External validation and updating of a Dutch prediction model for low hemoglobin deferral in Irish whole blood donors. Issue 3 (22nd April 2013)

• Record Type: Journal Article
• Authors: Baart, A. Mireille; Atsma, Femke; McSweeney, Ellen N.; Moons, Karel G.M.; Vergouwe, Yvonne; de Kort, Wim L.A.M.
• Is Part Of: Transfusion. Volume 54:Issue 3:Part 2(2014)
• Page Start: 762
• Page End: 769
• Publication Date: 2013-04-22
• Subjects: Hematology -- Periodicals; Blood -- Transfusion -- Periodicals; Blood Group Antigens -- Periodicals; Blood Preservation -- Periodicals; Blood Transfusion -- Periodicals; 615
• DOI: 10.1111/trf.12211
• Languages: English
• ISSNs: 0041-1132

Abstract

Abstract : Background: Recently, sex‐specific prediction models for low hemoglobin (Hb) deferral have been developed in Dutch whole blood donors. In the present study, we validated and updated the models in a cohort of Irish whole blood donors. Study Design and Methods: Prospectively collected data from 45, 031 Irish whole blood donors were used. Hb cutoff levels for donation were approximately 0.35 mmol/L lower in Ireland than the Dutch 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 Dutch cohort. The strength of most predictive factors, particularly previous Hb level, was lower in Irish 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 Dutch 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.