Dataset Information

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

ABSTRACT: Importance:Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking. Objective:To systematically identify low-yield inpatient laboratory testing through personalized predictions. Design, Setting, and Participants:In this retrospective diagnostic study with multivariable prediction models, 116?637 inpatients treated at Stanford University Hospital from January 1, 2008, to December 31, 2017, a total of 60?929 inpatients treated at University of Michigan from January 1, 2015, to December 31, 2018, and 13?940 inpatients treated at the University of California, San Francisco from January 1 to December 31, 2018, were assessed. Main Outcomes and Measures:Diagnostic accuracy measures, including sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUROC), of machine learning models when predicting whether inpatient laboratory tests yield a normal result as defined by local laboratory reference ranges. Results:In the recent data sets (July 1, 2014, to June 30, 2017) from Stanford University Hospital (including 22?664 female inpatients with a mean [SD] age of 58.8 [19.0] years and 22?016 male inpatients with a mean [SD] age of 59.0 [18.1] years), among the top 20 highest-volume tests, 792?397 were repeats of orders within 24 hours, including tests that are physiologically unlikely to yield new information that quickly (eg, white blood cell differential, glycated hemoglobin, and serum albumin level). The best-performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95% CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95% CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95% CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95% CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%). Conclusions and Relevance:The findings suggest that low-yield diagnostic testing is common and can be systematically identified through data-driven methods and patient context-aware predictions. Implementing machine learning models appear to be able to quantify the level of uncertainty and expected information gained from diagnostic tests explicitly, with the potential to encourage useful testing and discourage low-value testing that incurs direct costs and indirect harms.

SUBMITTER: Xu S

PROVIDER: S-EPMC6739729 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

Xu Song S Hom Jason J Balasubramanian Santhosh S Schroeder Lee F LF Najafi Nader N Roy Shivaal S Chen Jonathan H JH

JAMA network open 20190904 9

<h4>Importance</h4>Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking.<h4>Objective</h4>To systematically identify low-yield inpatient laboratory testing through personalized predictions.<h4>Design, setting, and participants< ...[more]

PMID: 31509205

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Project description:BACKGROUND: An outbreak of severe acute respiratory syndrome (SARS) began in Canada in February 2003. The initial diagnosis of SARS was based on clinical and epidemiological criteria. During the outbreak, molecular and serologic tests for the SARS-associated coronavirus (SARS-CoV) became available. However, without a "gold standard," it was impossible to determine the usefulness of these tests. We describe how these tests were used during the first phase of the SARS outbreak in Toronto and offer some recommendations that may be useful if SARS returns. METHODS: We examined the results of all diagnostic laboratory tests used in 117 patients admitted to hospitals in Toronto who met the Health Canada criteria for suspect or probable SARS. Focusing on tests for SARS-CoV, we attempted to determine the optimal specimen types and timing of specimen collection. RESULTS: Diagnostic test results for SARS-CoV were available for 110 of the 117 patients. SARS-CoV was detected by means of reverse-transcriptase polymerase chain reaction (RT-PCR) in at least one specimen in 59 (54.1%) of 109 patients. Serologic test results of convalescent samples were positive in 50 (96.2%) of 52 patients for whom paired serum samples were collected during the acute and convalescent phases of the illness. Of the 110 patients, 78 (70.9%) had specimens that tested positive by means of RT-PCR, serologic testing or both methods. The proportion of RT-PCR test results that were positive was similar between patients who met the criteria for suspect SARS (50.8%, 95% confidence interval [CI] 38.4%-63.2%) and those who met the criteria for probable SARS (58.0%, 95% CI 44.2%-70.7%). SARS-CoV was detected in nasopharyngeal swabs in 33 (32.4%) of 102 patients, in stool specimens in 19 (63.3%) of 30 patients, and in specimens from the lower respiratory tract in 10 (58.8%) of 17 patients. INTERPRETATION: These findings suggest that the rapid diagnostic tests in use at the time of the initial outbreak lack sufficient sensitivity to be used clinically to rule out SARS. As tests for SARS-CoV continue to be optimized, evaluation of the clinical presentation and elucidation of a contact history must remain the cornerstone of SARS diagnosis. In patients with SARS, specimens taken from the lower respiratory tract and stool samples test positive by means of RT-PCR more often than do samples taken from other areas.

Project description:BackgroundChagas disease is a public health challenge in Colombia, where only an estimated 1.2% of people at risk have accessed diagnosis, while less than 0.5% of affected people have obtained treatment. The development of simplified diagnostic algorithms would enable progress in access to diagnosis; however, the current diagnostic algorithm relies on at least two laboratory-based tests that require qualified personnel, processing equipment, and infrastructure, which are still generally lacking at the primary care level. Rapid diagnostic tests (RDTs) for Chagas disease could simplify diagnosis, but their performance in the epidemiological context of Colombia is not well known.MethodologyA retrospective analytical observational study of RDTs was performed to estimate the operational characteristics of 11 commercially available RDTs designed for in vitro detection of anti-T. cruzi IgG antibodies. The study was performed under controlled laboratory conditions using human serum samples.Principal findingsEleven RDTs were assessed, ten using 585 serum samples and one using 551 serum samples. Employing the current national diagnostic algorithm as a reference standard for serological diagnosis of chronic infection, the sensitivity of the assessed RDTs ranged from 75.5% to 99.0% (95% CI 70.5-100), while specificity ranged from 70.9% to 100% (95% CI 65.3-100). Most tests (7/11, 63.6%) had sensitivity above 90%, and almost all (10/11, 90.9%) had specificity above 90%. Five RDTs had both sensitivity and specificity above 90%.Conclusions/significanceThe evaluation of these 11 commercially available RDTs under controlled laboratory conditions is a first step in the assessment of the diagnostic performance of RDTs in Colombia. As a next step, field studies will be conducted on available RDTs with sensitivity and specificity greater than 90% in this study, to evaluate performance in real world conditions, with the final goal to allow simplified diagnostic algorithms.

Dataset Information

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

Publications

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

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