SHB: Small: Robustly Detecting Clinical Laboratory Errors

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。这种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，仅仪器错误校准造成的总血钙浓度测量误差每年就会造成6000万至1.99亿美元的损失。然而，绝大多数临床实验室误差不是源于仪器的错误校准。临床实验室差错影响所采集样本的约0.5%。其中，大约75%的临床实验室测试误差来自于样本到达分析仪器之前的样本收集、运输和储存过程，即分析前阶段。然而，医院临床检验实验室的质量控制措施标准只监测仪器的校准，因此对分析前阶段引入的样品故障完全视而不见，而分析前阶段是大多数误差的来源。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。当前的方法要么对错误非常不敏感，以至于不能可靠地检测出样本错误，要么经常将正常样本标记为有缺陷。该项目汇集了来自俄勒冈健康与科学大学和东北大学的一个跨学科的研究团队，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，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。开发可靠的实验室差错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，包括有故障的和无故障的。确定哪些测试有故障需要临床实验室专家审查测试和其他患者数据(例如图表)-考虑到低故障率，这是一种耗时且在经济上不可行的前景。该项目通过使用主动学习范例来解决这一挑战，该范例用于选择数据的子集供人类专家标记，重点是罕见的类别。该项目专注于慢性肾脏疾病，因为它在医学上具有重要意义，而且俄勒冈健康与科学大学拥有庞大的数据库。这项研究将为临床实验室错误检测提供算法，这些算法将扩展到用于其他疾病实体(例如糖尿病和心力衰竭)的测试。最终，这项研究开发的错误检测算法将进入临床实验室信息系统，并进一步进入商业化，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/.

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/.