Using Statistical Learning to Examine Variables that Contribute to Longer Hospital Stays After Endometrial Cancer Surgery.

Abstract

Endometrial cancer, primarily arising from the lining of the uterus (endometrium), is the most common gynecological malignancy in many developed countries. Risk factors include obesity, age, and a family history of related cancers. The prognosis is generally favorable when detected early. One important measure of clinical results and resource use is the duration of hospital stay (LOS) after surgery. Understanding the causes of prolonged hospital stays is crucial for improving patient care because both organizational and patient-related factors can have a substantial impact on LOS. This study uses a statistical learning technique to investigate the LOS following endometrial cancer surgery at the Antonio Cardarelli Hospital in Naples, Italy. It expands on earlier research on the reasons for prolonged LOS in surgical oncology. Illness severity showed a potential influence on LOS but was not statistically significant in the model.