Risk factors for complicated perioperative recovery in dogs undergoing staphylectomy or folded flap palatoplasty: Seventy-six cases (2018-2022).

OBJECTIVE: To analyze risk factors for complicated perioperative recovery of dogs undergoing either staphylectomy or folded flap palatoplasty. STUDY DESIGN: Retrospective study. ANIMALS: Seventy-six client-owned dogs. METHODS: Medical records of dogs that underwent either staphylectomy or folded flap palatoplasty were reviewed for signalment, brachycephalic risk (BRisk) score, history of gastrointestinal signs, laryngeal collapse grade, presence of preoperative aspiration pneumonia, intraoperative respiratory and cardiovascular complications, length of general anesthesia, number of corrected brachycephalic obstructive airway syndrome (BOAS) components, and gastrointestinal and respiratory postoperative complications. Complicated recovery was defined as requirement for prolonged oxygen treatment and/or tracheostomy or perioperative death. Penalized logistic regression was used to identify risk factors. RESULTS: Seventy-six dogs were enrolled in the study. Multivariate penalized logistic regression identified four risk factors for complicated recovery. These include surgery type (p = .0002), age (p = .0113), laryngeal collapse grade >2 (p < .0001) and length of general anesthesia (p = .0051). CONCLUSIONS: In this population, dogs that had staphylectomy, increasing age, laryngeal collapse grade >2 and increasing length of general anesthesia were at increased risk for perioperative complicated recovery. CLINICAL SIGNIFICANCE: The results of this study identified risk factors for perioperative complicated recovery in dogs undergoing elongated soft palate correction and may assist in surgical planning and early prediction of complications.

Utility of a slopes difference test for probing longitudinal multilevel aptitude treatment interactions: a simulation.

To determine which interventions work best for which students, precision education researchers can examine aptitude-treatment interactions (ATI) or skill-by-treatment interactions (STI) using longitudinal multilevel modeling. Probing techniques like the slopes difference test fit an ATI or STI framework, but power for using slopes difference tests in longitudinal multilevel modeling is unknown. The current study used simulation to determine which design factors influence the power of slopes difference tests. Design factors included effect size, number of waves, number of clusters, participants per cluster, proportion of assignment to the treatment group, and intraclass correlation. Of these factors, effect size, number of waves, number of clusters, and participants per cluster were the strongest determinants of power, model convergence, and rates of singularity. Slopes difference tests had greater power in longitudinal multilevel modeling than where it is originally utilized: multiple regression.