Differential diagnosis of bone marrow failure syndromes guided by machine learning

The choice to postpone treatment while awaiting genetic testing can result in significant delay in definitive therapies in severely pancytopenic patients. Conversely, inherited bone marrow failure (BMF) misdiagnosis can expose patients to ineffectual and expensive therapies, toxic transplant conditioning regimens, and inappropriate use of an affected family member as a stem cell donor. To predict the likelihood of patients having acquired or inherited BMF, we developed a two-step data-driven machine-learning model using 25 clinical and laboratory variables typically recorded at the initial clinical encounter. For model's development, patients were labeled as having acquired or inherited BMF depending on their genomic data. Datasets were unbiasedly clustered and an ensemble model was trained with cases from the largest cluster of the training cohort (n=359) and validated with an independent cohort (n=127). Cluster A, the largest group, was mostly immune or inherited aplastic anemia, whereas Cluster B was composed of underrepresented BMF phenotypes, and not included in the next step of data modeling due to small sample size. The ensemble model Cluster A-specific was accurate (89%) to predict BMF etiology, correctly predicting inherited and likely immune BMF in 79% and 92% of cases, respectively. Our model represents a practical guide for BMF diagnosis and highlights the importance of clinical and laboratory variables in the initial evaluation, particularly telomere length. Our tool can be potentially used by general hematologists and health care providers not specialized in BMF, and in under-resourced centers, to prioritize patients for genetic testing or for expeditious treatment.