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首页> 外文期刊>Transfusion medicine >Lifestyle behaviours, ethnicity and menstruation have little added value in prediction models for low haemoglobin deferral in whole blood donors
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Lifestyle behaviours, ethnicity and menstruation have little added value in prediction models for low haemoglobin deferral in whole blood donors

机译：生活方式行为，种族和月经在整个献血者中的低血红蛋白延迟的预测模型中几乎没有附加价值

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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.
机译：发明内容目的探讨问卷的预测因子对整个献血者低血红蛋白（HB）延长的现有预测模型的附加值。用于HB延迟风险的背景预测模型可以应用于献血者的邀请过程中。现有预测模型基于常规收集的数据。通过添加预测因素可以提高模型性能。方法通过使用以下措施将现有模型与扩展模型进行比较，评估食品消费，吸烟，身体活动，种族和月经的附加值，吸烟，体育活动，种族和月经，通过使用以下措施将现有模型与扩展模型进行比较：X 2，Concordance（C） - 级和净重新分类改善（NRI）。结果对模型的一个候选预测器的增加没有大幅提高模型性能。添加多个新候选预测因子的增加显着增加了X 2模型（男性的137到159，女性的157至199人），导致C统计量的非显着增加（男性为0.85〜0.87，女性的0.78至0.81）。在加入多个预测因子后，男性的NRI为11.4％，女性为1.5％。结论在整个献血者中添加生活方式行为，种族或月经到低HB延迟的预测模型提高了模型性能，但基本上没有。在实践中轻松使用，我们不建议将调查的预测因子添加到预测模型中。

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