Marinus J.C. Eijkemans

Bio: Marinus J.C. Eijkemans is an academic researcher from Utrecht University. The author has contributed to research in topics: Population & Polycystic ovary. The author has an hindex of 69, co-authored 263 publications receiving 20520 citations. Previous affiliations of Marinus J.C. Eijkemans include University Medical Center Utrecht & Oklahoma State University Center for Health Sciences.

Prognostic modelling with logistic regression analysis : a comparison of selection and estimation methods in small data sets

Abstract: Logistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is difficult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coefficients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with significance levels alpha of 0.01, 0.05, 0. 157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coefficients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coefficients. We found that stepwise selection with a low alpha (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coefficients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespecified predictors and incorporation of external information, when prognostic models are constructed in small data sets.

A meta-analysis of pregnancy outcomes in women with polycystic ovary syndrome

Abstract: Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with many characteristic features, including hyperandrogenaemia, insulin resistance and obesity which may have significant implications for pregnancy outcomes and long-term health of the woman. This meta-analysis was conducted to evaluate the risk of pregnancy and neonatal complications in women with PCOS. Electronic databases were searched for the following MeSH headings: PCOS, hyperandrogenism, pregnancy outcome, pregnancy complications, diabetes mellitus, type II. A handsearch of human reproduction and fertility and sterility was also conducted. Studies in which pregnancy outcomes in women with PCOS were compared with controls were considered for inclusion in this meta-analysis. Fifteen of 525 identified studies were included, involving 720 women presenting with PCOS and 4505 controls. Women with PCOS demonstrated a significantly higher risk of developing gestational diabetes [odds ratio (OR) 2.94; 95% confidence interval (CI): 1.70-5.08], pregnancy-induced hypertension (OR 3.67; 95% CI: 1.98-6.81), pre-eclampsia (OR 3.47; 95% CI: 1.95-6.17) and preterm birth (OR 1.75; 95% CI: 1.16-2.62). Their babies had a significantly higher risk of admission to a neonatal intensive care unit (OR 2.31; 95% CI: 1.25-4.26) and a higher perinatal mortality (OR 3.07; 95% CI: 1.03-9.21), unrelated to multiple births. In conclusion, women with PCOS are at increased risk of pregnancy and neonatal complications. Pre-pregnancy, antenatal and intrapartum care should be aimed at reducing these risks.

Common Carotid Intima-Media Thickness Measurements in Cardiovascular Risk Prediction A Meta-analysis

Abstract: Context: The evidence that measurement of the common carotid intima-media thickness (CIMT) improves the risk scores in prediction of the absolute risk of cardiovascular events is inconsistent. Objective: To determine whether common CIMT has added value in 10-year risk prediction of first-time myocardial infarctions or strokes, above that of the Framingham Risk Score. Data Sources: Relevant studies were identified through literature searches of databases (PubMed from 1950 to June 2012 and EMBASE from 1980 to June 2012) and expert opinion. Study Selection: Studies were included if participants were drawn from the general population, common CIMT was measured at baseline, and individuals were followed up for first-time myocardial infarction or stroke. Data Extraction: Individual data were combined into 1 data set and an individual participant data meta-analysis was performed on individuals without existing cardiovascular disease. Results: We included 14 population-based cohorts contributing data for 45 828 individuals. During a median follow-up of 11 years, 4007 first-time myocardial infarctions or strokes occurred. We first refitted the risk factors of the Framingham Risk Score and then extended the model with common CIMT measurements to estimate the absolute 10-year risks to develop a first-time myocardial infarction or stroke in both models. The C statistic of both models was similar (0.757; 95% CI, 0.749-0.764; and 0.759; 95% CI, 0.752-0.766). The net reclassification improvement with the addition of common CIMT was small (0.8%; 95% CI, 0.1%-1.6%). In those at intermediate risk, the net reclassification improvement was 3.6% in all individuals (95% CI, 2.7%-4.6%) and no differences between men and women. Conclusion: The addition of common CIMT measurements to the Framingham Risk Score was associated with small improvement in 10-year risk prediction of first-time myocardial infarction or stroke, but this improvement is unlikely to be of clinical importance.

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

European Guidelines on Cardiovascular Disease Prevention in Clinical Practice (Version 2012)

Abstract: ABI : ankle–brachial index ACCORD : Action to Control Cardiovascular Risk in Diabetes ADVANCE : Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation AGREE : Appraisal of Guidelines Research and Evaluation AHA : American Heart Association apoA1 : apolipoprotein A1 apoB : apolipoprotein B CABG : coronary artery bypass graft surgery CARDS : Collaborative AtoRvastatin Diabetes Study CCNAP : Council on Cardiovascular Nursing and Allied Professions CHARISMA : Clopidogrel for High Athero-thrombotic Risk and Ischemic Stabilisation, Management, and Avoidance CHD : coronary heart disease CKD : chronic kidney disease COMMIT : Clopidogrel and Metoprolol in Myocardial Infarction Trial CRP : C-reactive protein CURE : Clopidogrel in Unstable Angina to Prevent Recurrent Events CVD : cardiovascular disease DALYs : disability-adjusted life years DBP : diastolic blood pressure DCCT : Diabetes Control and Complications Trial ED : erectile dysfunction eGFR : estimated glomerular filtration rate EHN : European Heart Network EPIC : European Prospective Investigation into Cancer and Nutrition EUROASPIRE : European Action on Secondary and Primary Prevention through Intervention to Reduce Events GFR : glomerular filtration rate GOSPEL : Global Secondary Prevention Strategies to Limit Event Recurrence After MI GRADE : Grading of Recommendations Assessment, Development and Evaluation HbA1c : glycated haemoglobin HDL : high-density lipoprotein HF-ACTION : Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing HOT : Hypertension Optimal Treatment Study HPS : Heart Protection Study HR : hazard ratio hsCRP : high-sensitivity C-reactive protein HYVET : Hypertension in the Very Elderly Trial ICD : International Classification of Diseases IMT : intima-media thickness INVEST : International Verapamil SR/Trandolapril JTF : Joint Task Force LDL : low-density lipoprotein Lp(a) : lipoprotein(a) LpPLA2 : lipoprotein-associated phospholipase 2 LVH : left ventricular hypertrophy MATCH : Management of Atherothrombosis with Clopidogrel in High-risk Patients with Recent Transient Ischaemic Attack or Ischaemic Stroke MDRD : Modification of Diet in Renal Disease MET : metabolic equivalent MONICA : Multinational MONItoring of trends and determinants in CArdiovascular disease NICE : National Institute of Health and Clinical Excellence NRT : nicotine replacement therapy NSTEMI : non-ST elevation myocardial infarction ONTARGET : Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial OSA : obstructive sleep apnoea PAD : peripheral artery disease PCI : percutaneous coronary intervention PROactive : Prospective Pioglitazone Clinical Trial in Macrovascular Events PWV : pulse wave velocity QOF : Quality and Outcomes Framework RCT : randomized clinical trial RR : relative risk SBP : systolic blood pressure SCORE : Systematic Coronary Risk Evaluation Project SEARCH : Study of the Effectiveness of Additional Reductions in Cholesterol and SHEP : Systolic Hypertension in the Elderly Program STEMI : ST-elevation myocardial infarction SU.FOL.OM3 : SUpplementation with FOlate, vitamin B6 and B12 and/or OMega-3 fatty acids Syst-Eur : Systolic Hypertension in Europe TNT : Treating to New Targets UKPDS : United Kingdom Prospective Diabetes Study VADT : Veterans Affairs Diabetes Trial VALUE : Valsartan Antihypertensive Long-term Use VITATOPS : VITAmins TO Prevent Stroke VLDL : very low-density lipoprotein WHO : World Health Organization ### 1.1 Introduction Atherosclerotic cardiovascular disease (CVD) is a chronic disorder developing insidiously throughout life and usually progressing to an advanced stage by the time symptoms occur. It remains the major cause of premature death in Europe, even though CVD mortality has …