Asking informed consent may lead to significant participation bias and suboptimal cardiovascular risk management in learning healthcare systems.

Background: The Utrecht Cardiovascular Cohort - CardioVascular Risk Management (UCC-CVRM) was set up as a learning healthcare system (LHS), aiming at guideline based cardiovascular risk factor measurement in all patients in routine clinical care. However, not all patients provided informed consent, which may lead to participation bias. We aimed to study participation bias in a LHS by assessing differences in and completeness of cardiovascular risk management (CVRM) indicators in electronic health records (EHRs) of consenting, non-consenting, and non-responding patients, using the UCC-CVRM as an example.

Optimizing cardiovascular risk assessment and registration in a developing cardiovascular learning health care system: Women benefit most.

Since 2015 we organized a uniform, structured collection of a fixed set of cardiovascular risk factors according the (inter)national guidelines on cardiovascular risk management. We evaluated the current state of a developing cardiovascular towards learning healthcare system-the Utrecht Cardiovascular Cohort Cardiovascular Risk Management (UCC-CVRM)-and its potential effect on guideline adherence in cardiovascular risk management. We conducted a before-after study comparing data from patients included in UCC-CVRM (2015-2018) and patients treated in our center before UCC-CVRM (2013-2015) who would have been eligible for UCC-CVRM using the Utrecht Patient Oriented Database (UPOD).

Data mining to retrieve smoking status from electronic health records in general practice.

Aims: Optimize and assess the performance of an existing data mining algorithm for smoking status from hospital electronic health records (EHRs) in general practice EHRs.

Methods And Results: We optimized an existing algorithm in a training set containing all clinical notes from 498 individuals (75 712 contact moments) from the Julius General Practitioners' Network (JGPN). Each moment was classified as either 'current smoker', 'former smoker', 'never smoker', or 'no information'.

A Wolf in Sheep's Clothing: Reuse of Routinely Obtained Laboratory Data in Research.

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data.

Blood pressure and the risk of rebleeding and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

Introduction And Objective: Blood pressure is presumably related to rebleeding and delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (aSAH) and could serve as a target to improve outcome. We assessed the associations between blood pressure and rebleeding or DCI in aSAH-patients.

Materials And Methods: In this observational study in 1167 aSAH-patients admitted to the intensive care unit (ICU), adjusted hazard ratio's (aHR) were calculated for the time-dependent association of blood pressure and rebleeding or DCI.

Differential effects of renin-angiotensine-aldosteron system inhibition, sympathoinhibition and low sodium diet on blood pressure in women with a history of preeclampsia: A double-blind, placebo-controlled cross-over trial (the PALM study).

Current guidelines lack sufficient evidence to recommend a specific blood pressure lowering strategy to prevent cardiovascular disease after preeclampsia. We conducted a double-blind cross-over trial to identify the most potent antihypertensive strategy: renin-angiotensin-aldosterone system (RAAS) inhibition (losartan), sympathoinhibition (moxonidine), low sodium diet and placebo (n = 10). Due to low inclusion rate our study stopped prematurely.

Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports.

Methods: We used EHR data of patients included in the Second Manifestations of ARTerial disease (SMART) study. We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of major cardiovascular events in cardiovascular patients. Text preprocessing, including cleaning and stemming, was first applied to filter out the unwanted texts from X-ray radiology reports.

Real-time imputation of missing predictor values improved the application of prediction models in daily practice.

Objectives: In clinical practice, many prediction models cannot be used when predictor values are missing. We, therefore, propose and evaluate methods for real-time imputation.

Study Design And Setting: We describe (i) mean imputation (where missing values are replaced by the sample mean), (ii) joint modeling imputation (JMI, where we use a multivariate normal approximation to generate patient-specific imputations), and (iii) conditional modeling imputation (CMI, where a multivariable imputation model is derived for each predictor from a population).

Real-time imputation of missing predictor values in clinical practice.

Aims: Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice.

Methods And Results: We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics.

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study.

Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.

Study Design And Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel.

Evaluating a cardiovascular disease risk management care continuum within a learning healthcare system: a prospective cohort study.

Background: Many patients now present with multimorbidity and chronicity of disease. This means that multidisciplinary management in a care continuum, integrating primary care and hospital care services, is needed to ensure high quality care.

Aim: To evaluate cardiovascular risk management (CVRM) via linkage of health data sources, as an example of a multidisciplinary continuum within a learning healthcare system (LHS).

Long-term Graft Survival and Graft Function Following Pregnancy in Kidney Transplant Recipients: A Systematic Review and Meta-analysis.

Background: The incidence of pregnancy in kidney transplantation (KT) recipients is increasing. Studies report that the incidence of graft loss (GL) during pregnancy is low, but less data are available on long-term effects of pregnancy on the graft.

Methods: Therefore, we performed a meta-analysis and systematic review on GL and graft function, measured by serum creatinine (SCr), after pregnancy in KT recipients, stratified in years postpartum.

Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data.

Background: Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice.

Objective: This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center.

Methods: We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht.

Data mining information from electronic health records produced high yield and accuracy for current smoking status.

Objectives: Researchers are increasingly using routine clinical data for care evaluations and feedback to patients and clinicians. The quality of these evaluations depends on the quality and completeness of the input data.

Study Design And Setting: We assessed the performance of an electronic health record (EHR)-based data mining algorithm, using the example of the smoking status in a cardiovascular population.

A computerised decision support system for cardiovascular risk management 'live' in the electronic health record environment: development, validation and implementation-the Utrecht Cardiovascular Cohort Initiative.

Purpose: We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare.

Methods: We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS.

Results: In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides 'live' information on CVRM within the environment of the EHR.

The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis.

Background: Cardiovascular risk management (CVRM) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease. Computerized decision support systems (CDSS) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies. The effect on cardiovascular risk factor management is yet to be confirmed.

Hypertensive disorders of pregnancy and cardiometabolic outcomes in childhood: A systematic review.

Background: Hypertensive disorders of pregnancy (HDPs) are among the leading causes of maternal and perinatal morbidity and mortality worldwide and have been suggested to increase long-term cardiovascular disease risk in the offspring.

Objective: The objective of this study was to investigate whether HDPs are associated with cardiometabolic markers in childhood.

Search Strategy: PubMed, The Cochrane Library and reference lists of included studies up to January 2019.

Trajectory of Cardiovascular Risk Factors After Hypertensive Disorders of Pregnancy.

Women with a history of a hypertensive disorder of pregnancy (HDP) are at increased risk of premature cardiovascular disease. Cardiovascular risk management guidelines emphasize the need for prevention of cardiovascular disease in these women but fail to provide uniform recommendations on when and how to start cardiovascular risk assessment. The aim of this study was to identify a window of opportunity in which to start cardiovascular risk factor assessment by investigating changes in blood pressure, lipids, and fasting glucose levels over time in women with a history of an HDP.

eHealth as the Next-Generation Perinatal Care: An Overview of the Literature.

Background: Unrestricted by time and place, electronic health (eHealth) provides solutions for patient empowerment and value-based health care. Women in the reproductive age are particularly frequent users of internet, social media, and smartphone apps. Therefore, the pregnant patient seems to be a prime candidate for eHealth-supported health care with telemedicine for fetal and maternal conditions.

Preventing cardiovascular disease after hypertensive disorders of pregnancy: Searching for the how and when.

Background Women with a history of a hypertensive disorder during pregnancy (HDP) have an increased risk of cardiovascular events. Guidelines recommend assessment of cardiovascular risk factors in these women later in life, but provide limited advice on how this follow-up should be organized. Design Systematic review and meta-regression analysis.