Factors Associated with Early Discharge after Non-Emergent Right Colectomy for Colon Cancer: A NSQIP Analysis.
Malcolm H SquiresErin E DonahueMichelle L WallanderSally J TrufanReilly E SheaNicole F LindholmJoshua S HillJonathan C SaloPublished in: Current oncology (Toronto, Ont.) (2023)
The National Surgical Quality Improvement Project (NSQIP) dataset was used to identify perioperative variables associated with the length of stay (LOS) and early discharge among cancer patients undergoing colectomy. Patients who underwent non-emergent right colectomy for colon cancer from 2012 to 2019 were identified from the NSQIP and colectomy-targeted databases. Postoperative LOS was analyzed based on postoperative day (POD) of discharge, with patients grouped into Early Discharge (POD 0-2), Standard Discharge (POD 3-5), or Late Discharge (POD ≥ 6) cohorts. Multivariable ordinal logistic regression was performed to identify risk factors associated with early discharge. The NSQIP query yielded 26,072 patients: 3684 (14%) in the Early Discharge, 13,414 (52%) in the Standard Discharge, and 8974 (34%) in the Late Discharge cohorts. The median LOS was 4.0 days (IQR: 3.0-7.0). Thirty-day readmission rates were 7% for Early Discharge, 8% for Standard Discharge, and 12% for Late Discharge. On multivariable regression analysis, risk factors significantly associated with a shorter LOS included independent functional status, minimally invasive approach, and absence of ostomy or additional bowel resection (all p < 0.001). Perioperative variables can be used to develop a model to identify patients eligible for early discharge after right colectomy for colon cancer. Efforts to decrease the overall median length of stay should focus on optimization of modifiable risk factors.
Keyphrases
- quality improvement
- end stage renal disease
- patients undergoing
- ejection fraction
- risk factors
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- squamous cell carcinoma
- patient safety
- young adults
- machine learning
- acute kidney injury
- patient reported outcomes
- deep learning
- artificial intelligence