Comparing CCI and POSSUM for Predicting Oncogynecologic Surgery Complications

Overview

With the global rates of gynecologic cancers on the rise, optimizing perioperative care is imperative. Accurate risk prediction is essential for enhancing patient care, directing preoperative interventions, and facilitating informed decision-making in oncology. This research compares two widely-used risk assessment tools: the Charlson Comorbidity Index (CCI) and the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), in predicting perioperative outcomes. The CCI predominantly addresses comorbidities, providing simplicity and broad applicability, while POSSUM incorporates both physiological and operative factors for a more comprehensive risk assessment. Despite their application across various surgical specialties, the specific utility of these tools in onco-gynecologic surgery remains insufficiently explored. The study aims to evaluate the effectiveness of CCI and POSSUM in predicting perioperative complications, with a focus on the incidence of these complications, length of hospital stay, and 30-day mortality. The implementation of these risk tools may enhance multidisciplinary risk management, thus improving patient outcomes in gynecologic oncology surgery.

Onco-gynecologic surgeries, including procedures for ovarian, cervical, uterine, and vulvar cancers, are complex and challenging due to both the technical intricacies of the surgeries and the often compromised health of cancer patients. These procedures carry significant perioperative risks, making optimization of perioperative care critical, especially as global cases of gynecological cancers rise, with millions affected annually. Accurate risk prediction is vital to improve patient outcomes, allowing surgeons and anesthesiologists to tailor preoperative interventions and intraoperative management. These strategies help in identifying high-risk patients, implementing enhanced monitoring, specific anesthetic techniques, or staged surgical approaches to mitigate potential complications. Two prominent risk assessment tools used in surgical practice are the Charlson Comorbidity Index (CCI) and the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM). The CCI, is a weighted index that considers the number and severity of comorbid conditions to predict mortality risk. Its simplicity and reliance on readily available clinical information have led to its widespread adoption in clinical and research settings. Studies have validated its predictive value across various surgical populations, including oncology patients, although its specific utility in onco-gynecologic surgeries needs further exploration. In contrast, the POSSUM score offers a more comprehensive risk assessment by incorporating both physiological and operative factors. It includes preoperative variables like age and cardiac signs and considers operative factors like procedural complexity and blood loss. This dual approach provides a nuanced prediction of perioperative risk, useful across diverse surgical fields. Despite POSSUM's broad application, its effectiveness specifically in onco-gynecologic surgeries requires additional investigation to fully ascertain its predictive accuracy and utility. Currently, our center conducts preoperative evaluations involving anesthesiologists and gynecologists, yet formal risk assessments using CCI or POSSUM have not been implemented. Incorporating these tools could enhance multidisciplinary risk management, involving anesthetic teams, ward nurses, gynecologic oncologists, and intensivists. By systematically evaluating patient history, these indices can promote effective interdisciplinary communication, significantly improve patient safety, and optimize surgical outcomes. This study aims to compare CCI and POSSUM in predicting perioperative complications, including both anesthetic and surgical complications in onco-gynecologic surgery. Additionally, it seeks to report the incidence of complications, length of hospital stay, and 30-day mortality, providing valuable insights into optimizing patient care in this challenging field.