Effect of perioperative blood pressure variability on cerebral hyperperfusion syndrome after carotid artery stenting: A retrospective study

Objective To investigate the effect of perioperative blood pressure variability on cerebral hyperperfusion syndrome after carotid artery stenting. Methods A retrospective analysis was conducted of data collected from 418 patients who underwent carotid artery stenting in Guangxi Zhuang Autonomous Region People's Hospital in China. The blood pressure data were collected during operation (after balloon dilation, before stent release, after stent release) and within 3 days after the operation. The blood pressure variability was evaluated by measuring the mean, maximum, minimum, max–min, standard deviation (SD) of systolic blood pressure (SBP) and diastolic blood pressure (DBP). The correlation between blood pressure variability and cerebral hyperperfusion syndrome was analysed. Results Blood pressure data from 418 patients were analysed. Twenty patients (4.8%) developed cerebral hyperperfusion syndrome. The parameters of blood pressure variability were divided into four groups according to quartile. After adjusting for age, symptomatic carotid stenosis, unilateral carotid stenosis, bilateral carotid stenosis, collateral circulation, diabetes mellitus and chronic kidney disease, multivariate analysis showed that SBPMax, SBPMin, SBPMax−Min, SBPCV, DBPSD, DBPMax, DBPMin, DBPMax−Min and DBPCV were associated with the occurrence of cerebral hyperperfusion syndrome ( P < 0.05), respectively. Conclusion This study suggests that blood pressure variability during the perioperative period may increase the risk of cerebral hyperperfusion syndrome.

A geodatabase of blood pressure level and the associated factors including lifestyle, nutritional, air pollution, and urban greenspace

Objectives Hypertension is a prevalent chronic disease globally. A multifaceted combination of risk factors is associated with hypertension. Scientific literature has shown the association among individual and environmental factors with hypertension, however, a comprehensive database including demographic, environmental, individual attributes and nutritional status has been rarely studied. Moreover, an integrated spatial-epidemiological approach has been scarcely researched. Therefore, this study aims to provide and describe a geodatabase including individual-based and socio-environmental data related to people living in the city of Mashhad, Iran in 2018. Data description The database has been extracted from the PERSIAN Organizational Cohort study in Mashhad University of Medical Sciences. The data note includes three shapefiles and a help file. The shapefile format is a digital vector storage format for storing geometric location and associated attribute information. The first shapefile includes the data of population, air pollutants and amount of available green space for each census block of the city. The second shapefile consists of aggregated blood pressure data to the census blocks of the city. The third shapefile comprises the individual characteristics data (i.e., demographic, clinical, and lifestyle). Finally, the fourth file is a guide to the previous data files for users.

Workflow Automation for a Virtual Hypertension Management Program

Objectives Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes. Methods We developed a customer relationship management (CRM) solution at our institution for the purpose of improving processes and workflow for a virtual hypertension management program and describe here the development, implementation, and initial experience of this CRM system. Results Notable system features include task automation, patient data capture, multi-channel communication, integration with our electronic health record (EHR), and device integration (for blood pressure cuffs). In the five stages of our program (intake and eligibility screening, enrollment, device configuration/setup, medication titration, and maintenance), we describe some of the key process improvements and workflow automations that are enabled using our CRM platform, like automatic reminders to capture blood pressure data and present these data to our clinical team when ready for clinical decision making. We also describe key limitations of CRM, like balancing out-of-the-box functionality with development flexibility. Among our first group of referred patients, 76% (39/51) preferred email as their communication method, 26/51 (51%) were able to enroll electronically, and 63% of those enrolled (32/51) were able to transmit blood pressure data without phone support. Conclusion A CRM platform could improve clinical processes through multiple pathways, including workflow automation, multi-channel communication, and device integration. Future work will examine the operational improvements of this health information technology solution as well as assess clinical outcomes.

Novel Clinimetric Toolset to Quantify the Stability of Blood Pressure and Its Application to Evaluate Cardiovascular Function After Spinal Cord Injury

Profound dysfunction of the cardiovascular system occurs after spinal cord injury (SCI), which is a leading cause of mortality in this population. Most individuals with chronic SCI experience transient episodes of hypotensive and hypertensive blood pressure in response to daily life activities. There are currently limited tools available to evaluate the stability of blood pressure with respect to a reference range. The aim of this study was to develop a clinimetric toolset for accurately quantifying stability of the blood pressure measurements and taking into consideration the complex dynamics of blood pressure variability among individuals with SCI. The proposed toolset is based on distribution of the blood pressure data points within and outside of the clinically recommended range. This toolset consists of six outcome measures including 1) total deviation of the 90% of the blood pressure data points from the center of the target range (115 mmHg); 2) The area under the cumulative distribution curve starting from the percentage of blood pressure measurements within the range, and the percentage of values within symmetrically expanded boundary ranges, above and below the target range; 3) the slope of the cumulative distribution curve that is calculated by fitting an exponential cumulative distribution function and the natural logarithm of its rate parameter; 4) its x- and 5) y-axis intercepts; and 6) the fitting error. These outcome measures were validated using blood pressure measurements recorded during cardiovascular perturbation tests and prolonged monitoring period from individuals with chronic SCI and non-injured controls. The statistical analysis based on the effect size and intra-class correlation coefficient, demonstrated that the proposed outcome measures fulfill reliability, responsiveness and discrimination criteria. The novel methodology proposed in this study is reliable and effective for evaluating the stability of continuous blood pressure in individuals with chronic spinal cord injury.

Features Derived From Blood Pressure Predict Elevated Intracranial Pressure in Critically Ill Children

Clinicians frequently observe hemodynamic changes preceding elevated intracranial pressure (ICP) events. We employed a machine learning approach to identify novel and differentially expressed features associated with elevated ICP (eICP) events in children with severe brain injuries. Statistical features from physiologic data streams were derived from non-overlapping 30-minute analysis windows prior to 21 eICP events; 200 records without eICP events were used as controls. Ten Monte Carlo simulations with training/testing splits provided performance benchmarks for 4 machine learning approaches. XGB yielded the best performing predictive models. SHAP analyses demonstrated that a majority of the top 20 contributing features from each simulation consistently derived from blood pressure data streams up to 240 minutes prior to eICP events, rivaling ICP-derived features at 0-60 minutes. Our AUROC benchmark at the 30-60 minutes analysis window using the XGB model bundle was 0.82 (95% CI 0.81-0.83); the AUPRC was 0.24 (95% CI 0.23-0.25), well-above the expected baseline. We conclude that physiomarkers discernable by machine learning are concentrated within blood pressure data up to 4 hours prior to eICP events and demonstrate robust benchmark performance. Future predictive modeling of elevated ICP events should leverage features contained within hemodynamic signals.

A Bioinformatics Perspective on the Links Between Tetraspanin-Enriched Microdomains and Cardiovascular Pathophysiology

Background: Tetraspanins and integrins are integral membrane proteins. Tetraspanins interact with integrins to modulate the dynamics of adhesion, migration, proliferation, and signaling in the form of membrane domains called tetraspanin-enriched microdomains (TEMs). TEMs also contain other cell adhesion proteins like immunoglobulin superfamily (IgSF) proteins and claudins. Cardiovascular functions of these TEM proteins have emerged and remain to be further revealed.Objectives: The aims of this study are to explore the roles of these TEM proteins in the cardiovascular system using bioinformatics tools and databases and to highlight the TEM proteins that may functionally associate with cardiovascular physiology and pathology.Methods: For human samples, three databases—GTEx, NCBI-dbGaP, and NCBI-GEO—were used for the analyses. The dbGaP database was used for GWAS analysis to determine the association between target genes and human phenotypes. GEO is an NCBI public repository that archives genomics data. GTEx was used for the analyses of tissue-specific mRNA expression levels and eQTL. For murine samples, GeneNetwork was used to find gene–phenotype correlations and gene–gene correlations of expression levels in mice. The analysis of cardiovascular data was the focus of this study.Results: Some integrins and tetraspanins, such as ITGA8 and Cd151, are highly expressed in the human cardiovascular system. TEM components are associated with multiple cardiovascular pathophysiological events in humans. GWAS and GEO analyses showed that human Cd82 and ITGA9 are associated with blood pressure. Data from mice also suggest that various cardiovascular phenotypes are correlated with integrins and tetraspanins. For instance, Cd82 and ITGA9, again, have correlations with blood pressure in mice.Conclusion:ITGA9 is related to blood pressure in both species. KEGG analysis also linked ITGA9 to metabolism and MAPK signaling pathway. This work provides an example of using integrated bioinformatics approaches across different species to identify the connections of structurally and/or functionally related molecules to certain categories of diseases.

Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.

THE RELATIONSHIP OF BLOOD PRESSURE AND HEART RATE TO VO2MAX IN ADOLESCENT

ABSTRAK Pendahuluan : Selama aktivitas fisik yang kita lakukan maka akan terjadi perubahan tekanan darah denyut nadi sebagai respon untuk mengangkut O2 ke otot yang sedang beraktivitas. Adolescent usia 18-21 tahun jaman sekarang memiliki masalah terkait dengan gerak yang mengakibatkan menurunnya ketahanan latihan. Tujuan : menganalisa hubungan antara perubahan tekanan darah dan nadi terhadap VO2Max dan menganalisa seberapa erat hubungannya. Metode : penelitian ini merupakan penelitian observatif dengan Teknik pengambilan sampel purposive sampling dengan desain point time approach sejumlah 183 sampel dengan kriteria usia 18-21 tahun. Variabel independent pada penelitian ini adalah ketahanan latihan, dan variabel dependent adalah perubahan tekanan darah dan perubahan frekuensi nadi. Data diolah menggunakan perangkat lunak untuk Analisa data. Hasil : Dari uji normalitas data menggunakan Kolmogorov Smirnov untuk data tekanan darah dengan nilai 0.509, data nadi dengan nilai 0.069, data VO2Max dengan nilai 0.060. Uji Analisa data menggunakan uji pearson product moment menghasilkan nilai tekanan darah -0.000 dengan nilai r -0.029 dan nilai -0.029. Kesimpulan : terdapat hubungan perubahan tekanan darah dan nadi terhadap VO2Max. Kata Kunci : Tekanan darah, Nadi, VO2Max ABSTRACT Introduction: During the physical activity that we do, there will be a change in pulse blood pressure in response to transporting O2 to the muscles that are on the move. Adolescents 18-21 years of age today have problems related to movement that result in decreased resistance training. Purpose: to analyze the relationship between changes in blood pressure and pulse to VO2Max and to analyze how closely it is. Methods: This study is an observative study using purposive sampling technique with a point time approach design with a total of 183 samples with criteria aged 18-21 years. The independent variable in this study is exercise resistance, and the dependent variable is changes in blood pressure and changes in pulse frequency. The data is processed using software for data analysis. Results: From the data normality test using Kolmogorov Smirnov for blood pressure data with a value of 0.509, pulse data with a value of 0.069, VO2Max data with a value of 0.060. Test Data analysis using the Pearson product moment test resulted in a blood pressure value of -0.000 with a value of r -0.029 and a value of -0.029. Conclusion: there is a relationship between changes in blood pressure and pulse to VO2Max. Keyword : Blood Pressure, Heart Rate, VO2Max