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Risk-adjusted model reveals little variation in hospital readmissions after colorectal cancer surgery
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Researchers who used a risk-adjusted model to evaluate readmission rates after colorectal cancer surgery found little variation between hospitals.
The finding raises questions about the use of readmission rates as a “high-stakes quality measure,” particularly when rates do not reflect appropriate risk adjustments, researchers wrote.
CMS has emphasized the importance of reducing unplanned hospital readmissions. Because readmission rates have become a significant hospital quality metric, any variability among hospitals has important implications.
“After colorectal surgery, the rate of readmission within 30 days of discharge has been reported to be approximately 10% to 14%,” Donald J. Lucas, MD, MPH, of the department of surgery at Walter Reed National Military Medical Center, and colleagues wrote. “Readmission often represents an adverse patient event, results in increased cost of care, and sometimes serves as an indicator of underlying poor care.”
Hospitals with high readmission rates also are subject to monetary penalties, so pinpointing possible sources for high readmission rates is crucial, according to the researchers.
Lucas and colleagues conducted the study to assess whether readmission rates after colorectal cancer surgery varied among hospitals, as well as to isolate potential risk factors for readmission.
They performed a hierarchical multivariable logistic regression analysis of observational data obtained from the SEER-Medicare database. The analysis included 44,822 patients who underwent colectomy or proctectomy for colorectal cancer from January 1, 1997, to December 31, 2002.
The median age of patients was 78 years (interquartile range [IQR], 72-83). The most common comorbidities were chronic obstructive pulmonary disease (15.3%), diabetes mellitus (13.3%) and congestive heart failure (10.7%).
When Lucas and colleagues reviewed data from hospitals that performed at least five operations annually, they observed marked variation in raw readmission rates (range, 0% to 41.2%; IQR, 9.5%-14.8%).
However, after researchers used the hierarchical model to adjust for patient characteristics, comorbidities and operation types, they found no significant variability in readmission rates between hospitals (range, 11.3% to 13.2%; IQR, 12.1%-12.4%). Additionally, the 95% CI for hospital-specific readmission corresponded with the overall mean at every hospital.
“The use of risk-adjusted readmission rates as a high-stakes quality measure for payment adjustment or public reporting across surgical specialties should be proceed cautiously,” Lucas and colleagues concluded.
The researchers also suggested the uncertainty surrounding comparisons of readmission rates between hospitals could be reduced if greater numbers of patients were included in the calculations.
“The American College of Surgeons National Surgical Quality Improvement Program reports readmission across entire surgical specialties rather than for specific procedure types, with a requirement of at least 1,680 cases annually,” Lucas and colleagues wrote. “Although this may not be an issue with common ailments … low surgical volume in any one particular operation would result in an imprecise calculation of readmission. As an alternative, risk-adjusted readmission rates could be calculated for a surgical subspecialty or even across an entire department, rather than focusing on single operations … This approach would allow a broader evaluation of the hospital staff and the surgical team, which all contribute to differing rates in readmission.”
The study exposed risks shared by all hospitals and highlights the need for focused improvements, Frank G. Opelka, MD, vice chancellor for clinical affairs at the of the Louisiana State University Health Sciences Center, wrote in an accompanying editorial.
“The authors shed light on the concerns of raw data for readmissions, an approach that lends itself to misclassification of delivery systems and misguiding patients with the information if the analytics do not properly use risk adjustment and CIs,” Opelka said. “Lucas et al emphasize the importance of hierarchical regression in thoughtful design of analytics to ensure ‘apples-to-apples’ comparisons.”
Disclosure: The researchers report no relevant financial disclosures.
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