Prediction Algorithm for Surgical Intervention in Neonatal Brachial Plexus Palsy

Neonatal brachial plexus palsy (NBPP) results in reduced function of the affected arm with profound ramifications on quality of life. Advances in surgical technique have shown improvements in outcomes for appropriately selected patients. Patient selection, however, remains difficult.

To develop a decision algorithm that could be applied at the individual patient level, early in life, to reliably predict persistent NBPP that would benefit from surgery.

Retrospective review of NBPP patients was undertaken. Maternal and neonatal factors were entered into the C5.0 statistical package in R (The R Foundation). A 60/40 model was employed, whereby 60% of randomized data were used to train the decision tree, while the remaining 40% were used to test the decision tree. The outcome of interest for the decision tree was a severe lesion meeting requirements for surgical candidacy.

A decision tree prediction algorithm was generated from the entered variables. Variables utilized in the final decision tree included presence of Horner's syndrome, presence of a pseudomeningocele, Narakas grade, clavicle fracture at birth, birth weight >9 lbs, and induction or augmentation of labor. Sensitivity of the decision tree was 0.71, specificity 0.96, positive predictive value 0.94, negative predictive value 0.79, and F1 score 0.81.

We developed a decision tree prediction algorithm that can be applied shortly after birth to determine surgical candidacy of patients with NBPP, the first of its kind utilizing only maternal and neonatal factors. This conservative decision tree can be used to offer early surgical intervention for appropriate candidates.