首页 > 最新文献

Clinical chemistry最新文献

英文 中文
Reflecting on 70 Years of Clinical Chemistry.
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-02-03 DOI: 10.1093/clinchem/hvae209
Jason Y Park
{"title":"Reflecting on 70 Years of Clinical Chemistry.","authors":"Jason Y Park","doi":"10.1093/clinchem/hvae209","DOIUrl":"10.1093/clinchem/hvae209","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"227-229"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
New American Society of Hematology Thrombophilia Guidelines Could Provoke Surge in Laboratory Testing.
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-02-03 DOI: 10.1093/clinchem/hvae167
Anna E Merrill, Steven R Lentz
{"title":"New American Society of Hematology Thrombophilia Guidelines Could Provoke Surge in Laboratory Testing.","authors":"Anna E Merrill, Steven R Lentz","doi":"10.1093/clinchem/hvae167","DOIUrl":"https://doi.org/10.1093/clinchem/hvae167","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"71 2","pages":"337-338"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
dmTGS: Precise Targeted Enrichment Long-Read Sequencing Panel for Tandem Repeat Detection. dmTGS:用于串联重复检测的精确靶向富集长读测序面板。
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-02-03 DOI: 10.1093/clinchem/hvae164
Kang Yang, Yue Liu, Ji Zhang, Qian Yu, Feng Xu, Jiyuan Liu, Yuting Li, Xiaojie Zhang, Zhiqiang Wang, Ning Wang, Yuezhen Li, Yan Shi, Wan-Jin Chen

Background: Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.

Methods: We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.

Results: For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.

Conclusions: dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.

背景:串联重复序列(TRs)在人类基因组中含量丰富,并与重复扩增疾病相关。我们的研究旨在利用靶向长读程测序技术开发一个串联重复序列面板,以评估与这些疾病相关的已知TRs,并评估其临床实用性:我们利用 PacBio Sequel II 平台为 70 个 TR 位点开发了一个靶向长读数测序面板,称为动态突变第三代测序(dmTGS)。我们检测了 108 个疑似重复扩增疾病的样本,并将结果与传统分子方法进行了比较:对于 108 个样本,dmTGS 每个样本平均获得 8000 个高保真读数,平均读数长度为 4.7 kb,读数质量为 99.9%。dmTGS 在区分等位基因扩增和正常以及准确量化重复数方面优于重复引物-PCR 和荧光扩增片段长度分析-PCR。该方法与确证方法的一致性很高(rlinear = 0.991,P < 0.01),对 FMR1 CGG 预突变的检测灵敏度为 1%,对完全突变的检测灵敏度为 5%。对于 PLIN4 基因中的可变数目 TR,dmTGS 能精确鉴定出重复次数和序列图案。在筛查 57 名疑似遗传性肌肉疾病患者时,dmTGS 证实了 GIPC1、NOTCH2NLC、NUTM2B-AS1/LOC642361 和 DMPK 等基因中的重复扩展。结论:dmTGS 能准确检测出与重复扩增疾病相关的重复大小和中断图案,与传统的分子方法相比表现出更优越的性能。
{"title":"dmTGS: Precise Targeted Enrichment Long-Read Sequencing Panel for Tandem Repeat Detection.","authors":"Kang Yang, Yue Liu, Ji Zhang, Qian Yu, Feng Xu, Jiyuan Liu, Yuting Li, Xiaojie Zhang, Zhiqiang Wang, Ning Wang, Yuezhen Li, Yan Shi, Wan-Jin Chen","doi":"10.1093/clinchem/hvae164","DOIUrl":"10.1093/clinchem/hvae164","url":null,"abstract":"<p><strong>Background: </strong>Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.</p><p><strong>Methods: </strong>We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.</p><p><strong>Results: </strong>For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.</p><p><strong>Conclusions: </strong>dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"319-331"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Proteomic Prediction Models. 蛋白质组学预测模型。
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-02-03 DOI: 10.1093/clinchem/hvae207
Patrick M Bossuyt
{"title":"Proteomic Prediction Models.","authors":"Patrick M Bossuyt","doi":"10.1093/clinchem/hvae207","DOIUrl":"10.1093/clinchem/hvae207","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"238-240"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142806003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Persistent Unexplained Polyclonal IgA Gammopathy in a Patient with Multiple Myeloma.
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-02-03 DOI: 10.1093/clinchem/hvae136
Paul E Young, Heba Badr, Anthony O Okorodudu
{"title":"Persistent Unexplained Polyclonal IgA Gammopathy in a Patient with Multiple Myeloma.","authors":"Paul E Young, Heba Badr, Anthony O Okorodudu","doi":"10.1093/clinchem/hvae136","DOIUrl":"https://doi.org/10.1093/clinchem/hvae136","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"71 2","pages":"335-336"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Small Remnants versus Large Triglyceride-Rich Lipoproteins in Risk of Atherosclerotic Cardiovascular Disease.
IF 7.1 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-01-30 DOI: 10.1093/clinchem/hvae222
Benjamin N Wadström, Anders B Wulff, Kasper M Pedersen, Børge G Nordestgaard

Background: Small remnants may penetrate the arterial intima more efficiently compared to large triglyceride-rich lipoproteins (TGRL). We tested the hypothesis that the importance of remnant cholesterol for the risk of atherosclerotic cardiovascular disease (ASCVD) may depend on the size of the remnants and TGRL carrying cholesterol.

Methods: The cholesterol content of small remnants and large TGRL were measured in 25 572 individuals from the Copenhagen General Population Study (2003-2015) and in 222 721 individuals from the UK Biobank (2006-2010) using nuclear magnetic resonance spectroscopy. In the Copenhagen cohort during up to 15 years of follow-up and in the UK Biobank cohort during up to 16 years of follow-up, the numbers of individuals diagnosed with ASCVD (=myocardial infarction, ischemic stroke, and peripheral artery disease) in national health registries were 3869 and 11 424, respectively.

Results: Compared to individuals with low cholesterol content in both small remnants and large TGRL (cutpoints were median cholesterol content), multivariable-adjusted hazard ratios for risk of ASCVD were 1.21 (95% confidence interval: 1.07-1.37) for individuals with high cholesterol content in small remnants only and 0.94 (0.83-1.07) for individuals with high cholesterol content in large TGRL only; the multivariable-adjusted hazard ratio for risk of ASCVD per 10 percentile-units higher cholesterol content in small remnants vs that in large TGRL was 1.04 (1.01-1.07). In the UK Biobank cohort, corresponding hazard ratios were 1.11 (1.03-1.20), 1.01 (0.93-1.09), and 1.05 (1.04-1.07), respectively.

Conclusion: The importance of remnant cholesterol for the risk of ASCVD may depend on the size of the TGRL and remnants carrying cholesterol.

{"title":"Small Remnants versus Large Triglyceride-Rich Lipoproteins in Risk of Atherosclerotic Cardiovascular Disease.","authors":"Benjamin N Wadström, Anders B Wulff, Kasper M Pedersen, Børge G Nordestgaard","doi":"10.1093/clinchem/hvae222","DOIUrl":"https://doi.org/10.1093/clinchem/hvae222","url":null,"abstract":"<p><strong>Background: </strong>Small remnants may penetrate the arterial intima more efficiently compared to large triglyceride-rich lipoproteins (TGRL). We tested the hypothesis that the importance of remnant cholesterol for the risk of atherosclerotic cardiovascular disease (ASCVD) may depend on the size of the remnants and TGRL carrying cholesterol.</p><p><strong>Methods: </strong>The cholesterol content of small remnants and large TGRL were measured in 25 572 individuals from the Copenhagen General Population Study (2003-2015) and in 222 721 individuals from the UK Biobank (2006-2010) using nuclear magnetic resonance spectroscopy. In the Copenhagen cohort during up to 15 years of follow-up and in the UK Biobank cohort during up to 16 years of follow-up, the numbers of individuals diagnosed with ASCVD (=myocardial infarction, ischemic stroke, and peripheral artery disease) in national health registries were 3869 and 11 424, respectively.</p><p><strong>Results: </strong>Compared to individuals with low cholesterol content in both small remnants and large TGRL (cutpoints were median cholesterol content), multivariable-adjusted hazard ratios for risk of ASCVD were 1.21 (95% confidence interval: 1.07-1.37) for individuals with high cholesterol content in small remnants only and 0.94 (0.83-1.07) for individuals with high cholesterol content in large TGRL only; the multivariable-adjusted hazard ratio for risk of ASCVD per 10 percentile-units higher cholesterol content in small remnants vs that in large TGRL was 1.04 (1.01-1.07). In the UK Biobank cohort, corresponding hazard ratios were 1.11 (1.03-1.20), 1.01 (0.93-1.09), and 1.05 (1.04-1.07), respectively.</p><p><strong>Conclusion: </strong>The importance of remnant cholesterol for the risk of ASCVD may depend on the size of the TGRL and remnants carrying cholesterol.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Role of Laboratory Medicine in Improving Maternal Health Outcomes and Reducing Disparities.
IF 9.3 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-01-29 DOI: 10.1093/clinchem/hvaf002
Vahid Azimi,Ann M Gronowski
{"title":"The Role of Laboratory Medicine in Improving Maternal Health Outcomes and Reducing Disparities.","authors":"Vahid Azimi,Ann M Gronowski","doi":"10.1093/clinchem/hvaf002","DOIUrl":"https://doi.org/10.1093/clinchem/hvaf002","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"147 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How Can Digital PCR Support the Rapid Development of New Detection Tests in Future Pandemics? 数字PCR如何支持未来流行病新检测方法的快速发展?
IF 9.3 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-01-21 DOI: 10.1093/clinchem/hvae225
Helene Péré,David Veyer,Valerie Taly
{"title":"How Can Digital PCR Support the Rapid Development of New Detection Tests in Future Pandemics?","authors":"Helene Péré,David Veyer,Valerie Taly","doi":"10.1093/clinchem/hvae225","DOIUrl":"https://doi.org/10.1093/clinchem/hvae225","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"27 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study. 基于宿主基因表达秩的集成机器学习算法对急性细菌和病毒感染的鲁棒诊断:多队列模型开发和验证研究。
IF 9.3 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-01-21 DOI: 10.1093/clinchem/hvae220
Yifei Shen,Dongsheng Han,Wenxin Qu,Fei Yu,Dan Zhang,Yifan Xu,Enhui Shen,Qinjie Chu,Michael P Timko,Longjiang Fan,Shufa Zheng,Yu Chen
BACKGROUNDThe accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.METHODSTo improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort.RESULTSAfter selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly.CONCLUSIONSOur study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.
背景准确和及时的感染诊断对于改善患者预后和预防细菌耐药至关重要。宿主基因表达谱作为一种诊断感染的方法,在帮助早期和准确诊断感染方面具有很大的潜力。方法为了提高感染诊断的准确性,我们开发了一种基于秩的集成机器学习算法,用于通过宿主基因表达模式诊断感染。采用11个数据集作为训练数据集进行方法开发,并通过多队列训练样本对感染诊断算法进行优化。9个数据集作为该方法的独立验证数据集。我们在一项前瞻性临床队列研究中进一步验证了感染诊断的诊断能力。结果根据基因表达等级选择100个特征基因进行感染预测后,我们使用接收者操作特征曲线下的非感染vs感染区域(曲线下面积[AUC] 0.95 [95% CI, 0.93-0.97])和细菌vs病毒AUC 0.95 (95% CI, 0.93-0.97)训练分类器。然后,我们将非感染/感染分类器与细菌/病毒分类器结合使用,建立了鉴别感染诊断模型。细菌感染和病毒感染的敏感性分别为0.931和0.872,特异性分别为0.963和0.929。然后,我们将感染诊断应用于前瞻性临床队列(n = 517),发现它对95%的样本进行了正确分类。结论我们的研究表明,感染诊断算法是一种强大而稳健的工具,可以准确识别现实世界患者群体中的感染,具有深刻改善感染诊断领域临床护理的潜力。
{"title":"Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study.","authors":"Yifei Shen,Dongsheng Han,Wenxin Qu,Fei Yu,Dan Zhang,Yifan Xu,Enhui Shen,Qinjie Chu,Michael P Timko,Longjiang Fan,Shufa Zheng,Yu Chen","doi":"10.1093/clinchem/hvae220","DOIUrl":"https://doi.org/10.1093/clinchem/hvae220","url":null,"abstract":"BACKGROUNDThe accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.METHODSTo improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort.RESULTSAfter selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly.CONCLUSIONSOur study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"10 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting 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 9.3 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Pub Date : 2025-01-11 DOI: 10.1093/clinchem/hvae210
Brendan V Graham, Stephen R Master, Amrom E Obstfeld, Robert B Wilson
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.
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。
{"title":"A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting","authors":"Brendan V Graham, Stephen R Master, Amrom E Obstfeld, Robert B Wilson","doi":"10.1093/clinchem/hvae210","DOIUrl":"https://doi.org/10.1093/clinchem/hvae210","url":null,"abstract":"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.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"26 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Clinical chemistry
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1