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首页> 外文期刊>Journal of the American College of Surgeons >Derivation and validation of a simple calculator to predict home discharge after surgery
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Derivation and validation of a simple calculator to predict home discharge after surgery

机译:衍生和验证简单计算器预测手术后的家庭放电

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Background Surgical patients and their physicians currently have tools to provide individualized prognostication for morbidity and mortality. For improved shared decision making, formal prediction of patient-centered outcomes is necessary. We derived and validated a simple, interview-based method to predict discharge home after surgery. Study Design We used the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Patient User File for 2011. Derivation in general and vascular surgery patients undergoing inpatient surgery was completed using serial multiple logistic regression. Validation was performed within multiple surgical specialties. Results The derivation cohort included 88,068 patients, of whom 11,771 (13.4%) were not discharged home. The derived Home Calculator had excellent discrimination (c-statistic = 0.864) using 4 variables: age, American Society of Anesthesiologists' performance status, elective surgery, and preadmission residence. Validation cohorts had varying rates of home discharge as follows: general (63,826 of 71,591, 89.2%), vascular (12,319 of 16,102, 76.5%), gynecologic (16,603 of 17,005, 97.6%), urologic (13,662 of 14,435, 94.6%), orthopaedic (12,000 of 19,514, 61.5%), thoracic (4,467 of 5,092, 87.7%). The Home Calculator provided good to excellent discrimination in validation cohorts: general (c = 0.866), vascular (c = 0.800), gynecologic (c = 0.793), urologic (c = 0.814), orthopaedic (c = 0.876), and thoracic (c = 0.800). Comparable discrimination was demonstrated in sensitivity analyses in surgical patients admitted exclusively from home. Conclusions We derived and validated a simple Home Calculator that reliably predicts discharge to home after surgery and may be useful when counseling patients about postoperative course. Patient-centered tools such as this may allow physicians to better prepare patients and families for surgery and the recovery process.
机译:背景技术手术患者及其医生目前有工具为发病率和死亡率提供个性化预后。对于改进的共享决策,需要正式预测患者中心结果。我们派生并验证了一种简单的面试的方法,以预测手术后的放电。学习设计我们使用美国外科医生国家外科院校(ACS NSQIP)患者用户档案2011年。使用串行逻辑回归完成了在进行视力手术的一般和血管手术患者中的衍生。验证是在多个外科专业中进行的。结果衍生队队列包括88,068名患者,其中11,771名(13.4%)没有排放回家。衍生的家庭计算器使用4个变量具有出色的歧视(C-Statistic = 0.864):年龄,美国麻醉学家的绩效状况,选修外科和普形居住。验证队列的收购率不同,如下:一般(63,826,71,591,89.2%),血管(12,319个,16.102,76.5%),妇科(16,603,17,005,97.6%),泌尿科(13,662个,14,435,94.6%) ,矫形(12,000名,19,514,61.5%),胸胸(4,467,共5,092,87.7%)。家庭计算器在验证队列中提供了良好的歧视:一般(C = 0.866),血管(C = 0.800),妇科(C = 0.793),泌尿科(C = 0.814),矫形(C = 0.876)和胸部( c = 0.800)。在专门从家里允许的手术患者中敏感性分析证明了可比的歧视。结论我们得出并验证了一个简单的家庭计算器,可靠地预测手术后对家庭的出院,并且在术后课程咨询患者时可能有用。患者居中的工具,如此可能允许医生更好地为手术和恢复过程做好准备患者和家庭。

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  • 作者单位

    Center for Surgery and Public Health Brigham and Women's Hospital Boston MA United States;

    Center for Surgery and Public Health Brigham and Women's Hospital Boston MA United States;

    Department of Surgery Mayo Clinic Rochester MN United States Center for the Science of Health;

    Department of Emergency Medicine Mayo Clinic Rochester MN United States;

    Center for the Science of Health Care Delivery Mayo Clinic Rochester MN United States;

    Center for Surgery and Public Health Brigham and Women's Hospital Boston MA United States;

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  • 正文语种 eng
  • 中图分类 外科学;
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