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- Franklin, Stanley S., et al.
(författare)
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The Cardiovascular Risk of White-Coat Hypertension
- 2016
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Ingår i: Journal of the American College of Cardiology. - : Elsevier BV. - 0735-1097 .- 1558-3597. ; 68:19, s. 2033-2043
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND The role of white-coat hypertension (WCH) and the white-coat-effect (WCE) in development of cardiovascular disease (CVD) risk remains poorly understood. OBJECTIVES Using data from the population-based, 11-cohort IDACO (International Database on Ambulatory Blood Pressure Monitoring in Relation to Cardiovascular Outcomes), this study compared daytime ambulatory blood pressure monitoring with conventional blood pressure measurements in 653 untreated subjects with WCH and 653 normotensive control subjects. METHODS European Society Hypertension guidelines were used as a 5-stage risk score. Low risk was defined as 0 to 2 risk factors, and high risk was defined as >= 3 to 5 risk factors, diabetes, and/or history of prior CVD events. Age-and cohort-matching was done between 653 untreated subjects with WCH and 653 normotensive control subjects. RESULTS In a stepwise linear regression model, systolic WCE increased by 3.8 mm Hg (95% confidence interval [CI]: 3.1 to 4.6 mm Hg) per 10-year increase in age, and was similar in low-and high-risk subjects with or without prior CVD events. Over a median 10.6-year follow-up, incidence of new CVD events was higher in 159 high-risk subjects with WCH compared with 159 cohort-and age-matched high-risk normotensive subjects (adjusted hazard ratio [HR]: 2.06; 95% CI: 1.10 to 3.84; p = 0.023). The HR was not significant for 494 participants with low-risk WCH and age-matched low-risk normotensive subjects. Subgroup analysis by age showed that an association between WCH and incident CVD events is limited to older (age >= 60 years) high-risk WCH subjects; the adjusted HR was 2.19 (95% CI: 1.09 to 4.37; p = 0.027) in the older high-risk group and 0.88 (95% CI: 0.51 to 1.53; p = 0.66) in the older low-risk group (p for interaction = 0.044). CONCLUSIONS WCE size is related to aging, not to CVD risk. CVD risk in most persons with WCH is comparable to age-and risk-adjusted normotensive control subjects.
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| 2. |
- Mamas, Mamas A., et al.
(författare)
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Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models
- 2023
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Ingår i: The European Heart Journal - Digital Health. - : Oxford University Press. - 2634-3916. ; 4:6, s. 433-443
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.METHODS AND RESULTS: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk. CONCLUSION: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.REGISTRATION: Clinicaltrial.gov identifier is NCT02188355.
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| 3. |
- Sampson, Joshua N., et al.
(författare)
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Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for 13 Cancer Types
- 2015
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 0027-8874 .- 1460-2105. ; 107:12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, h(l)(2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (rho = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (rho = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (rho = 0.51, SE = 0.18), and bladder and lung (rho = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. Conclusion: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.
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