Comparison of two modeling approaches for the identification of predictors of complications in children with cerebral palsy following spine surgery.

Abstract

Background: Children with non-ambulatory cerebral palsy (CP) frequently develop progressive neuromuscular scoliosis and require surgical intervention. Due to their comorbidities, they are at high risk for developing peri- and post-operative complications. The objectives of this study were to compare stepwise and LASSO variable selection techniques for consistency in identifying predictors when modelling these post-operative complications and to identify potential predictors of respiratory complications and infections following spine surgery among children with CP.

Methods: In this retrospective cohort study, a large administrative claims database was queried to identify children who met the following criteria: 1) ≤ 25 years old, 2) diagnosis of CP, 3) underwent surgery during the study period, 4) had ≥ 12-months pre-operative, and 5) ≥ 3-months post-operative continuous health plan enrollment. Outcome measures included the development of a post-operative respiratory complication (e.g., pneumonia, aspiration pneumonia, atelectasis, pleural effusion, pneumothorax, pulmonary edema) or an infection (e.g., surgical site infection, urinary tract infection, meningitis, peritonitis, sepsis, or septicemia) within 3 months of surgery. Codes were used to identify CP, surgical procedures, medical comorbidities and the development of post-operative respiratory complications and infections. Two approaches to variable selection, stepwise and LASSO, were compared to determine which potential predictors of respiratory complications and infection development would be identified using each approach.

Results: The sample included 220 children. During the 3-month follow-up, 21.8% (n = 48) developed a respiratory complication and 12.7% (n = 28) developed an infection. The prevalence of 11 variables including age, sex and 9 comorbidities were initially considered to be potential predictors based on the intended outcome of interest. Model discrimination utilizing LASSO for variable selection was slightly improved over the stepwise regression approach. LASSO resulted in retention of additional comorbidities that may have meaningful associations to consider for future studies, including gastrointestinal issues, bladder dysfunction, epilepsy, anemia and coagulation deficiency.

Conclusions: Potential predictors of the development of post-operative complications were identified in this study and while identified predictors were similar using stepwise and LASSO regression approaches, model discrimination was slightly improved with LASSO. Findings will be used to inform future research processes determining which variables to consider for developing risk prediction models.