Department of Plastic and Reconstructive Surgery, Brown University and Rhode Island Hospital, Providence, RI.
Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA.
Ann Surg. 2019 Sep;270(3):544-553. doi: 10.1097/SLA.0000000000003472.
The aim of this study was to identify procedure-specific risk factors independently associated with incisional hernia (IH) and demonstrate the feasibility of preoperative risk stratification through the use of an IH risk calculator app and decision-support interface.
IH occurs after 10% to 15% of all abdominal surgeries (AS) and remains among the most challenging, seemingly unavoidable complications. However, there is a paucity of readily available, actionable tools capable of predicting IH occurrence at the point-of-care.
Patients (n = 29,739) undergoing AS from 2005 to 2016 were retrospectively identified within inpatient and ambulatory databases at our institution. Surgically treated IH, complications, and costs were assessed. Predictive models were generated using regression analysis and corroborated using a validation group.
The incidence of operative IH was 3.8% (N = 1127) at an average follow-up of 57.9 months. All variables were weighted according to β-coefficients generating 8 surgery-specific predictive models for IH occurrence, all of which demonstrated excellent risk discrimination (C-statistic = 0.76-0.89). IH occurred most frequently after colorectal (7.7%) and vascular (5.2%) surgery. The most common occurring risk factors that increased the likelihood of developing IH were history of AS (87.5%) and smoking history (75%). An integrated, surgeon-facing, point-of-care risk prediction instrument was created in an app for preoperative estimation of hernia after AS.
Operative IH occurred in 3.8% of patients after nearly 5 years of follow-up in a predictable manner. Using a bioinformatics approach, risk models were transformed into 8 unique surgery-specific models. A risk calculator app was developed which stakeholders can access to identify high-risk IH patients at the point-of-care.
本研究旨在确定与切口疝（IH）独立相关的特定手术风险因素，并通过使用 IH 风险计算器应用程序和决策支持界面来证明术前风险分层的可行性。
IH 发生在所有腹部手术（AS）的 10%至 15%之后，仍然是最具挑战性的、看似不可避免的并发症之一。然而，目前缺乏能够在护理点预测 IH 发生的现成、可操作的工具。
在我们机构的住院和门诊数据库中，回顾性地确定了 2005 年至 2016 年接受 AS 的患者（n=29739）。评估了手术治疗 IH、并发症和成本。使用回归分析生成预测模型，并使用验证组进行验证。
IH 的手术发生率为 3.8%（N=1127），平均随访时间为 57.9 个月。根据 β 系数对所有变量进行加权，生成了 8 个用于 IH 发生的手术特异性预测模型，所有模型均显示出良好的风险区分能力（C 统计量=0.76-0.89）。IH 最常发生在结直肠（7.7%）和血管（5.2%）手术后。增加 IH 发生可能性的最常见风险因素是 AS 病史（87.5%）和吸烟史（75%）。在一个术前用于估计 AS 后疝的应用程序中，创建了一个集成的、面向外科医生的、护理点风险预测工具。
在近 5 年的随访中，3.8%的患者发生了手术 IH，其发生方式具有可预测性。使用生物信息学方法，风险模型转化为 8 个独特的手术特异性模型。开发了一种风险计算器应用程序，利益相关者可以使用该应用程序在护理点识别高风险 IH 患者。
