Department of Pathology, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, USA.
Am J Clin Pathol. 2012 Sep;138(3):406-13. doi: 10.1309/AJCPQIRIB3CT1EJV.
Preanalytic laboratory testing errors are often difficult to identify. We demonstrate how laboratories can integrate statistical models with clinical judgment to develop protocols for preanalytic error detection. Specifically, we developed a protocol to identify spuriously elevated glucose values resulting from improper "line draws" or related phlebotomy errors. Using a decision tree-generating algorithm and an annotated set of training data, we generated decision trees to classify critically elevated glucose results as "real" or "spurious" based on available laboratory parameters. Decision trees revealed that a 30-day patient-specific average glucose concentration lower than 186.3 mg/dL (10.3 mmol/L), a current glucose concentration higher than 663 mg/dL (37 mmol/L), and an anion gap lower than 16.5 mEq/L (16.5 mmol/L) suggested a spurious result. We then used the results from the decision tree analysis to inform the implementation of a clinical protocol that significantly improved the laboratory's identification of spurious results. Similar approaches may be useful in developing protocols to identify other errors or to assist in clinical interpretation of results.
分析前实验室检测错误通常难以识别。我们展示了实验室如何将统计模型与临床判断相结合，制定分析前错误检测方案。具体来说，我们开发了一种方案来识别由于不当“划线”或相关采血管错误导致的假性升高血糖值。使用决策树生成算法和注释的训练数据集，我们生成了决策树，根据可用的实验室参数将危急升高的血糖结果分类为“真实”或“假性”。决策树显示，30 天患者特异性平均血糖浓度低于 186.3mg/dL（10.3mmol/L）、当前血糖浓度高于 663mg/dL（37mmol/L）和阴离子间隙低于 16.5mEq/L（16.5mmol/L）提示为假性结果。然后，我们使用决策树分析的结果来实施临床方案，从而显著提高了实验室对假性结果的识别能力。类似的方法可能有助于制定识别其他错误的方案，或协助临床解读结果。
