Risk factors for pediatric acute chest syndrome using machine learning techniques.

abstractAcute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric patients with sickle cell disease. Despite known risk factors for developing ACS, this complication is often unexpected. We hypothesized that a machine learning model could predict progression to ACS in children. A random forest model was used using the Scikit-Learn Python library. Prediction performance was assessed using the C-statistic, sensitivity, and specificity. The features included are demographics, vital signs, laboratory values, medication orders, and additional comorbidities. The positive cohort developed ACS on diagnosis or during admission, and the negative cohort did not. A total of 3223 encounters were included in the model for training and testing. Up to 65% of the positive cohort developed ACS 24 hours after admission. The model had a sensitivity of 65.1% and specificity of 77.4% at the cutoff point of 0.6. For diagnosing ACS, the negative predictive value was 89.8%, and the positive predictive value was 42.3%. For the correct positive cases, the model predicted ACS a median of 18 hours before the diagnosis was determined in the electronic health record. The features that were important included pulse, respiratory rate, presence of hypoxia, temperature, neutrophil count, and use of pain medications. Despite the model's limitations, it is, to our knowledge, the first model using machine learning techniques to predict ACS in children. Detecting patients who are at higher risk of ACS could affect the approach to acute care management.