Perioperative Risk Stratification Model for Readmission after Panniculectomy

Area of Science:

• Plastic Surgery
• Surgical Quality Improvement
• Health Services Research

Background:

• Readmission after surgery is a key indicator of care quality.
• Panniculectomy is a common surgical procedure with significant readmission rates.
• Predicting readmission is crucial for optimizing patient outcomes and resource allocation.

Purpose of the Study:

• To develop and validate a clinical risk score for predicting 30-day readmission after panniculectomy.
• To identify key predictors of readmission in the panniculectomy patient population.
• To provide a tool for risk stratification to improve surgical quality.

Main Methods:

• Utilized the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2005-2018).
• Developed a multivariable logistic regression model on a 70% training set to identify predictors of 30-day readmission.
• Validated the derived integer-based risk score on a 30% independent validation set.

Main Results:

• Identified key predictors including inpatient surgery, hypertension, obesity, functional dependence, COPD, wound class ≥3, ASA class >3, and liposuction.
• The final model demonstrated good predictive accuracy with an Area Under the Curve (AUC) of 0.710 in the validation group.
• The model showed statistically similar predicted and observed readmission incidences across risk strata.

Conclusions:

• A validated risk stratification model for panniculectomy readmission has been developed.
• The model's accuracy suggests potential for improving patient safety and surgical quality.
• Further prospective studies are recommended to evaluate the clinical utility of this risk score in reducing readmissions.