Factors Influencing The Hemodynamic Stability And Its Management In Patients Undergoing Pelvic Surgeries Under Spinal And General Anesthesia

Abstract

Background: Hemodynamic instability during and after surgery is a critical concern influencing patient outcomes, particularly in those with comorbidities or undergoing major procedures. This study aimed to analyze the perioperative factors associated with intraoperative and postoperative hemodynamic instability using a dataset of 270 patients.
Methods: A cross-sectional dataset was constructed containing demographic, clinical, intraoperative, and postoperative variables. Descriptive statistics summarized frequencies and percentages. Inferential analysis included chi-square tests for associations between categorical variables, independent samples t-tests for continuous variables across groups, and logistic regression to identify predictors of postoperative hemodynamic instability.
Results: The majority of patients were ASA class II (42.6%) with common comorbidities including hypertension (20.4%) and diabetes (19.6%). Hemodynamic changes occurred in 62.6% of cases, while 21.1% experienced postoperative hemodynamic instability. ICU admission was required in 29.3% of patients. Chi-square tests showed no significant association between vasopressor use and postoperative instability (χ² = 0.19, p = 0.6637), ICU admission and intraoperative hemodynamic changes (χ² = 0.29, p = 0.5902), or hospital stay duration and instability (χ² = 1.74, p = 0.6279). Similarly, t-tests revealed no significant differences in duration of surgery or lowest heart rate between stable and unstable groups (p > 0.1). Logistic regression identified no statistically significant predictors of postoperative instability (all p > 0.05), though higher fluid volumes and longer hospital stays showed non-significant positive trends.
Conclusion: In this study, inferential statistics did not identify any perioperative variables as significant predictors of postoperative hemodynamic instability. These findings emphasize the complexity of hemodynamic outcomes and highlight the need for larger real-world datasets and more advanced modeling techniques to better understand and manage perioperative risk