Background Intravenous (IV) fluid contamination is a common cause of preanalytical error that can delay or misguide treatment decisions, leading to patient harm. Current approaches for detecting contamination rely on delta checks, which require a prior result, or manual technologist intervention, which is inefficient and vulnerable to human error. Supervised machine learning may provide a means to detect contamination, but its implementation is hindered by its reliance on expert-labeled training data. An automated approach that is accurate, reproducible, and practical is needed. Methods A total of 25 747 291 basic metabolic panel (BMP) results from 312 721 patients were obtained from the laboratory information system (LIS). A Uniform Manifold Approximation and Projection (UMAP) model was trained and tested using a combination of real patient data and simulated IV fluid contamination. To provide an objective metric for classification, an “enrichment score” was derived and its performance assessed. Our current workflow was compared to UMAP predictions using expert chart review. Results UMAP embeddings from real patient results demonstrated outliers suspicious for IV fluid contamination when compared with the simulated contamination's embeddings. At a flag rate of 3 per 1000 results, the positive predictive value (PPV) was adjudicated to be 0.78 from 100 consecutive positive predictions. Of these, 58 were previously undetected by our current clinical workflows, with 49 BMPs displaying a total of 56 critical results. Conclusions Accurate and automatable detection of IV fluid contamination in BMP results is achievable without curating expertly labeled training data.

中文翻译：

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使用无监督机器学习自动检测静脉输液污染


背景 静脉 (IV) 液体污染是分析前错误的常见原因，可能会延迟或误导治疗决策，从而导致患者受伤。目前检测污染的方法依赖于增量检查（需要事先获得结果）或人工技术人员干预（效率低下且容易出现人为错误）。监督机器学习可能提供一种检测污染的方法，但其实施因依赖专家标记的训练数据而受到阻碍。需要一种准确、可重复且实用的自动化方法。方法从实验室信息系统（LIS）中获取312 721例患者的25 747 291份基础代谢检测（BMP）结果。使用真实患者数据和模拟静脉输液污染的组合来训练和测试统一流形逼近和投影 (UMAP) 模型。为了提供分类的客观指标，得出了“丰富分数”并评估了其性能。使用专家图表审查将我们当前的工作流程与 UMAP 预测进行比较。结果 与模拟污染的嵌入相比，来自真实患者结果的 UMAP 嵌入表明存在可疑 IV 液体污染的异常值。按照每 1000 个结果 3 个的标记率，100 个连续阳性预测的阳性预测值 (PPV) 被判定为 0.78。其中，58 个之前未被我们当前的临床工作流程检测到，其中 49 个 BMP 总共显示了 56 个关键结果。结论 无需整理经过专业标记的训练数据，即可实现 BMP 结果中 IV 液体污染的准确、自动化检测。