Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.

Comorbidities of diabetes and hypertension in Vietnam: current burden, trends over time, and correlated factors.

BACKGROUND: Vietnam conducted the national Noncommunicable Disease Risk-Factor Surveillance (STEPs) surveys in the years 2010, 2015, and 2021. This study aims to use STEPs data to assess the burden of comorbidity between diabetes and hypertension, analyze trends over time, and identify factors associated with this comorbidity. METHODS: The study extracted data for the population aged 25-64 years old from three STEPs surveys. Survey weight was used for all estimations of prevalence and 95% CI. Correlated factors with comorbidity were examined by a multivariate logistics model. RESULTS: The prevalence of comorbidity in 2021 was about 3.92% among Vietnamese people aged 25-64. In the last 10 years, this prevalence has increased more than 8 times (from 0.44% to 3.92%). Sub-populations demonstrating the most significant changes included the male population, people living in urban areas, and older people. Significant factors correlated with comorbidity included demographic factors, body mass index (BMI), and clustering of 4 noncommunicable diseases (NCDs) behavioral risk factors (OR = 3.48, p < 0.05). CONCLUSION: The high comorbidity between hypertension and diabetes underscores the imperative for integrated treatment and management approaches in Vietnam. Coordinated care is essential for addressing the complex interplay between these two prevalent conditions.

The Role of N-Terminal-Pro-B-Type Natriuretic Peptide (NT-proBNP) and High-Sensitivity Troponin T (Hs-Troponin T) in the Evaluation of the Syntax Score in Patients With Acute Coronary Syndrome.

Background N-terminal-pro-B-type natriuretic peptide (NT-proBNP) is used to diagnose acute and chronic heart failure, but many studies show a strong and independent correlation between NT-proBNP serum levels and the severity and number of coronary artery damage. Meanwhile, the serum of high-sensitivity Troponin T (hs-Troponin T) has a very high prognostic value for the degree of coronary artery damage in patients with acute coronary syndrome. The SYNTAX score was developed to better predict the risks of percutaneous or surgical revascularization by considering the functional impact of the coronary circulation with all of its anatomic components, such as the presence of bifurcations, total occlusions, thrombus, calcification, and small vessels. Therefore, we conducted this study to understand the role of NT-proBNP and hs-troponin T in SYNTAX score evaluation in patients with acute coronary syndrome. Methodology A cross-sectional descriptive study of 86 patients diagnosed with acute coronary syndrome with indications for coronary angiography and intervention in the Department of Emergency and Interventional Cardiology, Cardiovascular Center, Hue Central Hospital, was conducted from June 2020 to May 2022. Results The mean age was 66.94 ± 10.61 years. The concentrations of NT-proBNP and hs-Troponin T in our study were 1115.9 ± 1623.3 pg/mL and 0.86 ± 1.55 ng/mL, respectively. The mean SYNTAX score in the study was 16.5 ± 7.5. There was a positive moderate correlation between the mean levels of NT-proBNP and the degree of coronary artery damage, as indicated by the SYNTAX score (P < 0.01, rho = +0.453). Conversely, there was a weak positive correlation between hs-Troponin T concentrations and the severity of coronary artery disease, based on the SYNTAX score (P < 0.01, rho = +0.387). The area under the curve (AUC) of the hs-Troponin T concentration value was 0.701, using a cutoff point of 0.109 ng/mL for hs-Troponin T concentration. This predicted the intermediate and high SYNTAX scores, with a sensitivity of 76% and a specificity of 59%. In comparison, the AUC of the NT-proBNP concentration value was 0.75, utilizing a cutoff point of 1120.5 pg/mL for NT-proBNP concentration. This predicted the intermediate and high SYNTAX scores, with a sensitivity of 60% and a specificity of 80.3%. Conclusions The levels of NT-proBNP had a positive moderate correlation with the degree of coronary artery damage according to the SYNTAX score in patients with acute coronary syndrome. Hs-Troponin T levels of 0.109 ng/mL had higher sensitivity (76%) but lower specificity (59%) in predicting intermediate and high SYNTAX scores in patients with acute coronary syndromes than those of NT-proBNP levels of 1120.5 pg/mL, with a sensitivity of 60% and a specificity of 80.3%.

Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring.

Background: Inaccurate blood pressure (BP) classification results in inappropriate treatment. We tested whether machine learning (ML), using routine clinical data, can serve as a reliable alternative to ambulatory BP monitoring (ABPM) in classifying BP status. Methods: This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdansk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into 5 groups, named as follows: Normal/Target; Hypertension-Masked; Normal/Target-White-Coat (WC); Hypertension-WC; and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall, extreme gradient boosting (using XGBoost open source software) showed the highest area under the receiver operating characteristic curve of 0.85-0.88 across derivation cohorts, Glasgow (n = 923; 43% female; age 50.7 ± 16.3 years), Gdansk (n = 709; 46% female; age 54.4 ± 13 years), and Birmingham (n = 1222; 56% female; age 55.7 ± 14 years). But accuracy (0.57-0.72) and F1 (harmonic mean of precision and recall) scores (0.57-0.69) were low across the 3 patient cohorts. The evaluation cohort (n = 6213; 51% female; age 51.2 ± 10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-WC and the Hypertension-WC groups, with heightened 27-year all-cause mortality observed in all groups, except the Hypertension-Masked group, compared to the Normal/Target group. Conclusions: ML has limited potential in accurate BP classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.

Contexte: Les erreurs dans la classification des valeurs de la pression artérielle (PA) entraînent une inadéquation du traitement. Nous avons tâché de déterminer si l'apprentissage machine, à l'aide de données cliniques routinières, constituait une solution de rechange fiable à la surveillance ambulatoire de la PA pour définir le statut de la PA. Méthodologie: Cette étude a utilisé une approche multicentrique incluant trois cohortes de dérivation de Glasgow, Gdansk et Birmingham, et une quatrième cohorte d'évaluation indépendante. Les modèles d'apprentissage machine ont été développés en analysant les données démographiques, les valeurs de la PA mesurée au cabinet, les données relatives à la surveillance ambulatoire de la PA et aux épreuves de laboratoire recueillies auprès de patients adressés pour une évaluation de l'hypertension. Sept algorithmes d'apprentissage machine ont été appliqués pour classer les patients en cinq groupes : Normale/Cible; Hypertension-Masquée; Normal/Cible-Blouse blanche; Hypertension-Blouse blanche; Hypertension. Les événements cardiovasculaires sur 10 ans et le risque de mortalité toutes causes confondues sur 27 ans ont été calculés dans les groupes dérivés de l'apprentissage machine à l'aide d'un modèle de risques proportionnels de Cox. Résultats: D'une manière générale, l'amplification de gradient extrême (à l'aide du logiciel ouvert XGBoost) a mis en évidence l'aire sous la courbe de la fonction d'efficacité du récepteur (courbe ROC pour Receiver Operating Characteristic) la plus haute, soit 0,85 à 0,88, pour toutes les cohortes de dérivation : Glasgow (n = 923; 43 % de femmes; âge : 50,7 ± 16,3 ans); Gdansk (n = 709; 46 % de femmes; âge : 54,4 ± 13 ans); Birmingham (n = 1 222; 56 % de femmes; âge : 55,7 ± 14 ans). La précision (0,57 ­ 0,72) et le score F1 (moyenne harmonique de la précision et du rappel) (0,57 ­ 0,69) ont été faibles dans les trois cohortes de patients. La cohorte d'évaluation (n = 6 213; 51 % de femmes; âge : 51,2 ± 10,8 ans) a indiqué un risque d'événements cardiovasculaires composites sur 10 ans élevé dans les groupes Normale/Cible-Blouse blanche et Hypertension-Blouse blanche, tandis qu'une hausse de la mortalité toutes causes confondues sur 27 ans a été observée dans tous les groupes, sauf dans le groupe Hypertension-Masquée, comparativement au groupe Normale/Cible. Conclusions: Le potentiel d'exactitude de la classification de la PA à l'aide de l'apprentissage machine lorsque la surveillance ambulatoire de la PA n'est pas possible est limité. Des études de plus grande envergure portant sur des groupes de patients et des niveaux de ressources diversifiés s'imposent.

Establishing plausibility of cardiovascular adverse effects of immunotherapies using Mendelian randomisation.

Immune checkpoint inhibitors (ICIs) and Janus kinase inhibitors (JAKis) have raised concerns over serious unexpected cardiovascular adverse events. The widespread pleiotropy in genome-wide association studies offers an opportunity to identify cardiovascular risks from in-development drugs to help inform appropriate trial design and pharmacovigilance strategies. This study uses the Mendelian randomization (MR) approach to study the causal effects of 9 cardiovascular risk factors on ischemic stroke risk both independently and by mediation, followed by an interrogation of the implicated expression quantitative trait loci (eQTLs) to determine if the enriched pathways can explain the adverse stroke events observed with ICI or JAKi treatment. Genetic predisposition to higher systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), waist-to-hip ratio (WHR), low-density lipoprotein cholesterol (LDL), triglycerides (TG), type 2 diabetes (T2DM), and smoking index were associated with higher ischemic stroke risk. The associations of genetically predicted BMI, WHR, and TG on the outcome were attenuated after adjusting for genetically predicted T2DM [BMI: 53.15% mediated, 95% CI 17.21%-89.10%; WHR: 42.92% (4.17%-81.67%); TG: 72.05% (10.63%-133.46%)]. JAKis, programmed cell death protein 1 and programmed death ligand 1 inhibitors were implicated in the pathways enriched by the genes related to the instruments for each of SBP, DBP, WHR, T2DM, and LDL. Overall, MR mediation analyses support the role of T2DM in mediating the effects of BMI, WHR, and TG on ischemic stroke risk and follow-up pathway enrichment analysis highlights the utility of this approach in the early identification of potential harm from drugs.

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Violence and non-fatal injuries among Vietnamese in-school adolescents: national prevalence estimates and associated factors.

School violence and injury are major public health problems worldwide. The data of this study were from the Viet Nam Global School-based Student Health Survey (GSHS) - a nationally representative survey conducted in 2019, with a sample size of 7690 students aged 13-17 years old. We found the prevalence of violence and non-fatal injury to be 14.5% and 21.4%, respectively. Factors related to higher odds of violence and non-fatal injuries in both sexes included smoking cigarettes, drinking alcohol, having mental health problems (loneliness, worrying and suicidal thinking), and truancy. In contrast, parental monitoring was associated with lower odds of violence, and parental respect was a protective factor of both violence and non-fatal injuries. Students who experienced violence had a higher likelihood of having non-fatal injuries in both sexes. Future policies should consider individual factors and parent-child bonding, to mitigate the burden of violence and injury among in-school adolescents in Viet Nam.

Machine learning integration of multimodal data identifies key features of blood pressure regulation.

BACKGROUND: Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP). METHODS: We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB). FINDINGS: Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts. INTERPRETATION: We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies. FUNDING: Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.

Patterns of Raised Blood Pressure in Vietnam: Findings from the WHO STEPS Survey 2015.

This study aims to describe the prevalence of raised blood pressure and the situation of management for raised blood pressure among the adult population in Vietnam. It also aims to examine the association between diversified socioeconomic and behavioral factors of raised blood pressure and awareness of raised blood pressure. Data were obtained from the STEPS survey conducted in Vietnam in 2015. Survey sample was nationally representative with a total of 3,856 people aged 18-69 years old. The study outcomes included raised blood pressure and awareness of and control of raised blood pressure. Multiple logistic regression was used to examine the association of socioeconomic and behavior risk factors with the outcome variables. The overall prevalence of raised blood pressure in Vietnam in 2015 was 18.9% (95% CI: 17.4%-20.6%). The prevalence of raised blood pressure was higher among men. Significantly correlated factors with raised blood pressure were age, sex, body mass index, and diabetes status. Levels of awareness of raised blood pressure were higher among the older age group and overweight people and lower among ethnic minority groups. Raised blood pressure in Vietnam is a serious problem due to its magnitude and the unacceptably high unawareness rate in the population. Public health actions dealing with the problems of raised blood pressure are urgent, while taking into account its relationship with sex and socioeconomic status. It is clear that the interventions should address all people in society, with a focus on disadvantaged groups which are the rural and ethnic minority peoples.

Impact of glafenine hydrochloride on human endothelial cells and human vascular smooth muscle cells: a substance reducing proliferation, migration and extracellular matrix synthesis.

The aim of this study was to examine the effects of glafenine hydrochloride (a nonsteroidal anti-inflammatory drug) on proliferation, clonogenic activity, cell-cycle, migration, and the extracellular matrix protein tenascin of human aortic smooth muscle cells (haSMCs) and human endothelial cells (ECs) in vitro.HaSMCs and ECs were seeded in tissue culture flasks. The cells were treated for 4 days with glafenine hydrochloride (10 microM, 50 microM, 100 microM). Half of the treated groups were incubated again with glafenine hydrochloride, the other half received medium free of glafenine hydrochloride every 4 days until day 20. The growth kinetics and clonogenic activity were assessed. Cell cycle distribution was investigated by FACS, migratory ability was evaluated, and effects on extracellular matrix synthesis were assessed by immunofluorescence. Glafenine hydrochloride inhibited the proliferation and clonogenic activity of haSMCs and ECs in a dose-dependent manner. A block in the G2/M phase and a reduction in the G1 phase occurred. The migratory ability of haSMCs was impaired in a dose-dependent manner and the extracellular matrix protein tenascin was reduced. As glafenine hydrochloride has the ability to fully inhibit proliferation and to partially inhibit migration in haSMCs, it could be an interesting substance for further research in the field of restenosis therapy.