Risk factors for readmission after lower extremity bypass in the American College of Surgeons National Surgery Quality Improvement Program

• Published in: Journal of vascular surgery Vol. 59; no. 5; pp. 1331 - 1339
• Main Authors: Zhang, Jennifer Q., MD, Curran, Thomas, MD, McCallum, John C., MD, Wang, Li, BS, Wyers, Mark C., MD, Hamdan, Allen D., MD, Guzman, Raul J., MD, Schermerhorn, Marc L., MD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-05-2014
• Subjects: Aged; Chi-Square Distribution; Databases, Factual; Female; Humans; Logistic Models; Lower Extremity - blood supply; Male; Middle Aged; Multivariate Analysis; Odds Ratio; Patient Readmission; Peripheral Arterial Disease - mortality; Peripheral Arterial Disease - surgery; Postoperative Complications - diagnosis; Postoperative Complications - mortality; Postoperative Complications - therapy; Quality Improvement; Retrospective Studies; Risk Factors; Surgery; Time Factors; Treatment Outcome; United States; Vascular Grafting - adverse effects; Vascular Grafting - mortality; United States
• ISSN: 0741-5214; 1097-6809
• DOI: 10.1016/j.jvs.2013.12.032

Objective Readmission is associated with high mortality, morbidity, and cost. We used the American College of Surgeons National Surgery Quality Improvement Program (ACS-NSQIP) to determine risk factors for readmission after lower extremity bypass (LEB). Methods We identified all patients who received LEB in the 2011 ACS-NSQIP database. Multivariable logistic regression was used to assess independent predictors of 30-day readmission. We also identified our institutional contribution of LEB patients to the ACS-NSQIP from 2005 to 2011 to determine our institution's rate of readmission and readmission indications. Results Among 5018 patients undergoing LEB, ACS-NSQIP readmission analysis was performed on 4512, excluding those whose readmission data were unavailable, who suffered a death on index admission, or who remained in the hospital at 30 days. Overall readmission rate was 18%, and readmission rate of those with NSQIP-captured complications was 8%. Multivariable predictors of readmission were dependent functional status (odds ratio [OR], 1.40; 95% confidence interval [CI], 1.08-1.79), dyspnea (OR, 1.28; 95% CI, 1.02-1.60), cardiac comorbidity (OR, 1.46; 95% CI, 1.16-1.84), dialysis dependence (OR, 1.44; 95% CI, 1.05-1.97), obesity (OR, 1.28; 95% CI, 1.07-1.53), malnutrition (OR, 1.42; 95% CI, 1.12-1.79), critical limb ischemia operative indication (OR, 1.40; 95% CI, 1.10-1.79), and return to the operating room on index admission (OR, 8.0; 95% CI, 6.68-9.60). The most common postdischarge complications occurring in readmitted patients included wound complications (55%), multiple complications (22%), and graft failure (5%). Our institutional data contributed 465 LEB patients to the ACS-NSQIP from 2005 to 2012, with an overall readmission rate of 14%. Unplanned readmissions related to the original LEB (related unplanned) made up 75% of cases. The remainder 25% included readmissions that were planned staged procedures related to the original LEB (related planned, 11%) and admissions for a completely unrelated reason (unrelated unplanned, 14%). The most common readmission indications included wound infection (37%) and graft failure (10%). Readmissions were attributable to NSQIP-captured postdischarge complications in 44% of cases, an additional 44% had a non-NSQIP-defined reason for readmission, and the remainder (12%) included patients admitted for complications described in NSQIP but not meeting strict NSQIP criteria. Conclusions Readmissions are common after LEB. Optimization of select chronic conditions, closer follow-up of patients in poor health and those who required return to the operating room, and early detection of surgical site infections may improve readmission rates. Our finding that 25% of readmissions after LEB are not procedure related informs the broader discussion of how a readmission penalty affects vascular surgery in particular.