Multivariate Insights into Anesthesia Outcomes: A Statistical Exploration of Clinical Parameters

Abstract:Background: Effective anesthesia management requires a thorough understanding of patient-specificclinical parameters that influence airway management, intubation success, and postoperativecomplications. This study employs Multivariate Analysis of Variance (MANOVA) to assess the impactof age, gender, height, weight, and airway classification on anesthesia outcomes.Methods: A retrospective analysis was conducted on 103 patients undergoing anesthesia at a tertiarycare hospital. Key clinical variables, including ASA grade, Mallampati classification, intubationdifficulty, and anesthesia type, were statistically analyzed using MANOVA to determine theircollective influence on anesthesia-related complications. Visualizations, including confusion matricesand multivariate plots, were used to interpret the relationships between parameters.Results: The analysis revealed that age (p = 0.0002) and weight (p = 0.0338) significantly affectedanesthesia outcomes, particularly in airway management and intubation success rates. Gender (p =0.1436) and height (p = 0.3037) did not show significant effects. Patients with higher BMI andadvanced age exhibited greater airway challenges, necessitating specialized anesthetic interventions.Conclusion: The findings highlight the importance of preoperative assessment and personalizedanesthesia planning, especially for high-risk patients. Weight and age should be critical considerationsin anesthesia management strategies to minimize intraoperative and postoperative complications.Future research should incorporate larger datasets and additional clinical parameters to refine predictivemodels and optimize anesthesia outcomes.