Data Mining Approaches to Reference Interval Studies

Threshold of 25(OH)D and consequently adjusted parathyroid hormone reference intervals: data mining for relationship between vitamin D and parathyroid hormone

PURPOSE

By recruiting reference population, we aimed to (1): estimate the 25(OH)D threshold that maximally inhibits the PTH, which can be defined as the cutoff value for vitamin D sufficiency; (2) establish the PTH reference interval (RI) in population with sufficient vitamin D.

METHODS

Study data were retrieved from LIS (Laboratory Information Management System) under literature suggested criteria, and outliers were excluded using Tukey fence method. Locally weighted regression (LOESS) and segmented regression (SR) were conducted to estimate the threshold of 25(OH)D. Multivariate linear regression was performed to evaluate the associations between PTH concentration and variables including 25(OH)D, gender, age, estimated glomerular filtration rate (EGFR), body mass index (BMI), albumin-adjusted serum calcium (aCa), serum phosphate(P), serum magnesium(Mg), and blood collection season. Z test was adopted to evaluate whether the reference interval should be stratified by determinants such as age and gender.

RESULTS

A total of 64,979 apparently healthy subjects were recruited in this study, with median (Q1, Q3) 25(OH)D of 45.33 (36.15, 57.50) nmol/L and median (Q1, Q3) PTH of 42.19 (34.24, 52.20) ng/L. The segmented regression determined the 25(OH)D threshold of 55 nmol/L above which PTH would somewhat plateau and of 22 nmol/L below which PTH would rise steeply. Multivariate linear regression suggested that gender, EGFR, and BMI were independently associated with PTH concentrations. The PTH RI was calculated as 22.17-72.72 ng/L for subjects with 25(OH)D ≥ 55 nmol/L with no necessity of stratification according to gender, age, menopausal status nor season.

CONCLUSION

This study reported 25(OH)D thresholds of vitamin D sufficiency at 55 nmol/L and vitamin D deficiency at 22 nmol/L, and consequently established PTH RIs in subjects with sufficient vitamin D for northern China population for the first time.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

Utilization of five data mining algorithms combined with simplified preprocessing to establish reference intervals of thyroid-related hormones for non-elderly adults

BACKGROUND

Despite the extensive research on data mining algorithms, there is still a lack of a standard protocol to evaluate the performance of the existing algorithms. Therefore, the study aims to provide a novel procedure that combines data mining algorithms and simplified preprocessing to establish reference intervals (RIs), with the performance of five algorithms assessed objectively as well.

METHODS

Two data sets were derived from the population undergoing a physical examination. Hoffmann, Bhattacharya, Expectation Maximum (EM), kosmic, and refineR algorithms combined with two-step data preprocessing respectively were implemented in the Test data set to establish RIs for thyroid-related hormones. Algorithm-calculated RIs were compared with the standard RIs calculated from the Reference data set in which reference individuals were selected following strict inclusion and exclusion criteria. Objective assessment of the methods is implemented by the bias ratio (BR) matrix.

RESULTS

RIs of thyroid-related hormones are established. There is a high consistency between TSH RIs established by the EM algorithm and the standard TSH RIs (BR = 0.063), although EM algorithms seems to perform poor on other hormones. RIs calculated by Hoffmann, Bhattacharya, and refineR methods for free and total triiodo-thyronine, free and total thyroxine respectively are close and match the standard RIs.

CONCLUSION

An effective approach for objectively evaluating the performance of the algorithm based on the BR matrix is established. EM algorithm combined with simplified preprocessing can handle data with significant skewness, but its performance is limited in other scenarios. The other four algorithms perform well for data with Gaussian or near-Gaussian distribution. Using the appropriate algorithm based on the data distribution characteristics is recommended.

Indirect Method for Estimation of Reference Intervals of Inflammatory Markers

Background

The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII).

Methods

C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve.

Results

The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively.

Conclusions

The proposed RII method could provide additional information for RI verification or estimation with some limitations.

Reference intervals for clinical biochemistry and haematology tests during normal pregnancy

BACKGROUND

Pregnancy induces physiological changes which affect biochemical and haematological parameters. As the significance of laboratory test results change throughout pregnancy, the reference interval (RI) or key result interpretive guide should be specific to pregnancy. This study sought to establish trimester-specific-RIs for routine biochemical and haematological tests in healthy white European women with singleton pregnancies with comparison to RIs for non-pregnant European adults.

METHODS

A retrospective analysis of a prospective longitudinal single-centre study of healthy pregnant women conducted between November 2018 and December 2020 in a tertiary academic hospital with approximately 3000 births annually. Inclusion criteria: signed informed consent, age ≥18 years, white European, body mass index (BMI) <25 kg/m2, blood pressure <140/90mmHg, non-smoker, no previous pathology or gestational diabetes. Trimester defined as T1: up to 13 weeks + 6 days, T2: 14-27 weeks + 6 days and T3: ≥28-41 weeks + 6 days. Baseline demographics, anthropometric and laboratory measurements were recorded. In total, 31 biochemical and 10 haematological ISO15189:2012 accredited tests were measured using Roche Cobas® and Sysmex XN-9100™ analysers, respectively. RIs were established according to the International Federation of Clinical Chemistry (IFCC) recommended method.

RESULTS

Apparently healthy pregnant women (n = 124) with bio-banked serum samples in each trimester were recruited. At the booking visit, 49.2% (n = 61) of participants were nulliparous, with median age of 34.4 (IQR: 31.3-37.3) years, gestational age of 89 (IQR: 84-93) days, BMI of 22.5 (IQR: 21.0-23.7) kg/m² and systolic and diastolic blood pressure of 116 (110-125) mmHg and 67 (61-75) mmHg, respectively.

CONCLUSIONS

Normative trimester-specific biological intervals for routinely requested biochemical and haematological medical laboratory tests were established. These RIs will be invaluable to result interpretation and the management of pregnant women.

Reference intervals of urinary kidney injury biomarkers for middle-aged men and women determined by quantitative protein mass spectrometry

BACKGROUND AND AIMS

There is an ongoing need to recognize early kidney injury and its progression in structural chronic pathologies. The proteins NGAL, IGFBP7, TIMP2, KIM-1, CXCL9, TGF-β1, SLC22A2, nephrin, cubilin and uromodulin have been proposed as early kidney injury biomarkers. To guide clinical interpretation, their urinary concentrations should be accompanied by reference intervals, which we here establish in a representative Dutch middle-aged population.

MATERIALS AND METHODS

The 24-h urine samples from 1443 Caucasian middle-aged men and women, were analyzed for the biomarkers by quantitative LC-MS/MS. Biomarker excretion per 24-h were calculated, and urine creatinine and osmolality were measured for dilution normalization. This population was characterized by demographic and anthropometric parameters, comorbid conditions, and conventional kidney function measures.

RESULTS

NGAL, IGFBP7, TIMP2, KIM-1 and uromodulin could be quantified in this population, whereas nephrin, SLC22A2 and CXCL9 were below their detection limits. Urine creatinine and osmolality ( r= -were correlated to urine volume (r = -0.71; -0.74) and to IGFBP7 (r = 0.73; 0.71) and TIMP2 (r = 0.71; 0.69). Crude and normalized biomarker concentrations were affected by sex, but not by age, BMI, smoking, kidney function or common comorbid conditions. The reference intervals (men; women) were 18-108; 21-131 pmol IGFBP7/mmol creatinine, 1- 63; 4-224 pmol NGAL/mmol creatinine, 7-48; 7- 59 pmol TIMP2/mmol creatinine, <1-9; <1-12 pmol KIM-1/mmol creatinine and 0.1-1.2; 0.1-1.7 mg uromodulin/mmol creatinine.

CONCLUSION

We present dilution-normalized and sex-stratified urinary reference intervals of kidney injury biomarkers in a middle-aged Caucasian population.

Parametric and non-parametric estimation of reference intervals for routine laboratory tests: an analysis of health check-up data for 260 889 young men in the South Korean military

OBJECTIVES

Determination of reference intervals (RIs) using big data faces several obstacles due to heterogeneity in analysers, period and ethnicity. The present study aimed to establish the RIs for routine common blood count (CBC) and biochemistry laboratory tests in homogeneous, healthy, male Korean soldiers in their 20s using a large health check-up data set, comparing parametric and non-parametric estimation.

DESIGN

A multicentre, cross-sectional study.

SETTING

Seven armed forces hospitals in South Korea.

PARTICIPANTS

A total of 609 649 men underwent health examination when promoted to corporal between January 2015 and September 2021. 260 889 eligible individuals aged 20-25 were included in the analysis.

MAIN OUTCOMES AND MEASURES

The RIs were established by parametric and non-parametric methods. In the parametric approach, maximum likelihood estimation was applied to measure the Box-Cox transformation parameter and the values at the 2.5th and 97.5th percentiles were recalculated. The non-parametric approach adopted the Tukey's exclusion test and the values at the 2.5th and 97.5th percentiles were obtained. Classification by body mass index was also performed.

RESULTS

The obtained RIs for haematology parameters were comparable between devices. If the values followed a Gaussian distribution, parametric and non-parametric methods were well matched for haematology and biochemical markers. When the values were right-skewed, the upper limits were higher with parametric than with non-parametric methods. Participants with obesity showed higher RIs for CBC, some liver function tests and some lipid profiles than participants without obesity.

CONCLUSIONS

Using data from healthy, male Korean soldiers in their 20s, we proposed the RIs for CBC and biochemical parameters, comparing parametric and non-parametric estimation. As such approaches based on large data sets become more prevalent, further studies are needed to discriminate eligible individuals and determine RIs in an extrapolated sample.

Data mining of pediatric reference intervals

Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.