多分析仪机器学习模型在儿科全血细胞计数管错误中检测错血

IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Clinical chemistry Pub Date : 2025-01-11 DOI:10.1093/clinchem/hvae210
Brendan V Graham, Stephen R Master, Amrom E Obstfeld, Robert B Wilson
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Model performance was also compared to a pre-positive patient identification (pre-PPID) dataset. Results An Extreme Gradient Boosting (XGBoost) model with minimal preprocessing performed the best for both complete blood count with differential white cell count (CBC with Diff) tests (accuracy 0.9715) and complete blood count without differential white cell count (CBC without Diff) tests (accuracy 0.9647). Assessment on a downsampled, “low prevalence” validation data set resulted in estimated positive predictive values ranging from 0.01 to 0.67 (CBC with Diff) and 0.01 to 0.75 (CBC without Diff), depending on the probability threshold chosen. A comparison of prospective performance to PPID data demonstrated a large decrease in estimated WBIT errors. Conclusions We find that ML models can accurately predict WBITs in a primarily pediatric setting. 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引用次数: 0

摘要

多分析物机器学习(ML)模型可以潜在地识别以前无法检测到的错血管(WBIT)错误,改进当前的单分析物delta检查方法。然而,WBIT检测模型的性能尚未在现实世界的低患病率环境中进行评估。为了估计真实世界的阳性预测值,我们提出了一种方法,通过评估缺失数据的影响和使用“低患病率”验证数据集来评估WBIT检测模型。方法:我们在儿科环境中使用各种预测因子训练了一系列模型规格。我们在一系列概率阈值的修改后的“低流行率”验证数据集上评估了表现最佳的模型。还将模型性能与预阳性患者识别(pre-PPID)数据集进行了比较。结果经最小预处理的极限梯度增强(XGBoost)模型对全血细胞计数伴差异白细胞计数(CBC伴Diff)检测(准确性0.9715)和全血细胞计数伴差异白细胞计数(CBC伴Diff)检测(准确性0.9647)均有最佳效果。根据选择的概率阈值,对下采样的“低患病率”验证数据集进行评估,得出的估计阳性预测值范围为0.01至0.67(有Diff的CBC)和0.01至0.75(没有Diff的CBC)。对预期性能与PPID数据的比较表明,估计的WBIT误差大大降低。结论:我们发现ML模型可以准确预测以儿科为主的WBITs。在一系列概率阈值范围内评估模型性能可以最大限度地减少误报的数量,同时仍然提供额外的安全优势。实施PPID后估计WBITS的减少表明,对于在收集实验室标本时不使用PPID的医院来说,WBIT模型具有潜在的安全益处。
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A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting
Background Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a “low prevalence” validation data set. Methods We trained a range of model specifications using various predictors in a pediatric setting. We assessed the top-performing model on a modified, “low prevalence” validation data set across a range of probability thresholds. Model performance was also compared to a pre-positive patient identification (pre-PPID) dataset. Results An Extreme Gradient Boosting (XGBoost) model with minimal preprocessing performed the best for both complete blood count with differential white cell count (CBC with Diff) tests (accuracy 0.9715) and complete blood count without differential white cell count (CBC without Diff) tests (accuracy 0.9647). Assessment on a downsampled, “low prevalence” validation data set resulted in estimated positive predictive values ranging from 0.01 to 0.67 (CBC with Diff) and 0.01 to 0.75 (CBC without Diff), depending on the probability threshold chosen. A comparison of prospective performance to PPID data demonstrated a large decrease in estimated WBIT errors. Conclusions We find that ML models can accurately predict WBITs in a primarily pediatric setting. Evaluating model performance across a range of probability thresholds minimizes the number of false positives while still providing added safety benefits. The decrease in estimated WBITS post-PPID implementation shows the potential safety benefits of a WBIT model for hospitals not using PPID when collecting laboratory specimens.
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来源期刊
Clinical chemistry
Clinical chemistry 医学-医学实验技术
CiteScore
11.30
自引率
4.30%
发文量
212
审稿时长
1.7 months
期刊介绍: Clinical Chemistry is a peer-reviewed scientific journal that is the premier publication for the science and practice of clinical laboratory medicine. It was established in 1955 and is associated with the Association for Diagnostics & Laboratory Medicine (ADLM). The journal focuses on laboratory diagnosis and management of patients, and has expanded to include other clinical laboratory disciplines such as genomics, hematology, microbiology, and toxicology. It also publishes articles relevant to clinical specialties including cardiology, endocrinology, gastroenterology, genetics, immunology, infectious diseases, maternal-fetal medicine, neurology, nutrition, oncology, and pediatrics. In addition to original research, editorials, and reviews, Clinical Chemistry features recurring sections such as clinical case studies, perspectives, podcasts, and Q&A articles. It has the highest impact factor among journals of clinical chemistry, laboratory medicine, pathology, analytical chemistry, transfusion medicine, and clinical microbiology. The journal is indexed in databases such as MEDLINE and Web of Science.
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