Laboratory of Clinical Chemistry and Hematology, Jeroen Bosch Hospital, 's Hertogenbosch, the Netherlands.
Diagnostic Image Analysis Group, Radboudumc, Nijmegen, the Netherlands.
Clin Chem Lab Med. 2022 Mar 8;60(12):1921-1928. doi: 10.1515/cclm-2021-1194. Print 2022 Nov 25.
Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP).
Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool.
Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day.
Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.
用于解释实验室检验结果的计算算法可以为医生和医学检验专家提供支持。本研究的目的是开发、实施和评估一种机器学习算法，该算法仅使用一组基本的实验室检测（即全血细胞计数和 C 反应蛋白（CRP）），自动评估贫血初级保健患者低体铁储存（由低铁蛋白血浆水平反映）的风险。
使用贫血初级保健患者的实验室测量值来开发和验证机器学习算法。将该算法的性能与来自三家大型教学医院的 12 位医学检验专家进行比较，这些专家根据实验室检测报告（全血细胞计数和 CRP）预测贫血患者是否存在低铁蛋白水平。在第二轮评估中，将算法结果作为决策支持工具提供给医学检验专家。
基于两种不同的化学分析仪，开发了两种用于预测低铁蛋白浓度的独立算法，ROC 曲线下面积分别为 0.92（西门子）和 0.90（罗氏）。与算法相比，医学检验专家预测低铁蛋白浓度的准确性较低，即使在知道算法作为支持工具的输出时也是如此。该算法在实验室系统中的实施平均每天导致一个新的缺铁性诊断。
使用基于常规实验室检测结果的机器学习算法可以准确预测贫血患者的低铁蛋白水平。此外，该算法在实验室系统中的实施减少了原本无法识别的缺铁症数量。
