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: 1118061
• 负责人: Todd Leen
• 金额: $ 50万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118061&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疗信息的主要来源。 此类测试错误会导致延误、额外的临床评估、额外的费用，有时还会导致错误的治疗，从而增加患者的风险。 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非源于仪器校准错误。临床实验室错误影响约 0.5% 采集的样本。 其中，大约 75% 的临床实验室测试错误源自样品到达分析仪器之前的样品采集、运输和储存过程，即预分析阶段。 然而，医院临床测试实验室的质量控制措施标准仅监控仪器校准，因此完全忽视预分析阶段引入的样品故障，而大多数错误都是在该阶段产生的。 来自患者样本的数据，而不是仪器校准检查，是检测预分析阶段引入的故障的关键。 目前的方法要么对错误非常不敏感，以至于无法可靠地检测样本错误，要么经常将正常样本标记为有错误。该项目汇集了来自俄勒冈健康与科学大学和东北大学的跨学科研究团队，他们拥有机器学习、信号处理和实验室医学方面的专业知识，开发和应用统计机器学习技术，利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。 开发可靠的实验室错误统计检测器的主要障碍是标记样本的成本和低错误率。 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，包括有缺陷的和无缺陷的样本。确定哪些测试有问题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）——鉴于故障率较低，这是一项耗时且经济上不可行的前景。 该项目通过主动学习范例来解决这一挑战，该范例用于选择（重点是稀有类别）数据子集以供人类专家进行标记。 该项目专注于慢性肾脏疾病，因为其医学重要性和俄勒冈健康与科学大学的大型数据存储库。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，本研究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，从而大规模部署，对实验室成本、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/。