Neonatal Adverse Events' Trigger Tool Setup With Random Forest.

Objective: This study aimed to develop a trigger tool for detection of neonatal adverse events (AEs) and to validate its effectiveness.

Study Design: Random forest (RF) algorithm was used to build the predictive model by analyzing data from the medical records of 782 neonates in our previous study. Thirteen variables for each patient were used to predict neonatal AEs. Next, the critical variables were selected based on recursive elimination of variables to form the list of triggers. Then, a trigger tool with those triggers was established and tested by reviewing medical records. The positive predictive value of individual triggers and of the entire tool was evaluated.

Results: Data from 782 neonates, including 297 patients with and 485 patients without AEs, were collected to build the original RF model. Then, the 6 most important variables, including diarrhea, antibiotic use, fever, death, skin damage, and suspected necrotizing enterocolitis, were selected to establish a neonate-focused trigger tool. The forest with the 6 variables predicted AEs with a sensitivity of 70.7%, a specificity of 92.0%, and an error rate of 16.1%. In a validation study of the trigger tool, 655 neonates with birth weights ≥1500 g were enrolled, and review of their medical records revealed 1709 triggers and 1172 unique AEs. The 3 most common AEs identified were skin damage, iatrogenic diarrhea, and environmental factor-related fever. The total positive predictive value of the trigger tool was 0.686.

Conclusions: The neonate-focused trigger tool developed using the RF algorithm efficiently and reliably identifies AEs among hospitalized neonates with birth weights ≥1500 g.