ObjectivesAn inadequate intake of calcium in pregnancy is associated with higher risks of maternal hypertensive pregnancy disorders, premature birth and intrauterine growth restriction. An evidence based and clinically applicable tool to efficiently estimate the adequacy of calcium intake in pregnant women currently does not exist. The aim of this study is to develop an effective and simple digital screening tool for calcium intake in pregnancy. Study DesignWe extracted all data from the Rotterdam Periconceptional cohort (PREDICT study) conducted at the Erasmus MC, University Medical Centre in Rotterdam, the Netherlands, between November 2014 and December 2020. Data was extracted from food frequency questionnaires. The estimated average requirement of 750 m/day was defined as the lower limit for an adequate calcium intake. We created a prediction model, using multivariable binary logistic regression with backward stepwise selection, predicting the probability of adequate calcium intake. We developed a simple screening tool based on the prediction model. Results694 participants are included, of which 201 (29 %) had an adequate calcium intake. Total daily or weekly intakes of cheese, milk, and yogurt or curd were selected as predictors for the prediction model. The model had excellent discrimination (AUC 0.858), a good fit (Brier score 0.136, HL statistic p = 0.499) and satisfactory calibration. The test accuracy measures were: sensitivity 80.9 %, specificity 77.1 %, PPV 89.7 %, NPV 62.2 %. A colour coded digital screening tool was developed for use in clinical practice. ConclusionsThis evidence-based and simple screening tool is a reliable and efficient instrument to predict inadequate calcium intakes in pregnancy, which can easily be incorporated in daily clinical practice and existing pregnancy coaching platforms.
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