Background Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter results across multiple analytes. A recently reported unsupervised learning approach was more sensitive than routine workflows, but still lacked sensitivity to mild but significant contamination. Here, we leverage ensemble learning to more sensitively detect contaminated results using an approach which is explainable and generalizable across institutions. Methods An ensemble-based machine learning pipeline of general and fluid-specific models was trained on real-world and simulated contamination and internally and externally validated. Benchmarks for performance assessment were derived from in silico simulations, in vitro experiments, and expert review. Fluid-specific regression models estimated contamination severity. SHapley Additive exPlanation (SHAP) values were calculated to explain specimen-level predictions, and algorithmic fairness was evaluated by comparing flag rates across demographic and clinical subgroups. Results The sensitivities, specificities, and Matthews correlation coefficients were 0.858, 0.993, and 0.747 for the internal validation set, and 1.00, 0.980, and 0.387 for the external set. SHAP values provided plausible explanations for dextrose- and ketoacidosis-related hyperglycemia. Flag rates from the pipeline were higher than the current workflow, with improved detection of contamination events expected to exceed allowable limits for measurement error and reference change values. Conclusions An accurate, generalizable, and explainable ensemble-based machine learning pipeline was developed and validated for sensitively detecting IV fluid contamination. Implementing this pipeline would help identify errors that are poorly detected by current clinical workflows and a previously described unsupervised machine learning-based method.

中文翻译：

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对集成学习方法的前瞻性和外部验证，以灵敏地检测基础代谢组中的静脉液体污染


背景 临床标本中的静脉 （IV） 液污染在检测到时会给实验室带来操作负担，如果未被发现，可能会对患者造成伤害。即使是轻微的污染通常也足以显著改变多种分析物的结果。最近报道的一种无监督学习方法比常规工作流程更敏感，但仍然对轻微但严重的污染缺乏敏感性。在这里，我们利用集成学习，使用一种可解释和跨机构推广的方法更灵敏地检测受污染的结果。方法 在真实世界和模拟污染上训练了通用模型和流体特定模型的基于集成的机器学习管道，并进行了内部和外部验证。性能评估的基准来自计算机模拟、体外实验和专家评审。流体特异性回归模型估计了污染的严重程度。计算 SHapley 加法解释 （SHAP） 值以解释样本水平的预测，并通过比较人口统计学和临床亚组的标志率来评估算法公平性。结果 内部验证集的敏感性、特异性和 Matthews 相关系数分别为 0.858 、 0.993 和 0.747，外部验证集的敏感性、特异性和 Matthews 相关系数为 1.00 、 0.980 和 0.387。SHAP 值为葡萄糖酸中毒和酮症酸中毒相关的高血糖提供了合理的解释。来自管道的标记率高于当前工作流程，改进的污染事件检测预计将超过测量误差和参考变化值的允许限值。 结论 开发并验证了一种准确、可推广且可解释的基于集成 （ensemble） 的机器学习管道，用于灵敏地检测 IV 液污染。实施此管道将有助于识别当前临床工作流程和之前描述的基于无监督机器学习的方法无法检测到的错误。