Shahian David M, Blackstone Eugene H, Edwards Fred H, Grover Frederick L, Grunkemeier Gary L, Naftel David C, Nashef Samer A M, Nugent William C, Peterson Eric D
Lahey Clinic, Burlington, Massachusetts 01805, USA.
Ann Thorac Surg. 2004 Nov;78(5):1868-77. doi: 10.1016/j.athoracsur.2004.05.054.
Differences in medical outcomes may result from disease severity, treatment effectiveness, or chance. Because most outcome studies are observational rather than randomized, risk adjustment is necessary to account for case mix. This has usually been accomplished through the use of standard logistic regression models, although Bayesian models, hierarchical linear models, and machine-learning techniques such as neural networks have also been used. Many factors are essential to insuring the accuracy and usefulness of such models, including selection of an appropriate clinical database, inclusion of critical core variables, precise definitions for predictor variables and endpoints, proper model development, validation, and audit. Risk models may be used to assess the impact of specific predictors on outcome, to aid in patient counseling and treatment selection, to profile provider quality, and to serve as the basis of continuous quality improvement activities.
医疗结果的差异可能源于疾病严重程度、治疗效果或偶然性。由于大多数结果研究是观察性的而非随机的，因此需要进行风险调整以考虑病例组合情况。这通常通过使用标准逻辑回归模型来完成，不过贝叶斯模型、分层线性模型以及神经网络等机器学习技术也已被采用。确保此类模型的准确性和实用性至关重要的因素有很多，包括选择合适的临床数据库、纳入关键核心变量、对预测变量和终点进行精确界定、进行恰当的模型开发、验证及审核。风险模型可用于评估特定预测因素对结果的影响，辅助患者咨询和治疗选择，剖析医疗服务提供者的质量，并作为持续质量改进活动的基础。
