Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests
| العنوان: | Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests |
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| المؤلفون: | Jason Hom, Jonathan H. Chen, Santhosh Balasubramanian, Song Xu, Shivaal Roy, Lee F. Schroeder, Nader Najafi |
| المصدر: | JAMA Network Open |
| بيانات النشر: | American Medical Association (AMA), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Adult, Male, medicine.medical_specialty, Health Informatics, 030204 cardiovascular system & hematology, Sensitivity and Specificity, Blood Urea Nitrogen, Machine Learning, Hemoglobins, Leukocyte Count, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Predictive Value of Tests, Internal medicine, medicine, Humans, 030212 general & internal medicine, Original Investigation, Aged, Retrospective Studies, Glycated Hemoglobin, L-Lactate Dehydrogenase, Receiver operating characteristic, Clinical Laboratory Techniques, business.industry, Research, Troponin I, Correction, Diagnostic test, Retrospective cohort study, General Medicine, Middle Aged, University hospital, Predictive value, 3. Good health, Hospitalization, Online Only, Laboratory test, ROC Curve, chemistry, Area Under Curve, Predictive value of tests, Female, Other, Glycated hemoglobin, business |
| الوصف: | This diagnostic study uses machine learning models to assess the prevalence of low-yield inpatient laboratory tests. Key Points Question How prevalent are low-yield inpatient diagnostic laboratory tests for which results are predictable with machine learning models? Findings In this diagnostic study of 191 506 inpatients from 3 tertiary academic medical centers, common low-yield inpatient diagnostic laboratory test results were systematically identified through data-driven methods and personalized predictions. Meaning The findings suggest that data-driven methods can make explicit the level of uncertainty and expected information gain from diagnostic tests, with the potential to encourage useful testing and discourage low-value testing that can incur direct cost and indirect harm. 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. |
| تدمد: | 2574-3805 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e5b65545c3b7db61d1925b5c4ed5644Test https://doi.org/10.1001/jamanetworkopen.2019.10967Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....9e5b65545c3b7db61d1925b5c4ed5644 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 25743805 |
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