Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

Hamoen, Marleen; Vergouwe, Yvonne; Wijga, Alet H.; Heymans, Martijn W.; Jaddoe, Vincent W. V.; Twisk, Jos W. R.; Raat, Hein; Kroon, Marlou L. A. de

doi:10.1136/bmjopen-2018-023912

Cited by 5 publications

(4 citation statements)

References 49 publications

(46 reference statements)

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“…LDL cholesterol was calculated according to the Friedewald formula. Systolic and diastolic blood pressure (SBP and DBP) were measured using a sphygmomanometer (Datascope Accutorr Plus) [ 16 ]. Blood pressure was measured at the right brachial artery for four times with intervals of 1 min and the mean was calculated using the last three measurements of each participant [ 5 , 16 ].…”

Section: Methodsmentioning

confidence: 99%

“…Systolic and diastolic blood pressure (SBP and DBP) were measured using a sphygmomanometer (Datascope Accutorr Plus) [ 16 ]. Blood pressure was measured at the right brachial artery for four times with intervals of 1 min and the mean was calculated using the last three measurements of each participant [ 5 , 16 ]. Age- and sex-specific SD scores (SDS) were calculated for all individual outcome variables.…”

Section: Methodsmentioning

confidence: 99%

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Diet quality and cardiometabolic health in childhood: the Generation R Study

et al. 2021

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Purpose Diet is an important determinant of cardiometabolic disease risk in adults. We aimed to study associations of diet quality with cardiometabolic health in school-age children. Methods This study was embedded in the Generation R Study a prospective population-based cohort in Rotterdam, the Netherlands and included 3991 children. Food intake was assessed with a Food-Frequency Questionnaire at age 8 years. A diet quality score (0–10) was calculated reflecting adherence to age-specific dietary guidelines. The following outcome variables were measured at age 10 years and used to create a continuous cardiometabolic risk factor score: body fat percentage, insulin, triglycerides, HDL cholesterol, and systolic and diastolic blood pressure. Outcomes were expressed in age- and sex-specific standard deviation scores (SDS). Multivariable linear regression models were used to assess associations between the diet quality score and the cardiometabolic risk factor score and with the individual cardiometabolic risk factors. Results In models adjusted for socioeconomic and lifestyle factors and BMI, a higher diet quality was associated with a lower cardiometabolic risk factor score [− 0.08 per point higher diet score, (95% CI − 0.15, − 0.001)]. This association was mainly driven by associations of higher diet quality with lower systolic [− 0.04 SD (95% CI − 0.06, − 0.01)] and diastolic blood pressure [− 0.05 SD, (95% CI − 0.07, − 0.02)]. No statistically significant associations were found for insulin, triglycerides, HDL cholesterol, or body fat percentage as individual factors. Conclusions We found an association between higher diet quality and better cardiometabolic health in childhood, mainly driven by a lower blood pressure. Further research is needed to explore associations of diet quality in childhood with long-term cardiometabolic health.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Diet quality and cardiometabolic health in childhood: the Generation R Study

et al. 2021

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“… refund in MFPCA R package (FPC), merlin package on R (ME models). Extends to TDCM [ 111 ], and LA [ 112 ], calibration error included in stage II [ 52 , 60 ]. In conjunction with LA, TSM used to predict adverse events following endovascular abdominal aortic aneurysm repair [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

et al. 2020

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Background: Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. Methods: MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. Results: The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. Conclusions: The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

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“…Thereby, predictability of the 3-year dynamic survival is higher in LFM. Other studies that worked on blood pressure have considered landmarks separately while we used a model that landmarks were considered continuously [21,33,34]. This study showed that landmark models can be used to help clinicians to make better decision for diagnosis and treatment.…”

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confidence: 99%

Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data

Sayadi

Zare

Attar

et al. 2020

BioMed Research International

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Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

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Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study

Cited by 5 publications

References 49 publications

Diet quality and cardiometabolic health in childhood: the Generation R Study

Diet quality and cardiometabolic health in childhood: the Generation R Study

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data

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