系统变量简化切口疝风险预测工具。
Systematic variable reduction for simplification of incisional hernia risk prediction instruments.
机构信息
University of Pennsylvania, Division of Plastic Surgery, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
University of Pennsylvania, Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
出版信息
Am J Surg. 2022 Jul;224(1 Pt B):576-583. doi: 10.1016/j.amjsurg.2022.03.003. Epub 2022 Mar 8.
BACKGROUND
Incisional hernia (IH) is a complex, costly and difficult to manage surgical complication. We aim to create an accurate and parsimonious model to assess IH risk, pared down for practicality and translation in the clinical environment.
METHODS
Institutional abdominal surgical patients from 2002 to 2019 were identified (N = 102,281); primary outcome of IH, demographic factors, and comorbidities were extracted. A 32-variable Cox proportional hazards model was generated. Reduced-variable models were created by systematic removal of variables 1-4 and 23-25 at a time.
RESULTS
The c-statistic of the full 32-variable model was 0.7232. Four comorbidities decreased accuracy of the model: COPD, paralysis, cancer and combined autoimmune/hereditary collagenopathy or AAA diagnosis. The model with those 4 comorbidities removed had the highest c-statistic (0.7291). The most reduced model included 7 variables and had a c-statistic of 0.7127.
CONCLUSION
Accuracy of an IH predictive model is only marginally affected by a vast reduction in end-user inputs.
背景
切口疝（IH）是一种复杂、昂贵且难以处理的手术并发症。我们旨在创建一个准确且简洁的模型来评估 IH 风险，以便在临床环境中进行实用性和翻译。
方法
从 2002 年到 2019 年，确定了机构腹部手术患者（N=102281）；提取 IH、人口统计学因素和合并症的主要结局。生成了一个 32 变量的 Cox 比例风险模型。通过系统地删除变量 1-4 和 23-25 一次一个变量，创建了简化变量模型。
结果
全 32 变量模型的 C 统计量为 0.7232。四种合并症降低了模型的准确性：COPD、瘫痪、癌症和联合自身免疫/遗传性胶原病或 AAA 诊断。去除这 4 种合并症的模型具有最高的 C 统计量（0.7291）。最简化的模型包括 7 个变量，C 统计量为 0.7127。
结论
IH 预测模型的准确性仅受到最终用户输入大量减少的轻微影响。
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