Baltodano P A, Webb-Vargas Y, Soares K C, Hicks C W, Cooney C M, Cornell P, Burce K K, Pawlik T M, Eckhauser F E
Ravitch Division of GI and Minimally Invasive Surgery, Department of Surgery, The Johns Hopkins University School of Medicine, Blalock 618, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Hernia. 2016 Feb;20(1):119-29. doi: 10.1007/s10029-015-1413-2. Epub 2015 Aug 19.
BACKGROUND/PURPOSE: To present a validated model that reliably predicts unplanned readmission after open ventral hernia repair (open-VHR).
A total of 17,789 open-VHR patients were identified using the 2011-2012 ACS-NSQIP databases. This cohort was subdivided into 70 and 30% random testing and validation samples, respectively. Thirty-day unplanned readmission was defined as unexpected readmission for a postoperative occurrence related to the open-VHR procedure. Independent predictors of 30-day unplanned readmission were identified using multivariable logistic regression on the testing sample (n = 12,452 patients). Subsequently, the predictors were weighted according to β-coefficients to generate an integer-based Clinical Risk Score (CRS) predictive of readmission, which was validated using receiver operating characteristics (ROC) analysis of the validation sample (n = 5337 patients).
The rate of 30-day unplanned readmission was 4.7%. Independent risk factors included inpatient status at time of open-VHR, operation time, enterolysis, underweight, diabetes, preoperative anemia, length of stay, chronic obstructive pulmonary disease, history of bleeding disorders, hernia with gangrene, and panniculectomy (all P < 0.05). ROC analysis of the validation cohort rendered an area under the curve of 0.71, which demonstrates the accuracy of this prediction model. Predicted incidence within each 5 risk strata was statistically similar to the observed incidence in the validation sample (P = 0.18), further highlighting the accuracy of this model.
We present a validated risk stratification tool for unplanned readmissions following open-VHR. Future studies should determine if implementation of our CRS optimizes safety and reduces readmission rates in open-VHR patients.
背景/目的：提出一个经过验证的模型，该模型能可靠地预测开放性腹疝修补术（open-VHR）后计划外再入院情况。
使用2011 - 2012年美国外科医师学会国家外科质量改进计划（ACS-NSQIP）数据库识别出17789例接受open-VHR手术的患者。该队列分别被随机分为70%的测试样本和30%的验证样本。30天计划外再入院定义为因与open-VHR手术相关的术后事件而意外再次入院。在测试样本（n = 12452例患者）上使用多变量逻辑回归确定30天计划外再入院的独立预测因素。随后，根据β系数对预测因素进行加权，以生成一个基于整数的预测再入院的临床风险评分（CRS），并使用验证样本（n = 5337例患者）的受试者操作特征（ROC）分析对其进行验证。
30天计划外再入院率为4.7%。独立危险因素包括open-VHR手术时的住院状态、手术时间、肠粘连松解术、体重过轻、糖尿病、术前贫血、住院时间、慢性阻塞性肺疾病、出血性疾病史、伴有坏疽的疝和腹壁成形术（所有P < 0.05）。验证队列的ROC分析得出曲线下面积为0.71，这表明该预测模型的准确性。每个5个风险分层内的预测发病率与验证样本中的观察发病率在统计学上相似（P = 0.18），进一步突出了该模型的准确性。
我们提出了一个经过验证的open-VHR术后计划外再入院风险分层工具。未来的研究应确定实施我们的CRS是否能优化开放性腹疝修补术患者的安全性并降低再入院率。
