Oncology/Endocrine
Ninety-day readmission after colorectal cancer surgery in a Veterans Affairs cohort

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Abstract

Background

Readmissions following colorectal surgery are common. However, there are limited data examining unplanned readmissions (URs) after colorectal cancer (CRC) surgery. The goal of this study was to identify reasons and predictors of UR, and to examine their clinical impact on CRC patients.

Methods

A retrospective cohort study using a prospective CRC surgery database of patients treated at a VA tertiary referral center was performed (2005-2011). Ninety-day URs were recorded and classified based on reason for readmission. Clinical impact of UR was measured using a validated classification for postoperative complications. Multivariate logistic regression analyses were performed to identify predictors of UR.

Results

487 patients were included; 104 (21%) required UR. Although the majority of UR were due to surgical reasons (n = 72, 69%), medical complications contributed to 25% of all readmission events. Nearly half of UR (n = 44, 40%) had significant clinical implications requiring invasive interventions, intensive care unit stays, or led to death. After multivariate logistic regression, the following independent predictors of UR were identified: African–American race (odds ratio [OR] 0.47 [0.27–0.88]), ostomy creation (OR 2.50 [1.33–4.70]), and any postoperative complication (OR 4.36 [2.48–7.68]).

Conclusions

Ninety-day URs following colorectal cancer surgery are common, and represent serious events associated with worse outcomes. In addition to postoperative complications, surgical details that can be anticipated (i.e., ileostomy creation) and medical events unrelated to surgery, both contribute as important and potentially preventable reasons for UR. Future studies should focus on developing and examining interventions focused at improving the process of perioperative care for this high-risk population.

Introduction

Readmissions after colorectal surgery are common with studies reporting 30- and 90-d readmissions rates ranging from 7%–19% [1], [2], [3], [4], [5], [6] and 23%–27% [6], [7], [8], respectively. Given the number of colorectal procedures performed annually, readmissions after colorectal surgery are among the most common causes of surgical readmissions [2]. Despite being relatively common, unplanned readmissions (URs) are nevertheless considered adverse events, as they interfere with the postoperative recovery process [5] and lead to significant financial burden to patients and the health care system [6]. The estimated Medicare expenditures due to potentially preventable readmissions is up to $17.4 billion per year [9], [10]. Additionally, URs after surgery for malignancy, including specifically colorectal cancer (CRC), have been associated with worse long-term outcomes, including decreased 1-y overall survival [1], [11].

Given the significant impact on health care costs and the substantial variability in the rates of URs, readmissions are currently viewed as a measure of the quality of care provided to patients [12], [13]. The Medicare Payment Advisory Committee recommended to Congress to provide lower payments to hospitals with high risk-adjusted readmission rates for selected medical conditions [9]. The Patient Protection and Affordable Care Act is targeting lower rates of readmission as a method to decrease overall health care costs, and has followed the recommendation for decreased payments to hospitals with high rates of readmission [14], which took effect on October 2012, and is likely to include elective operations in the future [15].

In an effort to better understand and decrease readmissions after colorectal surgery, a series of studies have recently examined rates of readmissions and evaluated the most common causes [1], [3], [4], [5], [6], [7], [8], [16], [17], [18], [19], [20], [21], [22], [23]. However, the methods used to capture readmissions in these studies vary, and most have focused on readmission causes explicitly related to the surgical procedure, missing other potentially preventable reasons for readmissions, that are not necessarily related to the index hospitalization [24]. Similarly, the data examining reasons and predictors of URs focus on colorectal surgery in general, and data in cancer patients are limited. Given readmissions are associated with a negative impact on survival, and as patients with cancer require additional treatments and care coordination after recovery from surgery that extend beyond the traditionally studied 30-d window [1], [11], these issues are particularly relevant. Additionally, with the exception of cost and length of stay at the time of rehospitalization, the clinical impact during readmission episodes is currently unknown, and this is of great importance when understanding the continuity of care for cancer patients; for example, a readmission with a major clinical impact on a patient with cancer may delay receipt of adjuvant therapy, which ultimately may impact long-term survival, whereas a readmission for other less severe reasons may allow for standard recovery and no interruption of adjuvant therapies. Therefore, the importance of understanding the interplay of readmissions with these factors for the cancer population cannot be overlooked, especially as it relates to understanding the specific causes for readmissions (as a means to identify targets for intervention beyond reducing complications), and regarding the clinical impact of these events, and their potential effect during transitions of care.

Based on the previous considerations, we designed a retrospective cohort study, using direct physician chart review to examine the incidence of 90-d URs after curative surgery for CRC within an integrated health care system and to evaluate the specific causes contributing to readmission. We also sought to investigate the clinical impact of readmissions, using a validated model, and identify specific predictors of UR after surgery in this cancer population.

Section snippets

Study subjects

A retrospective cohort study was performed using a prospectively maintained database of all CRC operations performed at the Michael E. DeBakey Veterans Affairs Medical Center from 2005–2011, whether performed electively or emergently. This database is maintained through systematic physician chart reviews and contains information on patient demographics and comorbidities, operative and perioperative data, tumor and treatment characteristics, and long-term follow-up. Patients with American Joint

Baseline, treatment, and postoperative characteristics

A total of 495 patients were identified, and 487 were eligible for readmission; the remaining 8 patients died during the initial hospitalization. Baseline characteristics are noted in Table 1. Notably, this was an elderly cohort of patients, with almost half aged >65 y, and 22% at least 75-y-old. A high comorbidity index was prevalent in this population, with 41% of patients having a Charlson comorbidity score of at least 2. Also, close to one-third of patients required creation of an ostomy

Discussion

Hospital readmissions occur frequently after colorectal surgery [1], [3], [4], [5], [6], [7], [8], [16], [17], [18], [19], [20], [21], [22], [23]. With their variability across hospitals and associated cost to the health care system, readmissions have emerged as a measure of quality of care, as policymakers and providers alike work to identify methods to improve value in health care [9], [14]. Moreover, despite being recently questioned as an effective measure of surgical quality [28], from a

Conclusions

In conclusion, this study demonstrates that 90-d URs after CRC surgery are common and have substantial clinical impact, affecting the overall recovery process and postoperative care in this population. Although, postoperative complications are the most important reason and predictor of readmissions, we found that medical complications unrelated to surgery also contribute to a high proportion of readmission episodes. Efforts focusing at decreasing postoperative complications must be implemented

Acknowledgment

This material is based on the work supported in part by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. The authors had full access to all the data in the study and take responsibility for the integrity of the data and

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