Biostatistics Unit, Group Health Research Institute, Seattle, WA 98101, USA.
Epidemiol Infect. 2011 Sep;139(9):1296-306. doi: 10.1017/S0950268810002529. Epub 2010 Nov 19.
In epidemiological studies of community-acquired pneumonia (CAP) that utilize administrative data, cases are typically defined by the presence of a pneumonia hospital discharge diagnosis code. However, not all such hospitalizations represent true CAP cases. We identified 3991 hospitalizations during 1997-2005 in a managed care organization, and validated them as CAP or not by reviewing medical records. To improve the accuracy of CAP identification, classification algorithms that incorporated additional administrative information associated with the hospitalization were developed using the classification and regression tree analysis. We found that a pneumonia code designated as the primary discharge diagnosis and duration of hospital stay improved the classification of CAP hospitalizations. Compared to the commonly used method that is based on the presence of a primary discharge diagnosis code of pneumonia alone, these algorithms had higher sensitivity (81-98%) and positive predictive values (82-84%) with only modest decreases in specificity (48-82%) and negative predictive values (75-90%).
在利用行政数据进行社区获得性肺炎（CAP）的流行病学研究中，病例通常通过肺炎住院诊断代码的存在来定义。然而，并非所有此类住院都代表真正的 CAP 病例。我们在 1997-2005 年期间在一家管理式医疗组织中确定了 3991 例住院病例，并通过审查病历将其验证为 CAP 或非 CAP。为了提高 CAP 识别的准确性，我们使用分类和回归树分析开发了包含与住院相关的其他行政信息的分类算法。我们发现，肺炎代码被指定为主要出院诊断和住院时间可以改善 CAP 住院的分类。与仅基于肺炎主要出院诊断代码存在的常用方法相比，这些算法的敏感性（81-98%）和阳性预测值（82-84%）较高，特异性（48-82%）和阴性预测值（75-90%）仅略有下降。
