Understanding the dynamics of post-surgical recovery and its predictors in resource-limited settings: a prospective cohort study.

INTRODUCTION

Post-surgical recovery time is influenced by various factors, including patient demographics, surgical details, pre-existing conditions, post-operative care, and socioeconomic status. Understanding these dynamics is crucial for improving patient outcomes. This study aims to identify significant predictors of post-surgical recovery time in a resource-limited Ethiopian hospital setting and to evaluate the variability attributable to individual patient differences and surgical team variations.

METHODS

A linear mixed model was employed to analyze data from 490 patients who underwent various surgical procedures. The analysis considered multiple predictors, including age, gender, BMI, type and duration of surgery, comorbidities (diabetes and hypertension), ASA scores, postoperative complications, pain management strategies, physiotherapy, smoking status, alcohol consumption, and socioeconomic status. Random effects were included to account for variability at the patient and surgical team levels.

RESULTS

Significant predictors of prolonged recovery time included higher BMI, longer surgery duration, the presence of diabetes and hypertension, higher ASA scores, and major post-operative complications. Opioid pain management was associated with increased recovery time, while inpatient physiotherapy reduced recovery duration. Socioeconomic status also significantly influenced recovery time. The model fit statistics indicated a robust model, with the unstructured covariance structure providing the best fit.

CONCLUSION

The findings highlight the importance of individualized patient care and the effective management of modifiable factors such as BMI, surgery duration, and postoperative complications. Socioeconomic status emerged as a novel factor warranting further investigation. This study underscores the value of considering patient and surgical team variability in post-surgical recovery analysis, and calls for future research to explore additional predictors and alternative modeling techniques to enhance our understanding of the recovery process.