Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: An exploratory study of the SPRINT trial

2019 ◽  
Vol 26 (16) ◽  
pp. 1693-1706 ◽  
Author(s):  
Da-ya Yang ◽  
Zhi-qiang Nie ◽  
Li-zhen Liao ◽  
Shao-zhao Zhang ◽  
Hui-min Zhou ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Antihypertensive Treatment ◽  
Cox Regression ◽  
Cardiovascular Outcomes ◽  
Data Driven ◽  
Hypertensive Patients ◽  
Increased Risk ◽  
Data Driven Approach ◽  
Treatment Responses ◽  
Cluster 2

Background Hypertensive patients are highly heterogeneous in cardiovascular prognosis and treatment responses. A better classification system with phenomapping of clinical features would be of greater value to identify patients at higher risk of developing cardiovascular outcomes and direct individual decision-making for antihypertensive treatment. Methods An unsupervised, data-driven cluster analysis was performed for all baseline variables related to cardiovascular outcomes and treatment responses in subjects from the Systolic Blood Pressure Intervention Trial (SPRINT), in order to identify distinct subgroups with maximal within-group similarities and between-group differences. Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for cardiovascular outcomes and compare the effect of intensive antihypertensive treatment in different clusters. Results Four replicable clusters of patients were identified: cluster 1 (index hypertensives); cluster 2 (chronic kidney disease hypertensives); cluster 3 (obese hypertensives) and cluster 4 (extra risky hypertensives). In terms of prognosis, individuals in cluster 4 had the highest risk of developing primary outcomes. In terms of treatment responses, intensive antihypertensive treatment was shown to be beneficial only in cluster 4 (HR 0.73, 95% CI 0.55–0.98) and cluster 1 (HR 0.54, 95% CI 0.37–0.79) and was associated with an increased risk of severe adverse effects in cluster 2 (HR 1.18, 95% CI 1.05–1.32). Conclusion Using a data-driven approach, SPRINT subjects can be stratified into four phenotypically distinct subgroups with different profiles on cardiovascular prognoses and responses to intensive antihypertensive treatment. Of note, these results should be taken as hypothesis generating that warrant further validation in future prospective studies.

Contour clustering: A field-data-driven approach for documenting and analysing prototypical f0 contours

2021 ◽  
pp. 1-30
Author(s):  
Constantijn Kaland
Keyword(s):  
Cluster Analysis ◽  
User Interfaces ◽  
Data Driven ◽  
Language Documentation ◽  
Cluster Number ◽  
Automatic Data ◽  
Speaker Variability ◽  
The Core ◽  
Data Driven Approach ◽  
Confirmatory Analysis

ABSTRACT This paper reports an automatic data-driven analysis for describing prototypical intonation patterns, particularly suitable for initial stages of prosodic research and language description. The approach has several advantages over traditional ways to investigate intonation, such as the applicability to spontaneous speech, language- and domain-independency, and the potential of revealing meaningful functions of intonation. These features make the approach particularly useful for language documentation, where the description of prosody is often lacking. The core of this approach is a cluster analysis on a time-series of f0 measurements and consists of two scripts (Praat and R, available from https://constantijnkaland.github.io/contourclustering/). Graphical user interfaces can be used to perform the analyses on collected data ranging from spontaneous to highly controlled speech. There is limited need for manual annotation prior to analysis and speaker variability can be accounted for. After cluster analysis, Praat textgrids can be generated with the cluster number annotated for each individual contour. Although further confirmatory analysis is still required, the outcomes provide useful and unbiased directions for any investigation of prototypical f0 contours based on their acoustic form.

Abstract 3269: Association of Aortic Regurgitation with Cardiovascular Morbidity and Mortality in Hypertensive Patients with Left Ventricle Hypertrophy: The LIFE study

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Zhibin Li ◽  
Kristian Wachtell ◽  
Sverre E. Kjeldsen ◽  
Stevo Julius ◽  
Michael H. Olsen ◽  
...  
Keyword(s):  
Aortic Regurgitation ◽  
Cox Regression ◽  
Left Ventricular ◽  
Morbidity And Mortality ◽  
Hypertensive Patients ◽  
Life Study ◽  
Cardiovascular Morbidity And Mortality ◽  
Increased Risk ◽  
Lv Mass ◽  
Ventricle Hypertrophy

Background : Whether aortic regurgitation (AI) is associated with higher cardiovascular (CV) morbidity and mortality in hypertension with electrocardiographic (ECG) left ventricular hypertrophy (LVH) is unknown. Methods : Hypertensive patients with ECG-LVH were randomized to losartan- or atenolol-based treatment and followed for 4.8 years in the Losartan Intervention For Endpoint reduction in hypertension (LIFE) study. In the LIFE echo substudy, echocardiograms were used to detect AI. Baseline clinical, echocardiographic variables and cardiovascular endpoints data were used in current analyses. Results: The presence of AI was detected in 132 participants (68 women; 68.4 ± 7.3 years). AI was associated with older age (p < 0.001) but not gender. After adjustment for age, AI was associated with significantly increased LV mass indexed by body surface area (BSA) and height 2.7 (both p < 0.005), echocardiographic eccentric LVH (p < 0.05) but not concentric left ventricular (LV) geometry (p < 0.05). After adjusting for significant confounders including history of CV disease, Framingham risk score, randomized antihypertensive therapy, LV eccentric geometry, LV mass indexed by BSA and height 2.7 , multivariate Cox regression analyses showed that AI was independently associated with 2.83-fold more CV death (95% confidence interval [CI] 1.12 to 7.13), 2.24-fold more all-cause mortality (95% CI 1.17 to 4.28) (both p < 0.05). Conclusion : In hypertensive patients with ECG-LVH, AI independently identifies patients at increased risk of CV and all-course mortality.

scholarly journals Morbidity and mortality outcomes of COVID-19 patients with and without hypertension in Lagos, Nigeria: A retrospective cohort study

2020 ◽  
Author(s):  
Akin Osibogun ◽  
Akin Abayomi ◽  
Oluchi Kanma-Okafor ◽  
Jide Idris ◽  
Abimbola Bowale ◽  
...  
Keyword(s):  
Cohort Study ◽  
Critical Illness ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Cox Regression ◽  
Outcome Data ◽  
Morbidity And Mortality ◽  
P Value ◽  
Hypertensive Patients ◽  
Increased Risk

Abstract Background: The current pandemic of coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown epidemiological and clinical characteristics that appear worsened in hypertensive patients. The morbidity and mortality of the disease among hypertensive patients in Africa have yet to be well described.Methods: In this retrospective cohort study all confirmed COVID-19 adult patients (≥18 years of age) in Lagos between February 27 to July 6 2020 were included. Demographic, clinical and outcome data were extracted from electronic medical records of patients admitted at the COVID-19 isolation centers in Lagos. Outcomes included dying, being discharged after recovery or being evacuated/transferred. Descriptive statistics considered proportions, means and medians. The Chi-square and Fisher’s exact tests were used in determining associations between variables. Kaplan–Meier survival analysis and Cox regression were performed to quantify the risk of worse outcomes among hypertensives with COVID-19 and adjust for confounders. P-value ≤0.05 was considered statistically significant.Results: A total of 2075 adults with COVID-19 were included in this study. The prevalence of hypertension, the most common comorbidity, was 17.8% followed by diabetes (7.2%) and asthma (2.0%). Overall mortality was 4.2% while mortality among the hypertensives was 13.7%. Severe symptoms and mortality were significantly higher among the hypertensives and survival rates were significantly lowered by the presence of an additional comorbidity to 50% from 91% for those with hypertension alone and from 98% for all other patients (P<0.001). After adjustment for confounders (age and sex), severe COVID-19and death were higher for hypertensives {severe/critical illness: HR=2.41, P=0.001, 95%CI=1.4–4.0, death: HR=2.30, P=0.001, 95%CI=1.2–4.6, for those with hypertension only} {severe/critical illness: HR=3.76, P=0.001, 95%CI=2.1–6.4, death: crude HR=6.63, P=0.001, 95%CI=3.4–1.6, for those with additional comorbidities}. Hypertension posed an increased risk of severe morbidity (approx. 4-fold) and death (approx. 7-fold) from COVID-19 in the presence of multiple comorbidities. Conclusion: The potential morbidity and mortality risks of hypertension especially with other comorbidities in COVID-19 could help direct efforts towards prevention and prognostication. This provides the rationale for improving preventive caution for people with hypertension and other comorbidities and prioritizing them for future antiviral interventions.

scholarly journals Body Roundness Index Is a Superior Obesity Index in Predicting Diabetes Risk Among Hypertensive Patients: A Prospective Cohort Study in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Yingshan Liu ◽  
Xiaocong Liu ◽  
Haixia Guan ◽  
Shuting Zhang ◽  
Qibo Zhu ◽  
...  
Keyword(s):  
Cox Regression ◽  
Roc Curves ◽  
Diabetes Risk ◽  
Regression Analyses ◽  
General Obesity ◽  
Anthropometric Indices ◽  
Hypertensive Patients ◽  
Obesity Status ◽  
Increased Risk ◽  
Obesity Indices

Objective: Individuals with both hypertension and diabetes have been confirmed to significantly increase the risk of cardiovascular disease morbidity and mortality compared with those with only hypertension or diabetes. This study aimed to evaluate the potential of different anthropometric indices for predicting diabetes risk among hypertensive patients.Methods: The study group consisted of 6,990 hypertensive adults without diabetes who were recruited in China. Demographic and clinical assessment, physical examinations, laboratory tests, and anthropometric measurements, including body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and novel indices (ABSI, AVI, BAI, BRI, CI, WWI, and WHHR), were performed at baseline and during the (median) 3-year follow-up. Cox regression analyses were conducted to estimate effects from these indices for the onset of diabetes. Receiver operator characteristic (ROC) analyses were conducted to assess the predictive capacities of the anthropometric indices and determine the optimal cut-points.Results: A total of 816 (11.7%) developed diabetes during our prospective study. Multivariate Cox regression analyses revealed weight, WC, WHR, WHtR, BAI, BRI, and WWI as the independent risk factor for diabetes among hypertensive patients, regardless of whether it was treated as a continuous or categorical variable (P &lt; 0.05). Further Cox analyses combining BMI and different central obesity indices showed that elevated WC, WHR, WHtR, AVI, BRI, CI, regardless of the general obesity status, were found to be each independently associated with increased diabetes risk (P &lt; 0.05). Dynamic increases of BRI &lt; 5.24 to BRI ≥ 5.24 were associated with increased risk (HR = 1.29; 95% CI, 1.02, 1.64), and its reversal was associated with reduced risk (HR = 1.56; 95% CI, 1.23, 1.98) compared with the others (HR = 1.95; 95% CI, 1.63, 2.32). ROC analysis indicated that the areas under the ROC curves (AUC) of the anthropometric indices ranged from 0.531 to 0.63, with BRI (cut-off value = 4.62) and WHtR having the largest area.Conclusions: Based on this novel study, BRI was the most superior predictor and independent determinant for diabetes onset among the hypertensive population. Hypertensive patients with BRI &gt; 4.62, regardless of general obesity status, were at high risk of diabetes. Thus, the prompt screening and diagnosis of diabetes should be carried out among these patients for timely integrated intervention.

scholarly journals 625 Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events

2021 ◽  
Vol 23 (Supplement_G) ◽  
Author(s):  
Alberto Testa ◽  
Sabrina Anticoli ◽  
Francesca Romana Pezzella ◽  
Marilena Mangiardi ◽  
Alessandro Di Giosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Hierarchical Clustering ◽  
Metropolitan Area ◽  
Unsupervised Machine Learning ◽  
Large Metropolitan Area ◽  
Cerebrovascular Events ◽  
Increased Risk ◽  
The Impact ◽  
Cluster 2

Abstract Aims The impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. Methods and results Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from three tertiary care centre from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for environmental protection, and from the Meteorologic Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rate of acute cardiac and cerebrovascular events with Poisson models. As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated four separate clusters: Cluster 1, including 60 (5.1%) days, Cluster 2 with 419 (35.8%) days, Cluster 3 with 673 (57.6%) days, and Cluster 4 with 17 (1.5%) days, with significant between-cluster differences in weather and pollution features. Notably, Cluster 1 was characterized by low temperatures and high ozone concentrations (P &lt; 0.001). Overall cluster-wise comparisons showed significant overall differences in adverse cardiac and cerebrovascular events (P &lt; 0.001), as well as in cerebrovascular events (P &lt; 0.001) and strokes (P = 0.001). Between-cluster comparisons showed that Cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to Cluster 2, Cluster 3, and Cluster 4 (all P &lt; 0.05), as well as AMI in comparison to Cluster 3 (P = 0.047). In addition, Cluster 2 was associated with a higher risk of strokes in comparison to Cluster 4 (P = 0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events, and strokes for Cluster 1 and Cluster 2. Conclusions Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increases risk of acute cardiovascular events, especially cerebrovascular events.

scholarly journals Suboptimal Adherence to Antihypertensive Treatment: Causes and Management

2018 ◽  
Vol 10 (1) ◽  
pp. 41-45
Author(s):  
Stelina Alkagiet ◽  
Konstantinos Tziomalos
Keyword(s):  
Blood Pressure ◽  
Antihypertensive Treatment ◽  
Antihypertensive Agents ◽  
Fixed Dose ◽  
Hypertensive Patients ◽  
Treatment Regimens ◽  
Achieve Blood Pressure ◽  
Increased Risk ◽  
Suboptimal Adherence ◽  
Long Acting

Suboptimal adherence to antihypertensive treatment is very common and is associated with poor control of blood pressure and increased risk for cardiovascular events. Therefore, frequent evaluation of compliance is essential in all hypertensive patients. Simplifying treatment regimens, using fixed-dose combinations and long-acting agents improves adherence, facilitates achievement of treatment targets and reduces cardiovascular morbidity and healthcare expenditures. Accordingly, physicians should be educated to implement these changes in hypertensive patients, particularly in those who require multiple antihypertensive agents to achieve blood pressure controls and in those who receive additional medications for comorbidities.

scholarly journals Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Marco Proietti ◽  
Marco Vitolo ◽  
Stephanie L. Harrison ◽  
Deirdre A. Lane ◽  
Laurent Fauchier ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cox Regression ◽  
Research Programme ◽  
Hierarchical Cluster ◽  
Epidemiological Studies ◽  
Optimal Number ◽  
Adverse Outcomes ◽  
Clinical Phenotypes ◽  
Increased Risk ◽  
Cluster 2

Abstract Background Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients’ clinical phenotypes and analyse the differential clinical course. Methods We performed a hierarchical cluster analysis based on Ward’s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients’ prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27–3.62; HR 3.42, 95%CI 2.72–4.31; HR 2.79, 95%CI 2.32–3.35), and Cluster 1 (HR 1.88, 95%CI 1.48–2.38; HR 2.50, 95%CI 1.98–3.15; HR 2.09, 95%CI 1.74–2.51) reported a higher risk for the three outcomes respectively. Conclusions In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes.

Cardiovascular outcomes and achieved blood pressure in patients with and without diabetes at high cardiovascular risk

2019 ◽  
Vol 40 (25) ◽  
pp. 2032-2043 ◽  
Author(s):  
Michael Böhm ◽  
Helmut Schumacher ◽  
Koon K Teo ◽  
Eva M Lonn ◽  
Felix Mahfoud ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Risk ◽  
Primary Outcome ◽  
Cox Regression ◽  
Cardiovascular Death ◽  
Cardiovascular Outcomes ◽  
Composite Outcome ◽  
High Cardiovascular Risk ◽  
Increased Risk ◽  
Patients With Diabetes

Abstract Aims Studies have shown a non-linear relationship between systolic blood pressure (SBP) and diastolic blood pressure (DBP) and outcomes, with increased risk observed at both low and high blood pressure (BP) levels. We hypothesized that the BP-risk association is different in individuals with and without diabetes at high cardiovascular risk. Methods and results We identified patients with (N = 11 487) or without diabetes (N = 19 450), from 30 937 patients, from 133 centres in 44 countries with a median follow-up of 56 months in the ONTARGET/TRANSCEND studies. Patients had a prior history of stroke, myocardial infarction (MI), peripheral artery disease, or were high-risk diabetics. Patients in ONTARGET had been randomized to ramipril 10 mg daily, telmisartan 80 mg daily, or the combination of both. Patients in TRANSCEND were ACE intolerant and randomized to telmisartan 80 mg daily or matching placebo. We analysed the association of mean achieved in-trial SBP and DBP with the composite outcome of cardiovascular death, MI, stroke and hospitalization for congestive heart failure (CHF), the components of the composite, and all-cause death. Data were analysed by Cox regression and restricted cubic splines, adjusting for risk markers including treatment allocation and accompanying cardiovascular treatments. In patients with diabetes, event rates were higher across the whole spectrum of SBP and DBP compared with those without diabetes (P < 0.0001 for the primary composite outcome, P < 0.01 for all other endpoints). Mean achieved in-trial SBP ≥160 mmHg was associated with increased risk for the primary outcome [diabetes/no diabetes: adjusted hazard ratio (HR) 2.31 (1.93–2.76)/1.66 (1.36–2.02) compared with non-diabetics with SBP 120 to <140 mmHg], with similar findings for all other endpoints in patients with diabetes, and for MI and stroke in patients without diabetes. In-trial SBP <120 mmHg was associated with increased risk for the combined outcome in patients with diabetes [HR 1.53 (1.27–1.85)], and for cardiovascular death and all-cause death in all patients. In-trial DBP ≥90 mmHg was associated with increased risk for the primary outcome [diabetes/no diabetes: HR 2.32 (1.91–2.82)/1.61 (1.35–1.93) compared with non-diabetics with DBP 70 to <80 mmHg], with similar findings for all other endpoints, but not for CHF hospitalizations in patients without diabetes. In-trial DBP <70 mmHg was associated with increased risk for the combined outcome in all patients [diabetes/no diabetes: HR 1.77 (1.51–2.06)/1.30 (1.16–1.46)], and also for all other endpoints except stroke. Conclusion High on treatment BP levels (≥160 or ≥90 mmHg) are associated with increased risk of cardiovascular outcomes and death. Also low levels (<120 or <70 mmHg) are associated with increased cardiovascular outcomes (except stroke) and death. Patients with diabetes have consistently higher risks over the whole BP range, indicating that achieving optimal BP goals is most impactful in this group. These data favour guidelines taking lower BP boundaries into consideration, in particular in diabetes. Clinical trial registration http://clinicaltrials.gov.Unique identifier: NCT00153101.

scholarly journals Are machines stealing our jobs?

2020 ◽  
Vol 13 (1) ◽  
pp. 153-173 ◽  
Author(s):  
Andrea Gentili ◽  
Fabiano Compagnucci ◽  
Mauro Gallegati ◽  
Enzo Valentini
Keyword(s):  
Cluster Analysis ◽  
International Federation ◽  
Data Driven ◽  
The Political ◽  
Human Labour ◽  
The Future ◽  
Data Driven Approach ◽  
Technological Transition ◽  
Cross Country ◽  
Academic Debate

Abstract This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.

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