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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., Methods: All patients visiting the University Medical Center Utrecht for first time evaluation of a(n) (a)symptomatic vascular disease or condition were invited to participate. Routine care data was collected in the EHR and an informed consent was asked. Differences in patient characteristics were compared between consent groups. We performed multivariable logistic regression to identify determinants of non-consent. We used multinomial regression for an exploratory analysis for the determinants of non-response. Presence of CVRM indicators were compared between consent groups. A waiver (19/641) was obtained from our ethics committee., Results: Out of 5730 patients invited, 2378 were consenting, 1907 non-consenting, and 1445 non-responding. Non-consent was related to young and old age, lower education level, lower BMI, physical activity and haemoglobin levels, higher heartrate, cardiovascular disease history and absence of proteinuria. Non-response increased with young and old age, higher education level, physical activity, HbA1c and decreased with lower levels of haemoglobin, BMI, and systolic blood pressure. Presence of CVRM indicators was 5-30% lower in non-consenting patients and even lower in non-responding patients, compared to consenting patients. Non-consent and non-response varied across specialisms., Conclusions: A traditional informed consent procedure in a LHS may lead to participation bias and potentially to suboptimal CVRM, which is detrimental for feedback on findings in a LHS. This underlines the importance of reassessing the informed consent procedure in a LHS., (© 2023. The Author(s).)

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). Proportions of cardiovascular risk factor measurement before and after UCC-CVRM initiation were compared, as were proportions of patients that required (change of) blood pressure, lipid, or blood glucose lowering treatment. We estimated the likelihood to miss patients with hypertension, dyslipidemia, and elevated HbA1c before UCC-CVRM for the whole cohort and stratified for sex. In the present study, patients included up to October 2018 (n = 1904) were matched with 7195 UPOD patients with similar age, sex, department of referral and diagnose description. Completeness of risk factor measurement increased, ranging from 0% -77% before to 82%-94% after UCC-CVRM initiation. Before UCC-CVRM, we found more unmeasured risk factors in women compared to men. This sex-gap resolved in UCC-CVRM. The likelihood to miss hypertension, dyslipidemia, and elevated HbA1c was reduced by 67%, 75% and 90%, respectively, after UCC-CVRM initiation. A finding more pronounced in women compared to men. In conclusion, a systematic registration of the cardiovascular risk profile substantially improves guideline adherent assessment and decreases the risk of missing patients with elevated levels with an indication for treatment. The sex-gap disappeared after UCC-CVRM initiation. Thus, an LHS approach contributes to a more inclusive insight into quality of care and prevention of cardiovascular disease (progression)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Groenhof et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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. The aHRs were presented graphically, relative to a reference mean arterial pressure (MAP) of 100 mmHg and systolic blood pressure (sBP) of 150 mmHg., Results: A MAP below 100 mmHg in the 6, 3 and 1 h before each moment in time was associated with a decreased risk of rebleeding (e.g. within 6 h preceding rebleeding: MAP = 80 mmHg: aHR 0.30 (95% confidence interval (CI) 0.11-0.80)). A MAP below 60 mmHg in the 24 h before each moment in time was associated with an increased risk of DCI (e.g. MAP = 50 mmHg: aHR 2.59 (95% CI 1.12-5.96))., Conclusions: Our results suggest that a MAP below 100 mmHg is associated with decreased risk of rebleeding, and a MAP below 60 mmHg with increased risk of DCI., Competing Interests: Declaration of Competing Interest, (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

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. If they are unaware, researchers reusing routine laboratory data may eventually draw incorrect conclusions. In this paper, after discussing health care system characteristics on both the macro and micro level, we introduce the reader to hidden aspects of generating structured routine laboratory data in 4 steps (ordering, preanalysis, analysis, and postanalysis) and explain how each of these steps may interfere with the reuse of routine laboratory data. As researchers reusing these data, we underline the importance of domain knowledge of the health care professional, laboratory specialist, data manager, and patient to turn routine laboratory data into meaningful data sets to help obtain relevant insights that create value for clinical care., (©L Malin Overmars, Michael S A Niemantsverdriet, T Katrien J Groenhof, Mark C H De Groot, Cornelia A R Hulsbergen-Veelken, Wouter W Van Solinge, Ruben E A Musson, Maarten J Ten Berg, Imo E Hoefer, Saskia Haitjema. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.11.2022.)

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'. As a reference, we manually reviewed EHRs. Algorithm performance was assessed in an independent test set ( n  = 494, 78 129 moments) using precision, recall, and F1-score. Test set algorithm performance for 'current smoker' was precision 79.7%, recall 78.3%, and F1-score 0.79. For former smoker, it was precision 73.8%, recall 64.0%, and F1-score 0.69. For never smoker, it was precision 92.0%, recall 74.9%, and F1-score 0.83. On a patient level, performance for ever smoker (current and former smoker combined) was precision 87.9%, recall 94.7%, and F1-score 0.91. For never smoker, it was 98.0, 82.0, and 0.89%, respectively. We found a more narrative writing style in general practice than in hospital EHRs., Conclusion: Data mining can successfully retrieve smoking status information from general practice clinical notes with a good performance for classifying ever and never smokers. Differences between general practice and hospital EHRs call for optimization of data mining algorithms when applied beyond a primary development setting., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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. Initiatory analyses showed no significant effect of antihypertensive strategy on office blood pressure and 24-hour blood pressure. However, nocturnal dipping was significantly higher on RAAS inhibition and low sodium diet compared to placebo and sympathoinhibition. Optimal cardiovascular prevention after preeclampsia should be further explored., (Copyright © 2022. Published by Elsevier B.V.)

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. Thereafter, text representation methods were used to numerically represent unstructured radiology reports with vectors. Subsequently, these text representation methods were added to prediction models to assess their clinical relevance. In this step, we applied logistic regression, support vector machine (SVM), multilayer perceptron neural network, convolutional neural network, long short-term memory (LSTM), and bidirectional LSTM deep neural network (BiLSTM)., Results: We performed various experiments to evaluate the added value of the text in the prediction of major cardiovascular events. The two main scenarios were the integration of radiology reports (1) with classical clinical predictors and (2) with only age and sex in the case of unavailable clinical predictors. In total, data of 5603 patients were used with 5-fold cross-validation to train the models. In the first scenario, the multimodal BiLSTM (MI-BiLSTM) model achieved an area under the curve (AUC) of 84.7%, misclassification rate of 14.3%, and F1 score of 83.8%. In this scenario, the SVM model, trained on clinical variables and bag-of-words representation, achieved the lowest misclassification rate of 12.2%. In the case of unavailable clinical predictors, the MI-BiLSTM model trained on radiology reports and demographic (age and sex) variables reached an AUC, F1 score, and misclassification rate of 74.5%, 70.8%, and 20.4%, respectively., Conclusions: Using the case study of routine care chest X-ray radiology reports, we demonstrated the clinical relevance of integrating text features and classical predictors in our text mining pipeline for cardiovascular risk prediction. The MI-BiLSTM model with word embedding representation appeared to have a desirable performance when trained on text data integrated with the clinical variables from the SMART study. Our results mined from chest X-ray reports showed that models using text data in addition to laboratory values outperform those using only known clinical predictors., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Ayoub Bagheri et al.)

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). We compared these methods in a case study evaluating the root mean squared error (RMSE) and coverage of the 95% confidence intervals (i.e., the proportion of confidence intervals that contain the true predictor value) of imputed predictor values., Results: -RMSE was lowest when adopting JMI or CMI, although imputation of individual predictors did not always lead to substantial improvements as compared to mean imputation. JMI and CMI appeared particularly useful when the values of multiple predictors of the model were missing. Coverage reached the nominal level (i.e., 95%) for both CMI and JMI., Conclusion: Multiple imputations using either CMI or JMI is recommended when dealing with missing predictor values in real-time settings., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel., Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%)., Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

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. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modelling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model's overall performance [mean squared error (MSE) of linear predictor], discrimination (c-index), calibration (intercept and slope), and net benefit (decision curve analysis). When compared with mean imputation, JMI substantially improved the MSE (0.10 vs. 0.13), c-index (0.70 vs. 0.68), and calibration (calibration-in-the-large: 0.04 vs. 0.06; calibration slope: 1.01 vs. 0.92), especially when incorporating auxiliary variables. When the imputation method was based on an external cohort, calibration deteriorated, but discrimination remained similar., Conclusions: We recommend JMI with auxiliary variables for real-time imputation of missing values, and to update imputation models when implementing them in new settings or (sub)populations., (© The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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)., Design & Setting: In this prospective cohort study, data were linked from the Utrecht Cardiovascular Cohort (UCC) to the Julius General Practitioners' Network (JGPN) database. UCC offers structured CVRM at referral to the University Medical Centre (UMC) Utrecht. JGPN consists of electronic health record (EHR) data from referring GPs., Method: The cardiovascular risk factors were extracted for each patient 13 months before referral (JGPN), at UCC inclusion, and during 12 months follow-up (JGPN). The following areas were assessed: registration of risk factors; detection of risk factor(s) requiring treatment at UCC; communication of risk factors and actionable suggestions from the specialist to the GP; and change of management during follow-up., Results: In 52% of patients, ≥1 risk factors were registered (that is, extractable from structured fields within routine care health records) before UCC. In 12%-72% of patients, risk factor(s) existed that required (change or start of) treatment at UCC inclusion. Specialist communication included the complete risk profile in 67% of letters, but lacked actionable suggestions in 86%. In 29% of patients, at least one risk factor was registered after UCC. Change in management in GP records was seen in 21%-58% of them., Conclusion: Evaluation of a multidisciplinary LHS is possible via linkage of health data sources. Efforts have to be made to improve registration in primary care, as well as communication on findings and actionable suggestions for follow-up to bridge the gap in the CVRM continuum., (Copyright © 2020, The Authors.)

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. Furthermore, we included studies of nulliparous KT recipients., Results: Our search yielded 38 studies on GL and 18 studies on SCr. The pooled incidence of GL was 9.4% within 2 years after pregnancy, 9.2% within 2-5 years, 22.3% within 5-10 years, and 38.5% >10 years postpartum. In addition, our data show that, in case of graft survival, SCr remains stable over the years. Only within 2 years postpartum, Δ SCr was marginally higher (0.18 mg/dL, 95%CI [0.05-0.32], P = 0.01). Furthermore, no differences in GL were observed in 10 studies comparing GL after pregnancy with nulliparous controls. Systematic review of the literature showed that mainly prepregnancy proteinuria, hypertension, and high SCr are risk factors for GL., Conclusions: Overall, these data show that pregnancy after KT has no effect on long-term graft survival and only a possible effect on graft function within 2 years postpartum. This might be due to publication bias. No significant differences were observed between pre- and postpartum SCr at longer follow-up intervals.

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. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement., Results: Between February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management., Conclusions: Routine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback., (©T Katrien J Groenhof, Daniel Kofink, Michiel L Bots, Hendrik M Nathoe, Imo E Hoefer, Wouter W Van Solinge, A Titia Lely, Folkert W Asselbergs, Saskia Haitjema. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.04.2020.)

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. As a reference standard, we used the questionnaire from the Utrecht Cardiovascular Cohort (UCC). To assess diagnostic accuracy, we calculated sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV)., Results: We analyzed 1,661 patients included in the UCC to January 18, 2019. Of those, 14% (n = 238) had missing information on smoking status in the UCC questionnaire. Data mining provided information on smoking status in 99% of the 1,661 participants. Diagnostic accuracy for current smoking was sensitivity 88%, specificity 92%, NPV 98%, and PPV 63%. From false positives, 85% reported they had quit smoking at the time of the UCC., Conclusion: Data mining showed great potential in retrieving information on smoking (a near complete yield). Its diagnostic performance is good for negative smoking statuses. The implications of misclassification with data mining are dependent on the application of the data., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Selection Criteria: Studies comparing cardiometabolic markers in 2-18-year-old children of mothers with HDP in utero, to children of mothers without HDP., Data Collection and Analysis: Sixteen studies reported in 25 publications were included in this systematic review, of which three were considered as having high risk of bias. Thus 13 studies were included in the evidence synthesis: respectively two and eight reported pregnancy induced hypertension and preeclampsia, and three studies reported on both HDPs., Main Results: Most studies ( n  = 4/5) found a higher blood pressure in children exposed to pregnancy induced hypertension. Most studies ( n  = 7/10) found no statistically significantly higher blood pressure in children exposed to preeclampsia. No association was found between exposure to HDP and levels of cholesterol, triglycerides or glucose ( n  = 5/5). No studies investigated an association with (carotid) intima-media thickness, glycated haemoglobin or diabetes mellitus type 2., Conclusions: Most studies showed that exposure to pregnancy induced hypertension is associated with a higher offspring blood pressure. There is no convincing evidence for an association between exposure to preeclampsia and blood pressure in childhood. Based on current evidence, exposure to HDP is not associated with blood levels of cholesterol, triglycerides and glucose in childhood.

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 CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation., Conclusion: Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.

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., Methods: We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I 2  < 70%), pooled the results using a random-effects model., Results: Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. Among CVRM patients, the results tended towards a beneficial effect of CDSS, but only LDL-c target attainment in diabetes patients reached statistical significance. Prompting, integration into the electronical health record, patient empowerment, and medication support were related to more effective CVRM., Conclusion: We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment. Some features of CDSS seem more promising than others. However, the variability in CDSS characteristics and heterogeneity of the results - emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.

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. We identified women with a history of a normotensive pregnancy (n=1811) or an HDP (n=1005) within a high-risk population-based cohort study. We assessed changes in blood pressure, lipids, glucose, 10-year cardiovascular risk and the occurrence of hypertension, dyslipidemia, and diabetes mellitus longitudinally using 5 measurements at 3-year intervals. Generalized estimating equations were used for statistical analysis, with age as the time variable, adjusting for multiple comparisons using the least significant differences method. In women with an HDP, the overall prevalence of hypertension ( P<0.0001), dyslipidemia ( P=0.003), and diabetes mellitus ( P<0.0001) was significantly higher. They also developed hypertension and diabetes mellitus earlier. At age 35, few women with HDP need to be screened to detect clinically relevant hypertension: 9 need to be screened to detect 1 woman with a treatment indication as opposed to 38 women with history of a normotensive pregnancy. Our data supports cardiovascular follow-up of women with a history of an HDP starting within the fourth decade of life.

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. Methods The aim of our study was to provide an overview of existing knowledge on the changes over time in three major modifiable components of cardiovascular risk assessment after HDP: blood pressure, glucose homeostasis and lipid levels. Data from 44 studies and up to 6904 women with a history of a HDP were compared with risk factor levels reported for women of corresponding age in the National Health And Nutrition Examination Survey, Estudio Epidemiólogico de la Insuficiencia Renal en España and Hong Kong cohorts ( N = 27,803). Results Compared with the reference cohort, women with a HDP presented with higher mean blood pressure. Hypertension was present in a higher rate among women with a previous HDP from 15 years postpartum onwards. At 15 years postpartum (±age 45), one in five women with a history of a HDP suffer from hypertension. No differences in glucose homeostasis parameters or lipid levels were observed. Conclusions Based on our analysis, it is not possible to point out a time point to commence screening for cardiovascular risk factors in women after a HDP. We recommend redirection of future research towards the development of a stepwise approach identifying the women with the highest cardiovascular risk.