Development and internal validation of a clinical prediction model for the diagnosis of immune thrombocytopenia
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abstract
BACKGROUND: Immune thrombocytopenia (ITP) is a diagnosis of exclusion that can resemble other thrombocytopenic disorders. OBJECTIVES: To develop a clinical prediction model (CPM) for the diagnosis of ITP to aid hematogists in investigating patients presenting with undifferentiated thrombocytopenia. METHODS: We designed a CPM for ITP diagnosis at the time of the initial hematology consultation using penalized logistic regression based on data from patients with thrombocytopenia enrolled in the McMaster ITP registry (n = 523) called the Predict-ITP Tool. The case definition for ITP was a platelet count less than 100 × 109 /L and a platelet count response after high-dose corticosteroids or intravenous immune globulin, defined as the achievement of a platelet count above 50 × 109 /L and at least a doubling of baseline. Internal validation was done using bootstrap resampling. Model discrimination was assessed by the c-statistic, and calibration was assessed by the calibration slope, calibration-in-the-large, and calibration plot. RESULTS: The final model included the following variables: (1) platelet count variability (based on three or more platelet count values), (2) lowest platelet count value, (3) maximum mean platelet volume, and (4) history of major bleeding (defined by the ITP bleeding scale). The optimism-corrected c-statistic was 0.83, the calibration slope was 0.88, and calibration-in-the-large for all performance measures was <0.001 with standard error <0.001, indicating good discrimination and excellent calibration. CONCLUSIONS: The Predict-ITP Tool can estimate the likelihood of ITP for a given patient with thrombocytopenia at the time of the initial hematology consultation. The tool had high predictive accuracy for the diagnosis of ITP.
