Morris Melanie S, Graham Laura A, Richman Joshua S, Hollis Robert H, Jones Caroline E, Wahl Tyler, Itani Kamal M F, Mull Hillary J, Rosen Amy K, Copeland Laurel, Burns Edith, Telford Gordon, Whittle Jeffery, Wilson Mark, Knight Sara J, Hawn Mary T
*Birmingham and Tuscaloosa Health Services Research & Development Unit, Birmingham VA Medical Center, Birmingham, AL †Department of Surgery, University of Alabama at Birmingham, Birmingham, AL ‡VA Boston Healthcare System, Boston, MA §Boston University School of Medicine, Department of Surgery, Boston, MA ¶Harvard School of Medicine, Cambridge, MA ||Veterans Affairs, Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA **Veterans Affairs: Central Texas Veterans Health Care System, Baylor Scott & White Health, Center for Applied Health Research, Temple, TX ††Texas A&M Health Science Center, College of Medicine, Temple TX ‡‡Veterans Affairs, Milwaukee VAMC, Milwaukee, WI §§Department of Surgery, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania ¶¶Veterans Affairs, Palo Alto VAMC, Palo Alto, CA ||||Department of Surgery, Stanford University School of Medicine, Palo Alto CA.
Ann Surg. 2016 Oct;264(4):621-31. doi: 10.1097/SLA.0000000000001855.
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.
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.
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 and c-statistics with cross-validation to estimate predictive discrimination.
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 7.0% [c-statistic 0.67]); prediction was improved by inclusion of intraoperative (R 9.0%, c-statistic 0.69) and postoperative variables (R 10.4%, c-statistic 0.71). Including postdischarge complications improved predictive ability, explaining 19.6% of the variation (R 19.6%, c-statistic 0.76).
Postoperative readmissions are difficult to predict at the time of discharge, and of information available at that time, preoperative factors are the most important.
本研究旨在了解术前患者因素、手术特征及术后住院过程对术后30天再入院情况的相对影响。
确定手术后再入院风险较为困难。了解最重要的影响因素对于改善术后再入院风险的预测及降低该风险很重要。
将2008年至2014年美国退伍军人事务部国家外科质量改进计划中关于住院普通外科、血管外科和骨科手术的数据，与实验室检查、生命体征、既往医疗利用情况及术后并发症数据进行合并。变量分为术前、术中、术后/出院前及出院后几类。使用调整后的R值和c统计量并通过交叉验证比较预测30天再入院情况的逻辑模型，以评估预测辨别力。
我们的研究样本包括237,441例手术：43%为骨科手术，39%为普通外科手术，18%为血管外科手术。总体30天非计划再入院率为11.1%，不同外科专科有所差异（血管外科15.4%，普通外科12.9%，骨科7.6%，P<0.001）。最常见的再入院原因是伤口并发症（30.7%）、胃肠道并发症（16.1%）、出血（4.9%）及液体/电解质并发症（7.5%）。利用出院时可得信息构建的模型解释了再入院情况中10.4%的变异性。其中，术前患者层面因素对预测模型的贡献最大（R值7.0%[c统计量0.67]）；纳入术中因素（R值9.0%，c统计量0.69）和术后变量后预测效果得到改善（R值10.4%，c统计量0.71）。纳入出院后并发症可提高预测能力，解释19.6%的变异（R值19.6%，c统计量0.76）。
出院时难以预测术后再入院情况，而就当时可得信息而言，术前因素最为重要。
