Assessing 30-Day Hospital Readmission After Renal Transplantation: A Complex Task
Article first published online: 30 NOV 2012
© Copyright 2012 The American Society of Transplantation and the American Society of Transplant Surgeons
American Journal of Transplantation
Volume 12, Issue 12, pages 3171–3172, December 2012
How to Cite
Kaplan, B. and Sweeney, J. F. (2012), Assessing 30-Day Hospital Readmission After Renal Transplantation: A Complex Task. American Journal of Transplantation, 12: 3171–3172. doi: 10.1111/j.1600-6143.2012.04289.x
- Issue published online: 30 NOV 2012
- Article first published online: 30 NOV 2012
- Received 05 July 2012, revised 14 August 2012 and accepted for publication 14 August 2012
Hospital readmissions have become an important metric for measuring quality of patient care since the Centers for Medicare and Medicaid services began publishing 30-day readmission rates in 2009. The Patient Protection and Affordable Care Act contains a specific section (section 3025) which outlines how hospitals will be held accountable for 30-day hospital readmissions (1). In April of 2012, CMS proposed a new methodology for adjusting hospital reimbursements based upon the number of readmissions, with excessive readmissions leading to decreased payments (2). The United States Supreme Court upheld the Patient Protection and Affordable Care Act in June of 2012, leaving the door open for CMS to begin enforcing section 3025 (3).
Section 3025 started the focus on readmissions for selected medical diseases, but it is likely that CMS will extend this readmission policy to surgical procedures as early as fiscal year 2015. The readmission problem is fundamentally different in surgical patients compared to medical patients. Surgical patients have underlying comorbidities similar to medical patients; however, the surgical patient undergoes a specific procedure which carries an associated risk of readmission. This risk is influenced by underlying disease, the extent of the procedure and the incidence of adverse surgical related morbidity. In the case of renal transplantation, one deals with a patient with end-stage disease and often years of significant chronic illnesses. Though the surgical procedure that these patients undergo is intended to treat one or more of their chronic diseases, this may take months to years and would not be expected to mitigate perioperative risk associated with chronic illness or other nonmodifiable risk factors such as age. Transplant patients are discharged with complicated and toxic drug regimens as well as numerous metabolic changes subsequent to the new organ. In addition instrumentation of the lower urinary tract and the increased risk of poor wound healing due to chronic illness and/or medications may further increase the risk of readmission. With the increased attention placed upon readmissions in our current healthcare climate, it is essential to understand risk factors for readmission and how readmissions might be prevented in this complex patient population.
The article in the current issue of the American Journal of Transplantation by McAdams-DeMarco et al. is an initial attempt to understand these factors in kidney transplant recipients (4). This study uses a unique coupling of databases to look at 30-day hospital readmissions in patients that underwent a kidney transplant. The authors do identify several interesting associations for readmission; however with such a large data set there are certainly many statistically significant associative variables in the comparison. It is important to delineate those that are truly biologically, clinically and economically relevant. Further, as with all registry studies, only associations can be made and inferences as to causality must be tempered by the unalterable potential for nonrandom and unaccounted bias that inevitably occurs in complex medical decision making. In fact physician biases and experiences may take precedence over patient associated disease factors when making the decision to readmit a patient. These decisions are not always cut and dry, but rather they involve a physician's assessment of each particular patients need for in-hospital versus outpatient therapy. In this regard it is not surprising that older age may not only be an independent risk factor, but also drive behavior based on a presumption of greater risk and less physical reserve. The same may be said for the observation in this study of a decreased risk of readmission after induction therapy. Because of the belief that acute rejection may be less common with induction therapy, a higher proportion of patients may undergo a wait and see strategy for readmission as opposed to patients that did not receive induction therapy and who are thus perceived to be at a higher risk for rejection and therefore readmitted for a biopsy. These biases are often appropriate and the heuristics correct, but they also do not allow the surety that a risk factor is in fact independent and therefore necessarily causal in nature. The impact of induction therapy on hospital readmission is one area that needs to be evaluated prospectively.
In the article by McAdams-DeMarco et al., very elegant statistical approaches are utilized to find what in essence are relatively small differences in effect size (4). Further many of these factors are not modifiable, such as age and gender. Many other factors (e.g. cold ischemia time) are already associated with adverse outcomes other than readmission, and therefore are actively modified to the extent possible to mitigate adverse outcomes in the perioperative period. Furthermore, the known comorbidities that are the reason for renal disease (e.g. diabetes with concomitant vascular disease) place a patient at risk for perioperative complications that could lead to readmission.
This manuscript brings up a very important topic and the authors should be congratulated for their elegant analysis. The associations teased out should be subject to further study. But, the analysis is limited by the fact that the complex interplay of biologic and procedural risk factors coupled with caregiver perceptual differences may not be entirely accounted for in the data sets utilized. This may lead to over interpretation of small effect size associations. Though a necessary step, the starting point is not always the best vantage point to see where the path may lead.
The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
- 1The Patient Protection and Affordable Care Act. 111th United States Congress. March 23, 2010.
- 2Centers for Medicare and Medicaid Services. FY 2013 Hospital readmissions reduction program supplemental data file. Inpatient Prospective Payment System. Available at: http://www.cms.gov.
- 3National Federation of Independent Business v Sebelius, 567 US _ (2012). Available at: http://www.supremecourt.gov/opinions/11pdf/11-393c3a2.pdf.