Postoperative Recovery and Outcomes: A Retrospective Analysis of Patient Data

Abstract

Introduction: Postoperative recovery is a vital yet intricate process, influenced by a wide array of clinical and demographic factors that collectively determine the speed and quality of a patient’s return to health. These factors may include the nature and complexity of the surgical procedure, the patient’s baseline health status, age, gender, lifestyle habits, and access to postoperative care. Understanding the intricate relationships between these variables is essential for developing targeted interventions that address individual patient needs. By identifying key predictors of recovery outcomes, healthcare providers can implement evidence-based strategies to improve the overall postoperative experience, reduce complications, and enhance long-term well-being
Methods:In this retrospective study, we examined the records of ninety postoperative patients from a tertiary hospital. Factors measured included blood oxygen levels (LO2), surgical site stability (SURFSTBL), core stability in bed (CORESTBL), back pain stability (BPSTBL), comfort levels (COMFORT), admission decisions, and surgical levels (LSURF). Descriptive statistics were used to describe the sample demographics. Chi-square tests were employed to examine the relationships between categorical variables. A one-sample t-test evaluated variations in comfort levels from baseline. Bayesian regression analysis was conducted using SPSS version 27.0 to explore the relationships between comfort levels and clinical variables.
Results:Most patients had mid-levels of back pain stability (63.3%) and core stability (64.4%). Blood oxygen levels were categorized as good in 52.2% of cases and exceptional in 47.8% of cases. Core stability was stable in 92.2% of cases and unstable in 6.7%. The distribution of stable versus unstable values for surgical site stability and core stability was equal. The average comfort level was 10.94. Significant correlations were found between core stability and back pain stability, as well as between back pain stability and blood oxygen levels, using chi-square tests. Comfort ratings varied significantly from baseline (p<0.001). Bayesian regression identified core stability, surgical levels, and blood oxygen levels as significant predictors of comfort.
Conclusion: This study describes recovery profiles following surgery. A considerable correlation exists between back pain, comfort levels, and core stability among various clinical parameters. Optimal recovery trajectories and outcomes could be achieved with further studies incorporating more comprehensive factors. Targeted clinical pathways based on modifiable risk profiles can streamline care.