开发一种用于头颈鳞状细胞癌手术后结局审计的风险分层工具。
Developing a risk stratification tool for audit of outcome after surgery for head and neck squamous cell carcinoma.
作者信息
Tighe David F, Thomas Alan J, Sassoon Isabel, Kinsman Robin, McGurk Mark
机构信息
Royal Marsden NHS Foundation Trust, London, London, United Kingdom.
University of Brighton, School of Computer Engineering and Mathematics, Brighton, East Sussex, United Kingdom.
出版信息
Head Neck. 2017 Jul;39(7):1357-1363. doi: 10.1002/hed.24769. Epub 2017 Mar 29.
BACKGROUND
Patients treated surgically for head and neck squamous cell carcinoma (HNSCC) represent a heterogeneous group. Adjusting for patient case mix and complexity of surgery is essential if reporting outcomes represent surgical performance and quality of care.
METHODS
A case note audit totaling 1075 patients receiving 1218 operations done for HNSCC in 4 cancer networks was completed. Logistic regression, decision tree analysis, an artificial neural network, and Naïve Bayes Classifier were used to adjust for patient case-mix using pertinent preoperative variables.
RESULTS
Thirty-day complication rates varied widely (34%-51%; P < .015) between units. The predictive models allowed risk stratification. The artificial neural network demonstrated the best predictive performance (area under the curve [AUC] 0.85).
CONCLUSION
Early postoperative complications are a measurable outcome that can be used to benchmark surgical performance and quality of care. Surgical outcome reporting in national clinical audits should be taking account of the patient case mix.
背景
接受手术治疗的头颈部鳞状细胞癌（HNSCC）患者构成一个异质性群体。如果报告的结果要反映手术表现和护理质量，那么对患者病例组合和手术复杂性进行调整至关重要。
方法
完成了一项病例记录审核，共涉及4个癌症网络中1075例接受1218次HNSCC手术的患者。使用逻辑回归、决策树分析、人工神经网络和朴素贝叶斯分类器，通过相关术前变量对患者病例组合进行调整。
结果
各单位之间的30天并发症发生率差异很大（34% - 51%；P < 0.015）。预测模型实现了风险分层。人工神经网络表现出最佳的预测性能（曲线下面积[AUC]为0.85）。
结论
术后早期并发症是一个可衡量的结果，可用于对标手术表现和护理质量。国家临床审计中的手术结果报告应考虑患者病例组合。
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