SHB: Small: Robustly Detecting Clinical Laboratory Errors

SHB：小型：稳健地检测临床实验室错误

• 批准号: 1736497
• 负责人: Todd Leen
• 金额: $ 18.22万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736497&HistoricalAwards=false
• 关键词: SHB; Small; Robustly; Detecting; Clinical

Hospital clinical laboratory tests are a major source of medical information used to diagnose, treat, and monitor patients. Such test errors lead to delays, additional clinical evaluation, additional expense, and sometimes to erroneous treatments that increase risk to patients. One recent study suggests that errors in measured total blood calcium concentration due to instrument mis-calibration alone cost from $60M to $199M annually in the US. However, the vast majority of clinical laboratory errors do not originate in instrument mis-calibration. Clinical laboratory errors affect about 0.5% of samples collected. Of those, approximately 75% of clinical laboratory test errors originate during sample collection, transport, and storage before samples reach the analysis instruments i.e., the pre-analytic phase. However the quality control measures standard in hospital clinical test labs only monitor instrument calibration and are therefore completely blind to sample faults introduced in the pre-analytic phase, where most errors originate. Data derived from patient samples, rather than instrumentation calibration checks, holds the key to detect faults introduced in the pre-analytic phase. Current methods are either so insensitive to errors that they do not detect sample faults reliably, or they routinely flag normal samples as being faulty.This project brings together an interdisciplinary team of researchers from Oregon Health and Science University and Northeastern University with expertise in machine learning, signal processing, and laboratory medicine to develop and apply statistical machine learning technology to reliably detect errors in hospital clinical laboratory tests, using data derived from patient samples. The primary obstacle to developing reliable statistical detectors for lab errors is the cost of labeling samples combined with the low error rate. Developing and evaluating any automated error-detection algorithm requires a sufficient number of samples, both faulty and non-faulty. Determining which tests are faulty requires review of the tests and other patient data (e.g. charts) by a clinical lab expert - a time-consuming and economically unfeasible prospect given the low fault rate. The project addresses this challenge through active learning paradigms used to select, with emphasis on rare classes, subsets of the data for labeling by human experts. The project focuses on chronic kidney disease because of its medical importance and large data repository at Oregon Health and Science University. This research will provide algorithms for clinical lab error detection that will extend to tests used in other disease entities (for example diabetes and heart failure). Ultimately, the error-detection algorithms developed from this research will make their way into clinical laboratory information systems and further into commercialization and thus deployment on a scale significant enough to have widespread positive impact on laboratory costs patient risk. The project provides cross-disciplinary training in statistical pattern recognition and clinical laboratory science for graduate and undergraduate students. Additional information about the project can be found at: http://www.bme.ogi.edu/~tleen/LabErrorDetect/.

医院临床实验室测试是用于诊断、治疗和监测患者的医学信息的主要来源。 这种测试错误导致延迟、额外的临床评估、额外的费用，并且有时导致增加患者风险的错误治疗。 最近的一项研究表明，在美国，仅由于仪器校准错误而导致的总血钙浓度测量误差每年就造成6000万至1.99亿美元的损失。然而，绝大多数临床实验室错误并不源于仪器误校准。临床实验室错误影响约0.5%的样本采集。 其中，约75%的临床实验室检测错误源于样本到达分析仪器之前的样本采集、运输和储存过程，即，预分析阶段。 然而，医院临床检测实验室的质量控制措施标准仅监控仪器校准，因此对分析前阶段引入的样本故障完全视而不见，而大多数错误都是源于分析前阶段。 来自患者样本的数据，而不是仪器校准检查，是检测分析前阶段引入的故障的关键。 目前的方法要么对错误不敏感，无法可靠地检测样本故障，要么常规地将正常样本标记为故障。该项目汇集了来自俄勒冈州健康与科学大学和东北大学的跨学科研究人员团队，他们在机器学习，信号处理，和实验室医学，开发和应用统计机器学习技术，使用来自患者样本的数据可靠地检测医院临床实验室测试中的错误。 为实验室错误开发可靠的统计检测器的主要障碍是标记样品的成本和低错误率。 开发和评估任何自动错误检测算法都需要足够数量的样本，包括故障和非故障样本。确定哪些测试是错误的需要由临床实验室专家审查测试和其他患者数据（例如图表）-鉴于低故障率，这是一种耗时且经济上不可行的前景。 该项目通过主动学习范式来解决这一挑战，该范式用于选择（重点是稀有类）由人类专家标记的数据子集。 该项目的重点是慢性肾脏疾病，因为它的医学重要性和大型数据存储库在俄勒冈州健康与科学大学。这项研究将为临床实验室错误检测提供算法，该算法将扩展到其他疾病实体（例如糖尿病和心力衰竭）中使用的测试。 最终，从这项研究中开发的错误检测算法将进入临床实验室信息系统，并进一步商业化，从而在足够大的规模上部署，对实验室成本和患者风险产生广泛的积极影响。 该项目为研究生和本科生提供统计模式识别和临床实验室科学方面的跨学科培训。有关该项目的更多信息，请访问：http://www.bme.ogi.edu/~tleen/LabErrorDetect/。