Reference intervals: past, present, and future.

Clinical laboratory test results alone are of little value in diagnosing, treating, and monitoring health conditions; as such, a clinically actionable cutoff or reference interval is required to provide context for result interpretation. Healthcare practitioners base their diagnoses, follow-up treatments, and subsequent testing on these reference points. However, they may not be aware of inherent limitations related to the definition and derivation of reference intervals. Laboratorians are responsible for providing the reference intervals they report with results. Yet, the establishment and verification of reference intervals using conventional direct methods are complicated by resource constraints or unique patient demographics. To facilitate standardized reference interval best practices, multiple global scientific societies are actively drafting guidelines and seeking funding to promote these initiatives. Numerous national and international multicenter collaborations demonstrate the ability to leverage combined resources to conduct large reference interval studies by direct methods. However, not all demographics are equally accessible. Novel indirect methods are attractive solutions that utilize computational methods to define reference distributions and reference intervals from mixed data sets of pathologic and non-pathologic patient test results. In an effort to make reference intervals more accurate and personalized, individual-based reference intervals are shown to be more useful than population-based reference intervals in detecting clinically significant analyte changes in a patient that might otherwise go unrecognized when using wider, population-based reference intervals. Additionally, continuous reference intervals can provide more accurate ranges as compared to age-based partitions for individuals that are near the ends of the age partition. The advantages and disadvantages of different reference interval approaches as well as the advancement of non-conventional reference interval studies are discussed in this review.

Preterm to term infant postmenstrual age reference intervals for thyroid-stimulating hormone and free thyroxine.

BACKGROUND: Infants born preterm are affected by a hypothalamic-pituitary-thyroid axis that is immature and still developing as they progress closer to corrected term gestation. Multiple risk factors place preterm infants at risk for a hypothyroid state. However, there is variability in thyroid-stimulating hormone cutoff values and limited data on free thyroxine reference intervals to guide clinicians. METHODS: 1584 thyroid-stimulating hormone and 1576 free thyroxine laboratory samples that were originally collected to screen hospitalized infants for delayed-onset of hypothyroidism were retrospectively evaluated from a group of 1087 infants who ranged in postmenstrual age from 25 to 43 weeks gestation at the time of laboratory sample collection. Median thyroid hormone values and reference intervals were established using R and the mixtools package. RESULTS: Thyroid-stimulating hormone reference intervals remained similar across gestational ages from 0.340-9.681 µIU/mL in 25-27 6/7-week infants to 1.090-7.627 µIU/mL in 40-43-weeks infants. For the same age groups, free thyroxine reference intervals increased from 0.42-0.91 ng/dL to 0.87-1.32 ng/dL. CONCLUSION: The reference intervals identified suggest that infants <31 weeks gestation have a higher thyroid-stimulating hormone and lower free thyroxine level at baseline than previously anticipated. IMPACT: The increasing free thyroxine values in preterm to term infants indicate a maturing hypothalamic-pituitary-thyroid axis. Clinicians need thyroid hormone reference intervals that also vary by postmenstrual age to aid the evaluation of sick preterm infants who are at risk of a delayed hypothyroidism diagnosis that can be missed on the initial newborn screen. This study provides one of the largest samples of thyroid-stimulating hormone and free thyroxine data to establish reference intervals in preterm infants. Clinicians may utilize the identified postmenstrual age-based reference intervals to inform follow-up thyroid testing in preterm infants at several weeks postnatal age.

Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform.

BACKGROUND: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. OBJECTIVE: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. METHODS: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. RESULTS: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. CONCLUSIONS: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.

Fast, simple, and sensitive high-performance liquid chromatography method for measuring vitamins A and E in human blood plasma.

Vitamins A and E are fat-soluble vitamins that play important roles in several physiological processes. Monitoring their concentrations is needed to detect deficiency and guide therapy. In this study, we developed a high-performance liquid chromatography method to measure the major forms of vitamin A (retinol) and vitamin E (α-tocopherol and γ-tocopherol) in human blood plasma. Vitamins A and E were extracted with hexane and separated on a reversed-phase column using methanol as the mobile phase. Retinol was detected by ultraviolet absorption, whereas tocopherols were detected by fluorescence emission. The chromatographic cycle time was 4.0 min per sample. The analytical measurement range was 0.03-5.14, 0.32-36.02, and 0.10-9.99 mg/L for retinol, α-tocopherol, and γ-tocopherol, respectively. Intr-aassay and total coefficient of variation were <6.0% for all compounds. This method was traceable to standard reference materials offered by the National Institute of Standards and Technology. Reference intervals were established using plasma samples collected from 51 healthy adult donors and were found to be 0.30-1.20, 6.0-23.0, and 0.3-3.2 mg/L for retinol, α-tocopherol, and γ-tocopherol, respectively. In conclusion, we developed and validated a fast, simple, and sensitive high-performance liquid chromatography method for measuring the major forms of vitamins A and E in human plasma.

Applications of monolithic solid-phase extraction in chromatography-based clinical chemistry assays.

Complex matrices, for example urine, serum, plasma, and whole blood, which are common in clinical chemistry testing, contain many non-analyte compounds that can interfere with either detection or in-source ionization in chromatography-based assays. To overcome this problem, analytes are extracted by protein precipitation, solid-phase extraction (SPE), and liquid-liquid extraction. With correct chemistry and well controlled material SPE may furnish clean specimens with consistent performance. Traditionally, SPE has been performed with particle-based adsorbents, but monolithic SPE is attracting increasing interest of clinical laboratories. Monoliths, solid pieces of stationary phase, have bimodal structures consisting of macropores, which enable passage of solvent, and mesopores, in which analytes are separated. This structure results in low back-pressure with separation capabilities similar to those of particle-based adsorbents. Monoliths also enable increased sample throughput, reduced solvent use, varied support formats, and/or automation. However, many of these monoliths are not commercially available. In this review, application of monoliths to purification of samples from humans before chromatography-based assays will be critically reviewed.

Artificial Intelligence Applications in Clinical Chemistry.

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

A simple and fast liquid chromatography-tandem mass spectrometry method for measurement of underivatized L-arginine, symmetric dimethylarginine, and asymmetric dimethylarginine and establishment of the reference ranges.

Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor of nitric oxide synthase and an established biomarker for endothelial function, while symmetric dimethylarginine (SDMA), an emerging biomarker for renal function, has been shown to outperform creatinine-based equations for estimated glomerular filtration rate. In order to study these analytes for clinical research, a fast and simple method for measuring arginine (ARG), SDMA, and ADMA in plasma by liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been developed. Plasma (50 µL) was mixed with 50 µL of internal standard of (13)C-arginine and d(7)-ADMA followed by protein precipitation with methanol containing 1% ammonium acetate (300 µL). After centrifugation, the supernatant (100 µL) was mixed with 300 µL of acetonitrile with 1% formic acid, and the mixture was injected onto a silica column monitored by a mass spectrometer. The analytical cycle time was 5.0 min. The method was linear from 5.7 to 489.7 µM for ARG, 0.06 to 5.15 µM for SDMA, and from 0.34 to 5.65 µM for ADMA, with an accuracy of 99.0-120.0%. Total coefficients of variation for all analytes ranged from 2.7% to 7.7% for three concentration levels. The effects of hemolysis, lipemia, uremia, icterus, specimen tube types, storage at different temperature, and freeze/thaw were thoroughly investigated. Reference ranges were established using 51 well-defined reference subjects (12 men and 39 women, age 19-64 years): 53.1-129.7 µM for ARG, 0.32-0.65 µM for SDMA, and 0.36-0.67 µM for ADMA. In conclusion, the validated LC-MS/MS method described here offers a fast and reliable ARG, SDMA, and ADMA quantitation in plasma with minimum sample preparation.