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Lifestyle behaviours, ethnicity and menstruation have little added value in prediction models for low haemoglobin deferral in whole blood donors
Author(s) -
Baart A. Mireille,
Timmer Tiffany,
Kort Wim L. A. M.,
Hurk Katja
Publication year - 2020
Publication title -
transfusion medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.471
H-Index - 59
eISSN - 1365-3148
pISSN - 0958-7578
DOI - 10.1111/tme.12651
Subject(s) - deferral , concordance , medicine , statistic , predictive modelling , menstruation , ethnic group , demography , statistics , mathematics , accounting , sociology , business , anthropology
Summary Objective To investigate the added value of questionnaire‐based predictors to existing prediction models for low haemoglobin (Hb) deferral in whole blood donors. Background Prediction models for Hb deferral risk can be applied in the invitation process of donors for a blood donation. Existing prediction models are based on routinely collected data. The model performance might be improved by the addition of predictive factors. Methods The added value of food consumption, smoking, physical activity, ethnicity and menstruation in the prediction of Hb deferral was assessed by comparing the existing models with extended models using the following measures: model X 2 , concordance (c)‐statistic and net reclassification improvement (NRI). Results Addition of one candidate predictor to the models did not substantially improve the model performance. Addition of multiple new candidate predictors significantly increased the model X 2 (from 137 to 159 for men, and from 157 to 199 for women) and resulted in a non‐significant increase of the c‐statistic (from 0.85 to 0.87 for men, and from 0.78 to 0.81 for women). The NRI for men was 11.4% and for women 1.5% after addition of multiple predictors. Conclusion Addition of lifestyle behaviours, ethnicity or menstruation to prediction models for low Hb deferral in whole blood donors improved the model performance, but not substantially. For easy use in practice, we do not recommend addition of the investigated predictors to the prediction models.