Partitioning reference values for several subpopulations using cluster analysis

Deciphering reference intervals and clinical decision limits in equine endocrine diagnostic testing

Reference intervals (RIs) and clinical decision limits (CDLs) are frequently established to facilitate interpretation of values of endocrine biomarkers in the diagnosis of disease. Despite their commonplace use in clinical decision-making, these concepts can be confused. Comparing a test result with a RI provides an estimation as to whether or not the individual is healthy, whereas comparison with a CDL facilitates identification of individuals with a particular disease state or at greater risk of adverse clinical outcomes. In practice, there will also be a range of results for which the discriminative ability of the test is insufficient to inform a specific diagnostic decision. Including a range of uncertain test results, or 'grey zone', between positive and negative avoids the constraint of a binary decision in classifying an individual with a test value above (or below) a single cut-off value as diseased. This review will detail the application of both RIs and CDLs, including defining the range of uncertain test results, in the context of equine endocrinology.

LMS-based continuous reference percentiles for 14 laboratory parameters in the CALIPER cohort of healthy children and adolescents

OBJECTIVES

Marked physiological changes in growth and development present challenges in defining pediatric reference intervals for biomarkers of health and disease. Lambda, Mu, and Sigma (LMS)-based statistical modeling provides a continuous normal distribution by negating skewness and variation, and is commonly used to establish growth charts. Such LMS reference curves are suggested to enhance laboratory test result interpretation. The current study establishes LMS-based continuous reference percentiles for 14 biomarkers in the CALIPER cohort of healthy children and adolescents.

METHODS

Data from healthy children and adolescents aged 1-<19 years were used to establish continuous reference percentiles using a novel LMS-based statistical method, including 2.5th, 25th, 50th, 75th, and 97.5th percentiles. The LMS approach applies a Box-Cox data transformation and summarizes continuous distributions by age via three curves: skewness (Lambda), median (Mu), and coefficient of variation (Sigma).

RESULTS

LMS-based percentiles and z-scores were generated for 14 common pediatric biomarkers that demonstrate dynamic concentration patterns with age (e.g., alkaline phosphatase) and/or wherein the magnitude of difference from the population mean may be clinically relevant (e.g., triglycerides). The LMS model captured age- and sex-specific distributions accurately and was not substantially influenced by outlying points.

CONCLUSIONS

This is the first study to establish LMS-based continuous reference percentiles for biochemical markers in a healthy Canadian pediatric population. The current LMS-based approach builds upon previous continuous reference interval models by providing graded percentiles to improve test result interpretation, particularly with repeated measures over time. This method may assist in facilitating a patient-centered approach to laboratory medicine.

Cluster analysis of risky behaviors among the youth in Western Iran: Determining correlates and comparing clusters based on severity of disability and attitude toward mental health help-seeking

BACKGROUND AND AIMS

The objectives of the study were (i) cluster analysis of risky behaviors; (ii) determining correlates; and (iii) comparing clusters with regard to the attitude toward mental health help seeking.

METHODS

The current cross-sectional study is a part of the first phase of the Persian Youth Cohort. From October 2014 to January 2017, 2990 individuals from Ravansar City in western Iran completed structured interviews and national and international standard questionnaires. The obtained data were analyzed using two-stage cluster analysis, multinomial logistic regression analysis, and Chi-square test.

RESULTS

This model provided three distinct clusters: (i) low-risk group with mild distress, (ii) high-risk group with high distress, and (iii) violent group with medium distress. Some sociodemographic and lifetime psychiatric disorders were the correlates of unhealthy clusters (P < 0.05). Compared to the reference cluster, a higher number of members in unhealthy clusters were suffering from medium to severe disability. Nevertheless, the participants in these clusters were less inclined to mental health help seeking.

CONCLUSIONS

More than half of the youth were suffering from suicidal and violent behaviors. Since high-risk participants are less inclined to mental health help seeking, the health policymakers can successfully utilize the results in planning general health programs.

Reference limits of plasma and serum creatinine concentrations from intra-laboratory data bases of several German and Italian medical centres: Comparison between direct and indirect procedures

BACKGROUND

The current dogma of establishing intra-laboratory reference limits (RLs) and their periodical reviewing cannot be fulfilled by most laboratories due to the expenses involved. Thus, most laboratories adopt external sources for their RLs often neglecting the problems of transferability. Therefore, several attempts were undertaken to derive RLs from the large data pools stored in modern laboratory information systems. These attempts were further developed to a more sophisticated indirect procedure. The new model can be considered a combined approach because it pre-excludes some subjects by direct criteria. In the current study, the new concept was applied to estimate RLs for serum and plasma creatinine from several German and Italian laboratories.

METHODS

A smoothed kernel density function was estimated for the distribution of the total mixed data of the sample group (combined data of non-diseased and diseased subjects). It was assumed that the "central" part of the distribution of all data represents the non-diseased ("healthy") population. The central part was defined by truncation points using an optimisation method, and was used to estimate a Gaussian distribution of the values of presumably non-diseased subjects after Box-Cox transformation of the empirical data. This distribution was now considered as the distribution of the non-diseased subgroup. The percentiles of this parametrical distribution were calculated to obtain RLs.

RESULTS

RLs determined by the indirect combined decomposition technique led to similar RLs as the classical direct method. Furthermore, the RLs obtained from 14 laboratories in 2 different European regions reflected the well-known differences of various analytical procedures. Stratification for gender and age was necessary. With rising age, an increase of the upper RL and of the reference range was observed. Hospitalization appeared also to affect the RLs. The new approach led to RLs in an artificially mixed population of diseased and non-diseased subjects (selected by clinical criteria) which were identical to RLs determined by a direct method applied to the non-diseased subgroup.

CONCLUSIONS

The proposed strategy of combining exclusion criteria with a resolution technique led to plausible retrospective RLs from intra-laboratory data pools for creatinine. Differences between laboratories were mainly due to the well-known bias of the different analytical procedures.

Whether western normative laboratory values used for clinical diagnosis are applicable to Indian population? An overview on reference interval

Reference Intervals denote normative values related to laboratory parameters/analytes used by diagnostic centers for clinical diagnosis. International guidelines recommend that every country must establish reference intervals for healthy individuals belonging to a group of homogeneous population. Considering enormous racial and ethnic diversity of Indian population, it is mandatory to establish reference intervals specific to Indian population. The overview on reference interval describes why the national organizations in India need to initiate nationwide efforts to establish its own laboratory standards for apparently healthy reference individuals belonging to our polygenetic, polyethnic, polyracial, multilinguistic and multicultural predominantly rural and appreciable urban Indian population with varied dietary habits.

Reference intervals: the way forward

New facts have recently enhanced interest in the topic of reference intervals. In particular, the International Organization for Standardization standard 15189, requesting that 'biological reference intervals shall be periodically reviewed', and the directive of the European Union on in vitro diagnostic medical devices asking manufacturers to provide detailed information on reference intervals, have renewed interest in the subject. This review presents an update on the topic, discussing the theoretical aspects and the most critical issues. The basic approach to the definition of reference intervals proposed in the original International Federation of Clinical Chemistry documents still remain valid. The use of data mining to obtain reference data from existing databases has severe limitations. New statistical approaches to discard outliers and to compute reference limits have been recommended. On the other hand, perspectives opened by the improvement in standardization through the implementation of the concept of traceability suggest new models to define 'common' reference intervals that can be transferred and adopted by different clinical laboratories in order to decrease the proliferation of different reference intervals not always justified by differences in population characteristics or in analytical methodology.