Preoperatively predicting non-home discharge after surgery for gynecologic malignancy

Area of Science:

• Gynecologic Oncology
• Surgical Outcomes
• Health Services Research

Background:

• Returning home after surgery is a patient-centered outcome and is associated with lower costs.
• Predicting non-home discharge is crucial for resource allocation and patient care planning.

Purpose of the Study:

• To develop and validate a preoperative risk-scoring model for predicting non-home discharge after surgery for gynecologic malignancy.

Main Methods:

• A multivariable logistic regression model was developed using data from the Michigan Surgical Quality Collaborative database (2013-2015).
• The model was validated using the National Surgical Quality Improvement Program (NSQIP) database.
• Risk scores were assigned using a nomogram created from the developed model.

Main Results:

• Non-home discharge occurred in 3.1% of 2134 women, with skilled nursing facilities being the most common destination.
• Factors associated with non-home discharge included multiple comorbidities, laparotomy (vs. laparoscopy), age ≥70 years, American Society of Anesthesiologists class ≥3, and dependent functional status.
• The model demonstrated high predictive accuracy with a C-statistic of 0.89 in the development cohort and 0.84 in the validation cohort.

Conclusions:

• A preoperative risk-scoring model can accurately predict non-home discharge after gynecologic cancer surgery.
• This predictive model can aid in identifying high-risk patients for targeted interventions.
• Improved prediction of discharge destination can optimize resource utilization and enhance patient-centered care.