Empirical Analysis of Post-Operative Outcomes in Diabetic Patients Undergoing Elective Surgery: A Case Study from a Tertiary Care Center

Authors

  • Alex Wilson PhD
  • Kai Carter Associate Professor
  • Morgan Brown Professor
  • Drew Parker Dr. Sc

Keywords:

diabetes management, post-operative outcomes, glycemic control, elective surgery, hospitalization, complication rates, clinical practice, patient safety, surgical complications

Abstract

The rising prevalence of diabetes mellitus has led to an increased number of surgical procedures performed on diabetic patients, bringing forth novel challenges in post-operative care. This study investigates the post-operative outcomes of diabetic patients undergoing elective surgeries at a tertiary care center, employing a retrospective cohort design. We analyzed data from 500 patients over a five-year period (2018-2023), focusing on complications, length of hospital stay, and recovery times. Statistical analyses were conducted using SPSS version 26. Our findings revealed a significant correlation between glycemic control pre-surgery and post-operative complications, with a p-value of 0.002. Patients with well-controlled diabetes (HbA1c < 7%) exhibited a 30% decrease in complications compared to those with poor control. Additionally, the average length of stay was significantly longer (12 days vs. 7 days) in patients experiencing complications. This study provides critical insights into the importance of pre-operative glycemic management in improving surgical outcomes for diabetic patients.

Author Biographies

Alex Wilson, PhD

PhD
Harvard University
Massachusetts Hall, Cambridge, MA 02138, USA

Kai Carter, Associate Professor

Associate Professor
Ludwig Maximilian University of Munich
Geschwister-Scholl-Platz 1, 80539 Munich, Germany

Morgan Brown, Professor

Professor
University of Sydney
Camperdown NSW 2006, Australia

Drew Parker, Dr. Sc

Dr. Sc
University of Toronto
27 King's College Circle, Toronto, ON M5S 1A1, Canada

References

Mavuri, M., Chakrabarty, S., Rathod, U., & Sarda, D. (2025, December). Geospatial Analysis Using Transformer on TOAR and Meteorological Data for Early Warning of Particulate Matter Exceedance. In 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 7036-7043). IEEE.

Published

2025-11-03

Issue

Section

Articles