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External validation and updating of a D utch prediction model for low hemoglobin deferral in I rish whole blood donors
Author(s) -
Baart A. Mireille,
Atsma Femke,
McSweeney Ellen N.,
Moons Karel G.M.,
Vergouwe Yvonne,
Kort Wim L.A.M.
Publication year - 2014
Publication title -
transfusion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.045
H-Index - 132
eISSN - 1537-2995
pISSN - 0041-1132
DOI - 10.1111/trf.12211
Subject(s) - deferral , concordance , medicine , confidence interval , cutoff , cohort , statistic , predictive modelling , whole blood , hemoglobin , surgery , statistics , mathematics , physics , accounting , quantum mechanics , business
Background Recently, sex‐specific prediction models for low hemoglobin ( Hb ) deferral have been developed in D utch whole blood donors. In the present study, we validated and updated the models in a cohort of I rish whole blood donors. Study Design and Methods Prospectively collected data from 45,031 I rish whole blood donors were used. Hb cutoff levels for donation were approximately 0.35 mmol/ L lower in I reland than the D utch cutoff levels (8.07 mmol/ L vs. 8.40 mmol/ L in men; 7.45 mmol/ L vs. 7.80 mmol/ L in women). The predictive performance of the models was assessed with calibration plots, calibration‐in‐the‐large, and the concordance (c)‐statistic. The models were updated by revising the strength of the individual predictors in the models. Results A total of 613 men (2.4%) and 1624 women (8.4%) were deferred from donation because of a low Hb level. Validation demonstrated underestimation of predicted risks and lower c‐statistics for men and women compared to the D utch cohort. The strength of most predictive factors, particularly previous Hb level, was lower in I rish donors. The updated models showed a c‐statistic of 0.83 (95% confidence interval [ CI ], 0.81‐0.84) for men and 0.76 (95% CI , 0.74‐0.77) for women. Conclusion The performance of D utch prediction models for Hb deferral was limited when validated in Irish whole blood donors. Updating the models resulted in different predictor effects. This improved mainly the model calibration; the improvement in discrimination was small.