Identification of Modifiable Factors for Reducing Readmission After Colectomy: a National Analysis
Overview
Affiliations
Background: Rates of hospital readmission are currently used for public reporting and pay for performance. Colectomy procedures account for a large number of readmissions among operative procedures. Our objective was to compare the importance of 3 groups of clinical variables (demographics, preoperative risk factors, and postoperative complications) in predicting readmission after colectomy procedures.
Methods: Patient records (2005-2008) from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) were linked to Medicare inpatient claims. Patient demographics (n = 2), preoperative risk factors (n = 23), and 30-day postoperative complications (n = 17) were identified from ACS-NSQIP, whereas 30-day postoperative readmissions and costs were determined from Medicare. Multivariable logistic regression models were used to examine risk-adjusted predictors of colectomy readmission.
Results: Among 12,981 colectomy patients, the 30-day postoperative readmission rate was 13.5%. Readmitted patients had slightly greater rates of comorbidities and indicators of clinical severity and substantially greater rates of complications than non-readmitted patients. After risk adjustment, patients with a complication were 3.3 times as likely to be readmitted as patients without a complication. Among individual complications, progressive renal failure and organ-space surgical site infection had the highest risk-adjusted relative risks of readmission (4.6 and 4.0, respectively). Demographic, preoperative risk factor, and postoperative complication variables increased the ability to discriminate readmissions (reflected by the c-statistic) by 5.3%, 23.3%, and 35.4%, respectively.
Conclusion: Postoperative complications after colectomy are more predictive of readmission than traditional risk factors. Focusing quality improvement efforts on preventing and managing postoperative complications may be the most important step toward reducing readmission rates.
Published models that predict hospital readmission: a critical appraisal.
Grossman Liu L, Rogers J, Reeder R, Walsh C, Kansagara D, Vawdrey D BMJ Open. 2021; 11(8):e044964.
PMID: 34344671 PMC: 8336235. DOI: 10.1136/bmjopen-2020-044964.
Ruel M, Garcia M, Arbour C Nurs Open. 2021; 8(4):1550-1570.
PMID: 34102021 PMC: 8186688. DOI: 10.1002/nop2.730.
Hughes B, Sommerhalder C, Sieloff E, Williams K, Tyler D, Senagore A Healthcare (Basel). 2020; 8(4).
PMID: 33276456 PMC: 7711826. DOI: 10.3390/healthcare8040529.
Resurrecting immortal-time bias in the study of readmissions.
Hugar L, Borza T, Oerline M, Hollenbeck B, Skolarus T, Jacobs B Health Serv Res. 2019; 55(2):273-276.
PMID: 31880314 PMC: 7080383. DOI: 10.1111/1475-6773.13252.
Major P, Wysocki M, Torbicz G, Gajewska N, Dudek A, Malczak P Obes Surg. 2017; 28(2):323-332.
PMID: 28762024 PMC: 5778173. DOI: 10.1007/s11695-017-2844-x.