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A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study.

ABSTRACT: We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ?30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), non-HDL-C, FPG and family history of CVD] predictors in women and men, respectively. Additional risk factors were identified by Cox models which included: family history of CVD and waist circumference (in both genders); hip circumference, former smoking and using aspirin among men; diastolic blood pressure and lipid lowering drug among women. Survival trees and multivariate Cox models yielded comparable performance, as measured by integrated Brier score (IBS) and Harrell's C-index on validation datasets; however, survival trees produced more parsimonious models with a minimum number of well recognized risk factors of CHD incidence, and identified important interactions between these factors which have important implications for intervention programs and improve clinical decision making.

SUBMITTER: Ramezankhani A

PROVIDER: S-EPMC5468345 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study.

Ramezankhani Azra A Bagherzadeh-Khiabani Farideh F Khalili Davood D Azizi Fereidoun F Hadaegh Farzad F Hadaegh Farzad F

Scientific reports 20170612 1

We identified risk patterns associated with incident coronary heart disease (CHD) using survival tree, and compared performance of survival tree versus Cox proportional hazards (Cox PH) in a cohort of Iranian adults. Data on 8,279 participants (3,741 men) aged ≥30 yr were used to analysis. Survival trees identified seven subgroups with different risk patterns using four [(age, non-HDL-C, fasting plasma glucose (FPG) and family history of diabetes] and five [(age, systolic blood pressure (SBP), n ...[more]

PMID: 28607472

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Project description:Mechanical stress from haemodynamic perturbations or interventional manipulation of epicardial coronary atherosclerotic plaques with inflammatory destabilization can release particulate debris, thrombotic material and soluble substances into the coronary circulation. The physical material obstructs the coronary microcirculation, whereas the soluble substances induce endothelial dysfunction and facilitate vasoconstriction. Coronary microvascular obstruction and dysfunction result in patchy microinfarcts accompanied by an inflammatory reaction, both of which contribute to progressive myocardial contractile dysfunction. In clinical studies, the benefit of protection devices to retrieve atherothrombotic debris during percutaneous coronary interventions has been modest, and the treatment of microembolization has mostly relied on antiplatelet and vasodilator agents. The past 25 years have witnessed a relative proportional increase in non-ST-segment elevation myocardial infarction in the presentation of acute coronary syndromes. An associated increase in the incidence of plaque erosion rather than rupture has also been recognized as a key mechanism in the past decade. We propose that coronary microembolization is a decisive link between plaque erosion at the culprit lesion and the manifestation of non-ST-segment elevation myocardial infarction. In this Review, we characterize the features and mechanisms of coronary microembolization and discuss the clinical trials of drugs and devices for prevention and treatment. In this Review, Kleinbongard and Heusch characterize the features and mechanisms of coronary microembolization and discuss the clinical trials of drugs and devices for the prevention and treatment of this phenomenon. Key points Coronary microembolization results from spontaneous or interventional erosion or rupture of an epicardial coronary atherosclerotic plaque. Atherothrombotic plaque debris causes physical obstruction of coronary microvessels and results in microinfarcts with an inflammatory response. The surviving myocardium around the patchy microinfarcts has reduced contractile function as a result of inflammatory signal transduction leading to myofibrillar oxidation. Atherosclerotic plaque erosion or rupture releases soluble substances that induce coronary microvascular endothelial dysfunction, vasoconstriction, platelet aggregation and coagulation. Routine use of interventional protection devices does not improve patient outcomes but these devices are indicated in cases of high atherothrombotic plaque burden. Plaque stabilization with statins and canakinumab prevents coronary microembolization, whereas platelet inhibitors, vasodilators and interleukin antagonists attenuate coronary microvascular impairment.

Project description:BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

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A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study.

Publications

A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study.

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