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Table 4 Predictive model performance for specific medical conditions

From: Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity

Model1 GI bleed Infection Cardiovascular Malignancy Orthopedic surgery
N=12,388 N=57,473 N=47,996 N=27,831 N=24,264
C-statistic Pseudo-R2 C-statistic Pseudo-R2 C-statistic Pseudo -R2 C-statistic Pseudo -R2 C-statistic Pseudo-R2
Administrative Data2 0.587 0.032 0.616 0.038 0.666 0.058 0.828 0.379 0.686 0.121
(a) + Admission Hemoglobin 0.862 0.543 0.839 0.399 0.852 0.419 0.875 0.538 0.696 0.146
(b) + Severity of Illness 0.884 0.566 0.851 0.406 0.866 0.425 0.880 0.547 0.699 0.151
(c) + Prior RBC Transfusion 0.887 0.570 0.862 0.419 0.873 0.432 0.884 0.556 0.709 0.162
(d) + Initial Hospital Location 0.896 0.590 0.871 0.432 0.877 0.440 0.885 0.558 0.710 0.162
(e) + Hospital 0.900 0.599 0.875 0.441 0.884 0.451 0.890 0.563 0.729 0.191
  1. Footnotes
  2. 1Model performance in this table is measured using the area under the receiver operator characteristic curve (C-statistic) and Nagelkerke’s Pseudo R2.
  3. 2Administrative data includes age, sex, comorbid conditions (COPS2), admission type (emergency or elective), and admission diagnosis.
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BMC Health Services Research

ISSN: 1472-6963

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