Mining the Age-Dependent Reference Intervals of B Vitamins from Routine Laboratory Test Results

Objective: In our academic intensive care unit (ICU), there is excess ordering of routine laboratory tests. This is partially due to a lack of transparency of laboratory-processing costs and to the admission order plans that favor daily laboratory test orders. We hypothesized that a program that involves physician and staff education and alters the current ICU order sets will lead to a sustained decrease in routine laboratory test ordering. Design: Prospective cohort study. Setting: Academic closed medical ICU (MICU). Patients: All patients admitted to the MICU. Methods: We consistently educated residents, faculty, and staff about laboratory test costs. We removed the daily laboratory test option from the admission order sets and asked residents to order needed laboratory test results every day. We only allowed the G3+I-STAT (arterial blood gas only) cartridges in the MICU in hopes of decreasing duplicative laboratory test results. We added laboratory review to the daily rounding checklist. Measurement and Main Results: Total number of laboratory tests per patient-day decreased from 39.43 to an average of 26.74 ( P <.001) over a 9-month period. The number of iSTAT laboratory tests per patient-day decreased from 7.37 to an average of 1.16 ( P < .001) over the same time period. The number of iSTAT/central laboratory processing duplicative laboratory tests per patient-day decreased from 0.17 to an average of 0.01 ( P < .001). The percentage of patients who have daily laboratory test orders decreased from 100% to an average of 11.94% ( P <. 001). US$123 436 in direct savings and US$258 035 dollars in indirect savings could be achieved with these trends. Intensive care unit morbidity and mortality were not impacted. Conclusion: A simple technique of resident, nursing, and ancillary staff education, combined with alterations in order sets using electronic medical records, can lead to a sustained reduction in laboratory test utilization over time and to significant cost savings without affecting patient safety.

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High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2020-1371 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jakob Zierk ◽

Hannsjörg Baum ◽

Alexander Bertram ◽

Martin Boeker ◽

Armin Buchwald ◽

...

Keyword(s):

Information Systems ◽

Laboratory Test ◽

Clinical Decision Making ◽

Age Groups ◽

Clinical Decision ◽

Reference Intervals ◽

Test Results ◽

Laboratory Test Results ◽

Glutamyl Transferase ◽

Laboratory Information Systems

Abstract Objectives Assessment of children’s laboratory test results requires consideration of the extensive changes that occur during physiological development and result in pronounced sex- and age-specific dynamics in many biochemical analytes. Pediatric reference intervals have to account for these dynamics, but ethical and practical challenges limit the availability of appropriate pediatric reference intervals that cover children from birth to adulthood. We have therefore initiated the multi-center data-driven PEDREF project (Next-Generation Pediatric Reference Intervals) to create pediatric reference intervals using data from laboratory information systems. Methods We analyzed laboratory test results from 638,683 patients (217,883–982,548 samples per analyte, a median of 603,745 test results per analyte, and 10,298,067 test results in total) performed during patient care in 13 German centers. Test results from children with repeat measurements were discarded, and we estimated the distribution of physiological test results using a validated statistical approach (kosmic). Results We report continuous pediatric reference intervals and percentile charts for alanine transaminase, aspartate transaminase, lactate dehydrogenase, alkaline phosphatase, γ-glutamyl-transferase, total protein, albumin, creatinine, urea, sodium, potassium, calcium, chloride, anorganic phosphate, and magnesium. Reference intervals are provided as tables and fractional polynomial functions (i.e., mathematical equations) that can be integrated into laboratory information systems. Additionally, Z-scores and percentiles enable the normalization of test results by age and sex to facilitate their interpretation across age groups. Conclusions The provided reference intervals and percentile charts enable precise assessment of laboratory test results in children from birth to adulthood. Our findings highlight the pronounced dynamics in many biochemical analytes in neonates, which require particular consideration in reference intervals to support clinical decision making most effectively.

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Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning

10.1101/2020.06.17.20133892 ◽

2020 ◽

Cited By ~ 1

Author(s):

He Sarina Yang ◽

Ljiljana V. Vasovic ◽

Peter Steel ◽

Amy Chadburn ◽

Yu Hou ◽

...

Keyword(s):

Machine Learning ◽

Laboratory Test ◽

Patient Care ◽

Characteristic Curve ◽

Rapid Identification ◽

Routine Laboratory ◽

Test Results ◽

Rt Pcr ◽

Diagnostic Strategies ◽

Laboratory Test Results

AbstractBackgroundAccurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results of this test are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.MethodWe developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,346 SARS-CoV-2 RT-PCR tested patients (1,394 positive and 1,952 negative) evaluated at a large metropolitan hospital.ResultsThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days.ConclusionThis model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. This may facilitate patient care and quarantine, indicate who requires retesting, and direct personal protective equipment use while awaiting definitive RT-PCR results.

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Personalized reference intervals – statistical approaches and considerations

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-1066 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abdurrahman Coskun ◽

Sverre Sandberg ◽

Ibrahim Unsal ◽

Fulya G. Yavuz ◽

Coskun Cavusoglu ◽

...

Keyword(s):

Laboratory Test ◽

Population Based ◽

Reference Intervals ◽

Biological Variation ◽

Test Results ◽

Data Quality Assessment ◽

Laboratory Test Results ◽

Variation Database ◽

Statistical Approaches

Abstract For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on ‘prediction intervals’ can be applied. The first model utilizes estimates of ‘within-person biological variation’ which are based on an individual’s own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of ‘within-subject biological variation’, which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals’ test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.

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Establishing Reference Intervals for Clinical Laboratory Test Results: Is There a Better Way?

Yearbook of Pathology and Laboratory Medicine ◽

10.1016/s1077-9108(10)79506-2 ◽

2011 ◽

Vol 2011 ◽

pp. 240-241

Author(s):

M.G. Bissell

Keyword(s):

Laboratory Test ◽

Clinical Laboratory ◽

Reference Intervals ◽

Test Results ◽

Clinical Laboratory Test ◽

Laboratory Test Results

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