Finally, we carried out a multivariate regression tree analysis in order to predict the presence of SV.Results: We confirmed the influence of age on SV and found a significant relationship between SV and both the compensated and resistant hypertension grades. We highlighted a relationship between SV and dyslipidemia in subjects with new diagnosed hypertension, and between SV and smoking in subjects with compensated hypertension grade. The regression tree showed a classification accuracy of about 75% using as variables hypertension grades, age and antihypertensive treatment.Conclusions: We confirmed the SV dependence on age, resistant hypertension and smoking, highlighting a new association with dyslipidemia in new diagnosed hypertensive subjects and new relations depending on the hypertension grades. Thus, the SV inspection could be used to suggest a lipidologist as well as a hypertension specialist visit for a pharmacological and pressure check particularly in subjects presenting SV and dyslipidemia. However, further parameters are to be considered to improve the sensitivity of the prognostic tree model

Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE: Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.APPROACH: To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.MAIN RESULTS: The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.SIGNIFICANCE: The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process

Whole body MRI with diffusion weighted imaging versus 18F‑fuorodeoxyglucose‑PET/CT in the staging of lymphomas

Purpose To assess the diagnostic performance of Whole Body (WB)-MRI in comparison with 18F-Fluorodeoxyglucose-PET/CT (18F-FDG-PET/CT) in lymphoma staging and to assess whether quantitative metabolic parameters from 18F-FDG-PET/CT and Apparent Diffusion Coefficient (ADC) values are related. Materials and methods We prospectively enrolled patients with a histologically proven primary nodal lymphoma to undergo 18F-FDG-PET/CT and WB-MRI, both performed within 15 days one from the other, either before starting treatment (baseline) or during treatment (interim). Positive and negative predictive values of WB-MRI for the identification of nodal and extra-nodal disease were measured. The agreement between WB-MRI and 18F-FDG-PET/CT for the identification of lesions and staging was assessed through Cohen's coefficient k and observed agreement. Quantitative parameters of nodal lesions derived from 18F-FDG-PET/CT and WB-MRI (ADC) were measured and the Pearson or Spearman correlation coefficient was used to assess the correlation between them. The specified level of significance was p ≤ 0.05. Results Among the 91 identified patients, 8 refused to participate and 22 met exclusion criteria, thus images from 61 patients (37 men, mean age 30.7 years) were evaluated. The agreement between 18F-FDG-PET/CT and WB-MRI for the identification of nodal and extra-nodal lesions was 0.95 (95% CI 0.92 to 0.98) and 1.00 (95% CI NA), respectively; for staging it was 1.00 (95% CI NA). A strong negative correlation was found between ADCmean and SUVmean of nodal lesions in patients evaluated at baseline (Spearman coefficient rs = − 0.61, p = 0.001). Conclusion WB-MRI has a good diagnostic performance for staging of patients with lymphoma in comparison with 18F-FDG-PET/CT and is a promising technique for the quantitative assessment of disease burden in these patients

Clinical and genetic characteristics of late-onset Huntington's disease in a large European cohort

Background and purpose Huntington's disease (HD) is an autosomal dominant condition caused by CAG-triplet repeat expansions. CAG-triplet repeat expansion is inversely correlated with age of onset in HD and largely determines the clinical features. The aim of this study was to examine the phenotypic and genotypic correlates of late-onset HD (LoHD) and to determine whether LoHD is a more benign expression of HD. Methods This was a retrospective observational study of 5053 White European HD patients from the ENROLL-HD database. Sociodemographic, genetic and phenotypic variables at baseline evaluation of subjects with LoHD, common-onset HD (CoHD) and young-onset HD (YoHD) were compared. LoHD subjects were compared with healthy subjects (HS) aged >= 60 years. Differences between the CoHD and LoHD groups were also explored in subjects with 41 CAG triplets, a repeat number in the lower pathological expansion range associated with wide variability in age at onset. Results Late-onset HD presented predominantly as motor-onset disease, with a lower prevalence of both psychiatric history and current symptomatology. Absent/unknown HD family history was significantly more common in the LoHD group (31.2%) than in the other groups. The LoHD group had more severe motor and cognitive deficits than the HS group. Subjects with LoHD and CoHD with 41 triplets in the larger allele were comparable with regard to cognitive impairment, but those with LoHD had more severe motor disorders, less problematic behaviors and more often an unknown HD family history. Conclusions It is likely that cognitive disorders and motor symptoms of LoHD are at least partly age-related and not a direct expression of the disease. In addition to CAG-triplet repeat expansion, future studies should investigate the role of other genetic and environmental factors in determining age of onset

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Analysis of the circadian rhythm of cardiovascular signals and their prognostic use in decision support systems

The focus of my research activity has been on the processing of cardiovascular signals in order to be able to use them as a support tool for doctors in their clinical decision making. Although the analysis of these cardiovascular signals has mainly been based on the punctual estimation of blood pressure and heart rate parameters, it is well known that the outpatient information, obtained from the 24h ambulatory monitoring, can provide prognostic support. Therefore I have tried to examine in detail how the values of the parameters related to blood pressure and heart rate over 24h change and how the relationship between them varies. Since the cardiovascular risk factors alter the trend of these biological signals, I have performed an analysis of the effects of each single risk factors on the circadian trend of the two signals and their relationship. Since, in recent years, mathematical approaches have been developed for the construction of clinical decision support systems applied, in the cardiovascular field, only to the classification of single heart beats of different etiologies; I have developed decision support systems to identify subjects with or without cardiovascular diseases. The pathologies examined were ischemic heart (IHD) and dilated cardiomyopathy (DCM). The described problems have been addressed using linear and nonlinear methods of signal processing and applying artificial intelligence algorithms. The average circadian trends of pressure and heart rate and their relationship on different categories of subjects were obtained. The linear and non-linear parameters were calculated from the heart rate variability signal and machine learning techniques were developed, the Artificial Neural Network (ANN) and Classification and Regression Tree (CART), applied to the previous parameters in addition to age, gender and to a specific clinical parameter. The results showed that the cardiovascular signals over 24h show a characteristic linear circadian rhythm divisible into four time intervals for the pressure signal (three intervals for the heart rate) in both normotensive and hypertensive subjects highlighting the importance of taking into account the time of day in which the signal is measured. The relationship between these two signals evaluated over 24h could be useful for understanding the control mechanism of the autonomic nervous system. The examination of the effects of risk factors such as smoking, obesity and dyslipidemia on cardiovascular signals showed that each factor modifies the physiological signals. The investigation of the influence of age and gender on cardiovascular signals highlighted an inversion of the trend in linear and non-linear parameters of heart rate variability in subjects>60 years of age and a gender differentiation only during the night. Finally, the results obtained by developing decision support systems based on machine learning techniques applied to various combinations of parameters, selected through principal component analysis, stepwise regression or correlated for less than 90%, showed that the ANN technique identify normal subjects and IHD with an accuracy of 80% and that the CART algorithm classify DCM patients with an accuracy of 97%. The latter technique was also able to distinguish these two etiologies from each other and from normal subjects with an accuracy of 81%. The results of my PhD activity highlight the importance of circadian analysis of cardiovascular signals, suggesting that particular attention should be paid to the time in which the measurements are performed providing useful information for the evaluation of the mechanisms that regulate the physiological control of the examined signals. Furthermore, the use of decision support systems based on machine learning techniques applied to parameters obtained in a non-invasive way from the processing of the heart rate variability is useful for diagnosing various cardiovascular diseases

Novel Classification of Ischemic Heart Disease Using Artificial Neural Network

Ischemic heart disease (IHD), particularly in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. Machine learning techniques applied to parameters extracted form heart rate variability (HRV) signal seem to be a valuable support in the early diagnosis of some cardiac diseases. However, so far, IHD patients were identified using Artificial Neural Networks (ANNs) applied to a limited number of HRV parameters and only to very few subjects. In this study, we used several linear and non-linear HRV parameters applied to ANNs, in order to confirm these results on a large cohort of 965 sample of subjects and to identify which features could discriminate IHD patients with high accuracy. By using principal component analysis and stepwise regression, we reduced the original 17 parameters to five, used as inputs, for a series of ANNs. The highest accuracy of 82% was achieved using meanRR, LFn, SD1, gender and age parameters and two hidden neurons