Abstract
PURPOSE: The diagnosis of chronic lymphocytic leukemia (CLL) is often delayed several years in advance of disease. Addressing this care gap would aid in identifying at-risk patients who may benefit from targeted evaluation to prevent adverse outcomes. To our knowledge, to date, however, there are no widely utilized machine learning (ML) models that predict development of CLL. Therefore, the objective of this study was to leverage readily available laboratory data to train and test the performance of ML-based risk models for abnormal lymphocytosis associated with CLL. METHODS: The observational study population was composed of deidentified laboratory data procured from a large US outpatient network. The 7-year longitudinal data set included 1,090,707 adult patients with the following inclusion criteria: age 50 to 75 years and initial absolute lymphocyte count (ALC) <5 × 10(9)/L. The data set was split into training and held-out test sets, where 80% of the data were used in training and 20% were used for independent testing. ML models were developed using random forest survival methods. The ground truth outcome was abnormal lymphocytosis associated with CLL and monoclonal B-cell lymphocytosis diagnosis: ALC ≥5 × 10(9)/L with ≥40% relative lymphocytosis. RESULTS: The 12-variable risk classifier model accurately predicted ALC ≥5 × 10(9)/L within 5 years and achieved an area under the curve receiver operating characteristic of 0.92. The most important predictors were ALC (initial, slope), WBC (last, max, slope, initial), platelet (last, slope, max, initial), age, and sex. CONCLUSION: Our ML risk classifier accurately predicts abnormal lymphocytosis associated with CLL using routine laboratory data. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition for patients at a risk of CLL.