Analysis of risk factors for readmission after hysterectomy: Use of a national database
Recommended Citation
Sangha R, Saeed H, and Rubinfeld I. Analysis of risk factors for readmission after hysterectomy: Use of a national database. J Minim Invasive Gynecol 2017; 24(7):S76.
Document Type
Conference Proceeding
Publication Date
2017
Publication Title
J Minim Invasive Gynecol
Abstract
Study Objective: There is a paucity of predictive work done to help risk assess and stratify patients potential for readmission hysterectomy. We sought to further explore the risk of readmission after hysterectomy. Design: Retrospective review of database. Setting: National Surgical Quality Improvement Project database ( NSQIP) Patients: Women who had undergone hysterectomy for non-cancer indications, 2005 and 2015. Intervention: Hysterectomy (any approach). Measurements and Main Results: We queried 11 years of the National Surgical Quality Improvement Project ( NSQIP)Participant Use File (PUF), 2005-2015. CPT codes for hysterectomy were chosen. Data was analyzed in R with univariate followed by multivariate analysis. There were 2636 readmissions and 81322 non-readmitted patients (rate of 3.1%). There were significant differences based on route of surgery (p < .001), with the majority 51.4% being open, and least after vaginal route ( 13.9%). Readmissions were younger (mean age 47.1 vs 48.0, p < .001), had more associated procedures based on work rvu (mean 16.9.1 vs 17.2, P < .001), were more likely to be diabetic (5.1% Insulin and 6.9% oral hypoglycemic, vs 2.0% and 5.7 % in non readmitted, P < .001), they also had higher ASA scores. Similar patterns were noted with hypertension, COPD, CHF, smoking history, dyspnea, functional status, steroid use, operative times, wound class. By multivariate logistic regression the most powerful independent predictors or readmission were: frailty (OR > 500), functional status (OR 1.64), steroid use (OR 1.65), ASA class (OR 1.47), COPD (OR 1.26), wound class (OR 1.25) and diabetes (OR 1.23). Conclusion: Improved prediction of readmission risk will facilitate discussion and informed consent with families, as well as empower quality improvement projects targeted on risk factor modification or escalation of readmission prevention interventions.
Volume
24
Issue
7
First Page
S76