Postoperative 30-Day Readmission: Time to Focus on What Happens Outside the Hospital

Melanie S. Morris, MD; Laura A. Graham, MPH; Joshua S. Richman, MD, PhD; Robert H. Hollis, MD; Caroline E. Jones, MD; Tyler Wahl, MD; Kamal M. F. Itani, MD; Hillary J. Mull, PhD; Amy K. Rosen, PhD; Laurel Copeland, PhD; Edith Burns, MD; Gordon Telford, MD; Jeffery Whittle, MD, MPH; Mark Wilson, MD; Sara J. Knight, PhD; Mary T. Hawn, MD, MPH

Disclosures

Annals of Surgery. 2016;264(4):621-631. 

In This Article

Abstract and Introduction

Abstract

Objective: The aim of this study is to understand the relative contribution of preoperative patient factors, operative characteristics, and postoperative hospital course on 30-day postoperative readmissions.

Background: Determining the risk of readmission after surgery is difficult. Understanding the most important contributing factors is important to improving prediction of and reducing postoperative readmission risk.

Methods: National Veterans Affairs Surgical Quality Improvement Program data on inpatient general, vascular, and orthopedic surgery from 2008 to 2014 were merged with laboratory, vital signs, prior healthcare utilization, and postoperative complications data. Variables were categorized as preoperative, operative, postoperative/predischarge, and postdischarge. Logistic models predicting 30-day readmission were compared using adjusted R 2 and c-statistics with cross-validation to estimate predictive discrimination.

Results: Our study sample included 237,441 surgeries: 43% orthopedic, 39% general, and 18% vascular. Overall 30-day unplanned readmission rate was 11.1%, differing by surgical specialty (vascular 15.4%, general 12.9%, and orthopedic 7.6%, P < 0.001). Most common readmission reasons were wound complications (30.7%), gastrointestinal (16.1%), bleeding (4.9%), and fluid/electrolyte (7.5%) complications. Models using information available at the time of discharge explained 10.4% of the variability in readmissions. Of these, preoperative patient-level factors contributed the most to predictive models (R 2 7.0% [c-statistic 0.67]); prediction was improved by inclusion of intraoperative (R 2 9.0%, c-statistic 0.69) and postoperative variables (R 2 10.4%, c-statistic 0.71). Including postdischarge complications improved predictive ability, explaining 19.6% of the variation (R 2 19.6%, c-statistic 0.76).

Conclusions: Postoperative readmissions are difficult to predict at the time of discharge, and of information available at that time, preoperative factors are the most important.

Introduction

Readmission rates are now publicly reported, with financial implications for hospitals following the implementation of the Hospital Readmission Reduction Program.[1] Currently, the Centers for Medicare and Medicaid Services (CMS) reduces reimbursements to hospitals that exceed the national average for all-cause readmission rates for congestive heart failure, myocardial infarction, pneumonia, chronic lung disease, joint replacement, and cardiac bypass surgery.[1] CMS plans to expand these categories to include other operations, not yet defined.

A report on readmissions published in 2009 found that Medicare patients had an 18% 30-day readmission rate, with associated costs estimated at $17 billion.[2] This report, among other findings, led to targeting readmission as a potential outcome for cost savings and a marker of hospital quality. Readmissions for surgical patients involve different circumstances than for medical patients. Medical patients are frequently readmitted for the same diagnosis with which they were discharged, whereas surgical patients are more likely to be readmitted with a surgical complication that occurs with known frequency, and that varies by patient factors and procedure type. Understanding which patients are at high risk for readmission and which readmissions for surgical patients are preventable has been challenging. Furthermore, how to classify which reasons are preventable or unavoidable has yet to reach a consensus.

To reduce readmission rates following surgery, we must first understand which are predictable and under what circumstances they are preventable. Many readmissions are likely unavoidable and necessary to manage postoperative complications or disturbances in patients' underlying physiologic state secondary to surgery. If preventable surgical readmissions could be predicted, then targeted interventions could be designed and tested. The aim of this study is to understand the relative contribution of patient factors, operative characteristics, and postoperative hospital course to predicting risk of readmission. This will help to determine at what timepoints risk stratification can occur to allow initiation of interventions.

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