External validation of a prediction model for diagnosing immune thrombocytopenia

There is no reliable way of diagnosing immune thrombocytopenia (ITP), which leads to delays in care, use of incorrect treatments, and increased patient anxiety. We developed the Predict-ITP Tool to classify patients as ITP or non-ITP, based on the following: 1) fluctuations in platelet counts over time; 2) lowest platelet count in the recent past; 3) highest mean platelet volume; and 4) a major bleed at any time in the past. Our preliminary internal validation study showed promise.

The objective of this project is to validate the Predict-ITP Tool by collecting data from 960 patients from 11 clinics across Canada to see how accurately the tool would have classified patients as ITP or non-ITP at the first hematology visit. We will compare these results with the diagnosis determined by the hematologist over time. An independent expert committee will adjudicate uncertain diagnoses.

There is a global shortage of a treatment for ITP called immunoglobulins. Since ITP is difficult to diagnose, some patients receive immunoglobulins unnecessarily. The Predict-ITP Tool identifies patients with ITP to make sure immunoglobulins are used appropriately. Over time, the tool will improve diagnostic accuracy, reduce delays in diagnosis and care, and spare patients of unnecessary tests and treatments.

ARNOLD, Donald

McMaster University

Graduate Fellowship Program

Ontario

$70,000