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Circulating biomarker use for the prediction and detection of pre-eclampsia

, 26 August 2020/in Featured Articles /by 3wmedia

Pre-eclampsia is a major cause of maternal and perinatal mortality. Scientific advances in recent decades have meant new possibilities for enhancing the prediction and diagnosis of pre-eclampsia. Here we detail current biomarker-based approaches under development or validated for clinical translation that will revolutionize obstetric practice in the years ahead.

by Dr Kirsten Palmer¹, Associate Professor Fabricio da Silva Costa¹

Pre-eclampsia
Pre-eclampsia affects 3–8% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality. Globally, pre-eclampsia is responsible for over 60 000 maternal deaths and greater than 500 000 neonatal deaths every year. It is a heterogeneous condition, likely having multiple underlying etiologies, producing a clinical syndrome typically characterized by maternal hypertension and multi-organ dysfunction, including fetoplacental, renal, hepatic, hematological and/or neurological dysfunction. Currently, the mainstay of treatment is delivery, with delivery of the placenta curing the condition. Although this is a reasonable option when the disease presents at term (37–42 weeks gestation), when pre-eclampsia arises prematurely delivery places the neonate at the significant risks of prematurity. As such, current medical approaches for preterm pre-eclampsia, especially if occurring <34 weeks gestation, are centred around close observation of both maternal and fetal well-being, aiming to prolong gestation towards term, but timing delivery to minimize risks for mother and child due to evolving pre-eclampsia. On average this approach will only see a gestational advancement of 7–14 days [1]. Therefore, these women often require care in highly specialized obstetric units with access to a neonatal intensive care unit. The ability to predict those women most at risk of developing pre-eclampsia, may afford the opportunity to commence preventative therapies, such as aspirin, but may also enable better allocation of healthcare models.

Pre-eclampsia had long been considered to solely be of concern during the time of pregnancy; however, it is now appreciated that it is also associated with negative long-term health implications for the mother and child. Women who had pre-eclampsia are at increased risk of cardiovascular disease and death in the decades that follow [2]. Similarly, children born from pregnancies affected by pre-eclampsia are also at greater risk of hypertension later in life [3]. As such, it is becoming increasingly important to accurately diagnose pre-eclampsia and provide ongoing long-term healthcare following birth to minimize the morbidity.

The pathophysiology of pre-eclampsia
Multiple phenotypes of pre-eclampsia exist. Currently, these are understood to be early-onset and late-onset forms of pre-eclampsia. Underlying abnormal placentation is a key component of early-onset disease, where the shallowly implanted placenta leads to ischemia-reperfusion injuries within the placenta. This impacts on placental gene expression leading to an upregulation of hypoxia-regulated genes including anti-angiogenic proteins, such as soluble fms-like tyrosine kinase 1 (sFLT-1) and soluble endoglin. sFLT-1, a soluble version of the vascular endothelial growth factor receptor 1, is able to bind and antagonize the actions of the angiogenic proteins, vascular endothelial growth factor (VEGF) and placental growth factor (PlGF). These proteins are essential in maintaining endothelial homeostasis and a reduction in their bioavailability produces the widespread end-organ endothelial dysfunction that yields the clinical pre-eclamptic phenotype.

In comparison, late-onset pre-eclampsia is thought to result from pre-existing endothelial dysfunction, such as exists in women with chronic hypertension, obesity and diabetes. In normal pregnancy, sFLT-1 within the maternal circulation gradually rises across gestation. In women with pre-existing endothelial dysfunction, this normal rise in sFLT-1 will lead to a worsening of endothelial function, which may tip the balance towards the clinical development of term pre-eclampsia.

With the discovery of sFLT-1 in 2003 [4], pre-eclampsia research has been firmly focused both on better prediction and diagnosis of the disease, but also the development of therapeutics. The use of anti-angiogenic and angiogenic biomarkers for pre-eclampsia is now entering into clinical use or in the process of translation to improve prediction and diagnosis across pregnancy.

Predicting pre-eclampsia during the first trimester
First-trimester screening for fetal aneuploidy is the most commonly employed test in early gestation for the prediction of later pregnancy complications, namely, delivery of an infant with a chromosomal anomaly. The principles underpinning these multiparametric tests have informed the development of screening strategies using multiple biomarkers in early gestation for the prediction of other complications, such as pre-eclampsia [5].

In 2009, researchers from the Fetal Medicine Foundation (FMF; UK) evaluated a multiparametric test incorporating maternal factors, mean arterial pressure, uterine artery Doppler pulsatility index, circulating PlGF, and PAPP-A. It detected 93% of early-onset pre-eclampsia with a false positive rate (FPR) of 5% [6]. This approach has subsequently been externally validated producing similar rates of detection for early-onset pre-eclampsia (80.8–91.7%), but with a 10% FPR [7, 8].

Very recently, a prospective multicentre study of first-trimester screening for pre-eclampsia in singleton pregnancies was published [9]. The study population had 239 (2.7%) cases that developed pre-eclampsia, including 17 (0.2%), 59 (0.7%) and 180 (2.0%) at <32, <37 and >37 weeks, respectively. Using combined screening by maternal factors, mean arterial pressure, uterine artery pulsatility index and serum PlGF the detection rate was 100% (95% CI 80–100) for pre-eclampsia at <32 weeks, 75% (95% CI 62–85) at <37 weeks and 43% (95% CI 35–50) at >37 weeks, with a 10% FPR. As such, The FMF model is the most accurate and thoroughly validated algorithm available at 11–13 weeks to predict pre-eclampsia in a low-risk population.

A clear benefit to predicting women at high risk of pre-eclampsia in the first trimester is the opportunity it affords to institute preventative therapies. Currently, low dose aspirin is the only medication that appears to reduce the rate of pre-eclampsia in high-risk women, with the results of the ASPRE study (Aspirin for evidence-based PRE-eclampsia prevention) [10], a European multicentre randomized controlled trial, eagerly awaited to define how effective preventative aspirin truly is. This trial applied the FMF screening model in 30 000 women at 11–13 weeks gestation, with those at increased risk of pre-eclampsia randomly assigned to aspirin (n=798) or placebo (n=822). However, the FMF algorithm cannot detect all cases of preterm pre-eclampsia and detection of term disease was poor, furthermore, aspirin cannot prevent all cases. Thus, further studies with novel biomarkers to improve screening (especially for term pre-eclampsia) and new treatments are needed to reduce the global burden of this disease.

Predicting pre-eclampsia later in pregnancy
Circulating anti-angiogenic factor levels are often below the detection limit of available assays during the first trimester; however, both sFLT-1 and soluble endoglin levels rise as gestation advances and are significantly raised in the maternal circulation, while PlGF levels are significantly reduced, weeks before the clinical development of pre-eclampsia [11, 12]. These findings have prompted widespread study into the use of these biomarkers in predictive algorithms for pre-eclampsia. PlGF appears the most useful biomarker in first-trimester screening, as well as in the second trimester either on its own or in a ratio with sFLT-1. Automated systems able to rapidly process these biomarkers are already available and the utility of PlGF and sFLT-1 has now been assessed in multiple trials. PlGF appears the most accurate biomarker, capable of detecting approximately 75% of women who will develop early-onset pre-eclampsia [13], whereas the sFLT-1:PlGF ratio appears to perform well as a negative predictor of early-onset pre-eclampsia [14].

Recently, an assay able to detect a placental-specific variant of sFLT-1, known as sFLT-1 e15a, has been developed [15]. This may provide improved positive predictive performance in predicting who will develop pre-eclampsia. As anticipated, total sFLT-1 and sFLT-1 e15a, are most useful in predicting early-onset disease [16], in which abnormal placental pathology is central to disease development. However, measurement in the third trimester and assessing the longitudinal change in expression may be useful for the prediction of late-onset pre-eclampsia [17]. Certainly, total sFLT-1, as well as PlGF and maternal factors currently appear the most promising for predicting term disease; however, none have been validated or perform favourably enough for translation to clinical
practice [18].

Improving the diagnosis of pre-eclampsia
The use of angiogenic and anti-angiogenic biomarkers may provide significant improvement for the accurate diagnosis of pre-eclampsia itself. Current markers used to diagnose pre-eclampsia consist of a history of pre-eclamptic symptoms (such as headache, visual disturbance or epigastric pain), physical signs (such as high blood pressure, hyper-reflexia or tender liver edge) and biochemical markers (such as proteinuria; elevated liver transaminases, uric acid or creatinine; or thrombocytopenia). However, these can also be present in other pre-existing medical conditions, such as renal disease, which can make the accurate diagnosis of pre-eclampsia a challenge.

PlGF currently appears the most promising biomarker for improving the accurate diagnosis of pre-eclampsia, even in the setting of pre-existing renal disease or chronic hypertension [19]. PlGF outperforms our current diagnostic approach for pre-eclampsia, particularly for disease occurring <35 weeks gestation. However, the challenge remains to find an accurate diagnostic test to identify late-onset pre-eclampsia and improve test performance to minimize the FPR.

Conclusion
The last 15 years has seen a rapid increase in our knowledge of the pre-eclamptic process enabling development of promising new approaches to disease prediction and diagnosis. However, the pathway to translation of these tests into widespread clinical use has been slowed by the heterogeneity of pre-eclampsia itself, as it is likely that no one test or approach will work for all forms of pre-eclampsia. New approaches and ongoing progression in our understanding of the disease process provide hope that our clinical approach to pre-eclampsia will change significantly in the years ahead, hopefully to the betterment of the women and infants we care for.

References
1. Magee LA, Yong PJ, Espinosa V, Cote AM, Chen I, von Dadelszen P. Expectant management of severe preeclampsia remote from term: a structured systematic review. Hypertens Pregnancy 2009; 28(3): 312–347.
2. Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ 2007; 335(7627): 974.
3. Pinheiro TV, Brunetto S, Ramos JG, Bernardi JR, Goldani MZ. Hypertensive disorders during pregnancy and health outcomes in the offspring: a systematic review. J Dev Orig Health Dis 2016; 7(4): 391–407.
4. Maynard S, Min J, Merchan J, Lim K, Li J, Mondal S, Libermann T, Morgan J, Sellke F, et al. Excess placental soluble fms-like tyrosine kinase 1 (sFLT-1) may contribute to endothelial dysfunction, hypertension, and proteinuria in pre-eclampsia. J Clin Invest 2003; 111(5): 649–658.
5. Cuckle HS. Screening for pre-eclampsia–lessons from aneuploidy screening. Placenta 2011; 32 Suppl: S42–48.
6. Poon LC, Kametas NA, Maiz N, Akolekar R, Nicolaides KH. First-trimester prediction of hypertensive disorders in pregnancy. Hypertension 2009; 53(5): 812–818.
7. Park FJ, Leung CH, Poon LC, Williams PF, Rothwell SJ, Hyett JA. Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy. Aust N Z J Obstet Gynaecol 2013; 53(6): 532–539.
8. Scazzocchio E, Figueras F, Crispi F, Meler E, Masoller N, Mula R, Gratacos E. Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting. Am J Obstet Gynecol 2013; 208(3): 203.e1-203.e10.
9. O’Gorman N, Wright D, Poon LC, Rolnik DL, Syngelaki A, Wright A, Akolekar R, Cicero S, Janga D, et al. Accuracy of competing risks model in screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks’ gestation. Ultrasound Obstet Gynecol 2017; doi: 10.1002/uog.17399.
10. O’Gorman N, Wright D, Rolnik DL, Nicolaides KH, Poon LC. Study protocol for the randomised controlled trial: combined multimarker screening and randomised patient treatment with ASpirin for evidence-based PREeclampsia prevention (ASPRE). BMJ open 2016; 6(6): e011801.
11. Levine RJ, Maynard SE, Qian C, Lim K-H, England LJ, Yu KF, Schisterman EF, Thadhani R, Sachs BP, et al. Circulating angiogenic factors and the risk of preeclampsia. N Engl J Med 2004; 350(7): 672–683.
12. Levine RJ, Qian C, Maynard SE, Yu KF, Epstein FH, Karumanchi SA. Serum sFlt1 concentration during preeclampsia and mid trimester blood pressure in healthy nulliparous women. Am J Obstet Gynecol 2006; 194(4): 1034–1041.
13. Andersen LB, Dechend R, Jorgensen JS, Luef BM, Nielsen J, Barington T, Christesen HT. Prediction of preeclampsia with angiogenic biomarkers. Results from the prospective Odense Child Cohort. Hypertens Pregnancy 2016; 35(3): 405–419.
14. Zeisler H, Llurba E, Chantraine F, Vatish M, Staff AC, Sennstrom M, Olovsson M, Brennecke SP, Stepan H, et al. Predictive value of the sFlt-1: PlGF ratio in women with suspected preeclampsia. N Engl J Med 2016; 374(1): 13–22.
15. Palmer KR, Kaitu’u-Lino TJ, Hastie R, Hannan NJ, Ye L, Binder N, Cannon P, Tuohey L, Johns TG, et al. Placental-specific sFLT-1 e15a protein is increased in preeclampsia, antagonizes vascular endothelial growth factor signaling, and has antiangiogenic activity. Hypertension 2015; 66(6): 1251–1259.
16. Palmer KR, Kaitu’u-Lino TJ, Cannon P, Tuohey L, De Silva MS, Varas-Godoy M, Acuna S, Galaz J, Tong S, Illanes SE. Maternal plasma concentrations of the placental specific sFLT-1 variant, sFLT-1 e15a, in fetal growth restriction and preeclampsia. J Matern Fetal Neonatal Med 2017; 30(6): 635–639.
17. Khalil A, Maiz N, Garcia-Mandujano R, Penco JM, Nicolaides KH. Longitudinal changes in maternal serum placental growth factor and soluble fms-like tyrosine kinase-1 in women at increased risk of pre-eclampsia. Ultrasound Obstet Gynecol 2016; 47(3): 324–331.
18. Andrietti S, Silva M, Wright A, Wright D, Nicolaides KH. Competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 35-37 weeks’ gestation. Ultrasound Obstet Gynecol 2016; 48(1): 72–79.
19. Chappell LC, Duckworth S, Seed PT, Griffin M, Myers J, Mackillop L, Simpson N, Waugh J, Anumba D, et al. Diagnostic accuracy of placental growth factor in women with suspected preeclampsia: a prospective multicenter study. Circulation 2013; 128(19): 2121–2131.

The authors
Kirsten Palmer¹ MBBS, PhD; Fabricio da Silva Costa¹ MD, PhD
¹Department of Obstetrics and Gynaecology, Monash University,
Monash Medical Centre, Clayton 3168,
Victoria, Australia

*Corresponding author
E-mail: kirsten.palmer@monash.edu

Improving diagnostic accuracy and laboratory test result interpretation in children and adolescents

, 26 August 2020/in Featured Articles /by 3wmedia

Appropriate reference intervals are critical for interpretation of laboratory test results and accurate assessment of health and disease. However, pediatric reference intervals are severely lacking, leading to significant risk of misdiagnosis. CALIPER has addressed these gaps by establishing a robust reference interval database based on thousands of healthy children and adolescents.

by Victoria Higgins and Dr Khosrow Adeli

Introduction
The clinical laboratory provides objective data through laboratory testing of bodily fluids (e.g. serum, plasma) to aid in several aspects of medical decision making, including identifying risk factors and symptoms, diagnosing disease, and monitoring treatment. To correctly interpret laboratory test results, they are often compared to reference intervals (RIs), sometimes referred to as ‘normative’ or ‘expected’ values. RIs are commonly defined as the central 95% of the distribution of laboratory test results expected in a healthy, reference population [1]. Laboratory values that fall outside the appropriate RI may be interpreted as abnormal, possibly indicating the need for additional medical follow-up and/or treatment [2]. Given their critical importance to healthcare it would be expected that accurate RIs, appropriate for the patient population, are used in clinical practice. However, this is unfortunately far from the truth.

Importance of pediatric reference intervals
It can be challenging and costly for individual laboratories to develop RIs for their specific patient population, due to the necessity of recruiting a sufficiently large number of healthy individuals [i.e. The Clinical Laboratory Standards Institute (CLSI) recommends 120 individuals per partition] [1]. This is particularly true for pediatrics, a population in which unique RIs are of high importance. To interpret pediatric test results, laboratories often use RIs that were established on an adult reference population. The use of adult RIs to interpret pediatric test results can lead to erroneous and inaccurate interpretation. This is highlighted in Figure 1, which depicts the concentration of alkaline phosphatase (ALP) throughout pediatric, adult and geriatric age. It is evident that the pediatric population has vastly unique normative ALP values. Unique analyte concentrations in pediatrics is also true for sex hormones, growth hormones and several other analytes [3–5]. Therefore, children should not be viewed as small adults in the context of medical practice, but require separate RIs (i.e. partitions) for different age and/or sex groups, in addition to neonates and premature babies [5].

Closing the gaps in pediatric reference intervals
The current CLSI guidelines, which are mostly focused on adult RIs, acknowledge the special challenges of establishing age- and sex-specific pediatric RIs and recommend development of new initiatives to address the current gaps. The quality of a RI critically depends on the selected reference population. Therefore, the direct method of establishing RIs, which involves recruiting healthy individuals and applying exclusion criteria to select an appropriate reference population, is recommended over the indirect method, which involves using an already existing database (e.g. laboratory information system) to calculate RIs [1]. It is imperative for RI initiatives to focus on recruiting a sufficiently large and healthy reference population to accurately establish appropriate RIs for the pediatric population (i.e. using the direct method). Recognizing the critical need to establish pediatric RIs, several national initiatives have collected health information and blood samples from healthy pediatric populations. These initiatives include KiGGS in Germany [6], the Lifestyle of Our Kids (LOOK) study in Australia [7], CHILDx in the United States [8–10], The COPENHAGEN Puberty Study in the Nordic countries [11], and The Canadian Laboratory Initiative on Pediatric RIs (CALIPER) in Canada [5, 12].

The KiGGS initiative collected comprehensive, nationwide data on the health status of over 17 000 children and adolescents aged 0 to 17 years, across 167 locations in Germany [6]. This study has focused on laboratory parameters of general health indices, markers of nutritional status, immunization status, iron metabolism, thyroid, and indices of atopic sensitization. They have published age-dependent percentiles (3rd to 97th) in German, which may serve as a basis for RIs [13]. The LOOK study in Australia developed age-specific RIs for 37 chemistries, immunoassays, and derived parameters [7]. The CHILDx study was initiated in 2002 at ARUP (Associated Regional and University Pathologists) Laboratories and established RIs for 35 markers for children aged 6 months to 6 years and 58 markers for children aged 7–17 years [8–10]. The Nordic countries have also successfully established pediatric RIs for 21 biochemical properties using samples from healthy children and adolescents aged 5–19 years collected from schools from 2006–2008 in the Copenhagen area in Denmark as part of The COPENHAGEN Puberty Study [11]. However, arguably the most successful initiative has been the CALIPER project in Canada.

CALIPER project
The CALIPER project was initiated by The Paediatric Focus Group of the Canadian Society of Clinical Chemists (CSCC) and primarily based at The Hospital for Sick Children in Toronto (ON, Canada). CALIPER has recruited over 9 000 healthy children and adolescents from schools and community centres to participate at blood collection clinics by completing a health questionnaire, body measurements and donating a blood sample. Using this biobank of healthy pediatric samples, CALIPER has established age-, sex- and, for some biomarkers, Tanner Stage-specific pediatric RIs for over 100 biomarkers including, common biochemical markers, protein markers, lipids and enzymes [12], specialty endocrine markers [14], fertility hormones [15], cancer biomarkers [16], vitamins [17], metabolic disease biomarkers [18], testosterone indices [19] and specialized biochemical markers [20, 21]. All RIs were established in accordance with CLSI guidelines, including sample size requirements, outlier removal, statistical method for partitioning, as well as RI and confidence interval calculations [1].

The majority of RIs were established using Abbott ARCHITECT assays, initially limiting the direct applicability of the CALIPER database to all Canadian laboratories. CALIPER subsequently performed a series of transference and verification studies to expand the CALIPER database to additional assays commonly used in clinical laboratories, including Beckman, Ortho, Roche and Siemens [22–25]. Again, CALIPER performed these studies in accordance with CLSI guidelines and, in fact, often exceeded the sample size and statistical criteria requirements [1, 26]. The comprehensive CALIPER pediatric RI database is available online (www.caliperproject.ca), as well as through a mobile application (CALIPERApp) available on iTunes and Google Play. These tools allow the CALIPER database to be easily accessible to laboratory professionals, physicians, parents and patients.

Continued improvement in pediatric laboratory test interpretation
While significant improvements have been made in pediatric laboratory test interpretation over the past decade, several gaps remain. First, RI data for neonates (including premature babies) and infants (age 0 to <1 year) remains a challenge, owing to difficulties accessing a healthy neonate and infant population. However, the limited neonatal and infantile reference data CALIPER has collected highlights the profound differences in the newborn period, necessitating accurate RIs for this age group. For example, Figure 2 shows the dynamic concentration of creatinine throughout the pediatric age range, particularly the elevated and highly variable levels in the first two weeks of life. A large-scale, comprehensive study aimed at recruiting healthy neonates and infants is required to fill this gap. CALIPER is currently initiating a study with the aim of establishing a complete RI database for neonates and infants, which will greatly improve neonatal healthcare for premature babies, newborns, and infants from primary to complex, tertiary care pediatric centres.

Secondly, the effect of ethnicity on biomarker concentration remains to be comprehensively examined. The International Federation of Clinical Chemistry (IFCC) recommends that every country establishes RIs [27]; however, most nations adopt RIs from studies predominately performed in Western countries based on primarily Caucasian populations without considerations of ethnic differences. Although the majority of biomarkers do not differ between individuals of different ethnic backgrounds, a preliminary examination of the influence of ethnicity in pediatrics by CALIPER has shown that some biomarkers do significantly differ among ethnic groups, including immunoglobulin G (IgG), transferrin, ferritin, and follicle-stimulating hormone (FSH) [12, 14, 15]. Another study examined the influence of ethnicity in adults and found that serum creatine kinase (CK) activity is significantly higher for those of African ancestry. As elevated CK activity is an indicator of statin-induced myopathy, elevated CK activity in those of African ancestry could result in inappropriate discontinuation of statin therapy if ethnic-specific RIs are not used [28]. Another recent study used data from the National Health and Nutrition Examination Survey (NHANES) to develop racial/ethnic-specific RIs among Asians, Blacks, Hispanics, and Whites [29]. CALIPER has initiated a new study to robustly determine the effect of ethnicity on the concentration of routine serum biomarkers by examining and comparing reference values in the four major Canadian ethnic groups (i.e. Caucasian, South Asian, East Asian, and Black).

Lastly, as clinical laboratories adopt their RIs from numerous different sources, including textbooks, manufacturer product inserts, expert opinions, or published literature, RIs in clinical practice may vary substantially between laboratories. A national survey performed in Australia by the Australian Association of Clinical Biochemists (AACB) Harmonisation Group highlights the extensive variation in adult RIs used in clinical practice, which greatly compromises the consistency and reliability of laboratory test result interpretation and patient care [30]. A recent Canadian RI study (manuscript submitted; Adeli K, et al. 2017) by the CSCC Harmonized RI (hRI) Working Group, further highlights the considerable variation in RIs across laboratories with a greater variation observed in pediatric RIs in current clinical use, even between clinical laboratories using the same instrument. These surveys highlight the critical need for harmonized RIs in clinical practice. Initiatives in the Nordic countries [31], UK [32], Australia [33] and Japan [34] have already established harmonized RIs for a number of laboratory tests primarily for adults, but also for pediatrics. The CSCC hRI Working Group is now also working towards Canada-wide RI harmonization.

Conclusion
Children cannot be viewed as small adults and indeed require pediatric-specific RIs appropriately partitioned by age and sex for accurate laboratory test result interpretation. Several national initiatives have begun to address these critical gaps over the past decade by establishing age-, sex- and Tanner Stage-specific RIs for several major analytical platforms. The CALIPER initiative in Canada has arguably been the most comprehensive study to date, with clinical laboratories in several countries globally implementing the CALIPER database into clinical practice. Despite the significant strides recently achieved, further research is warranted in several areas including the establishment of RIs specific to the neonatal and infantile period, ethnic-specific RI for a subset of laboratory markers, and RI harmonization. Collectively, the comprehensive reference database published by CALIPER and the emerging data from ongoing studies directly address the evidence gap in pediatric RIs and contribute to evidence-based interpretation of laboratory test results and enhanced diagnostic accuracy of laboratory biomarkers in current clinical practice.

References
1. Defining, establishing, and verifying RIs in the clinical laboratory; approved guidelines – third edition CLSI document C28-A3. Clinical and Laboratory Standards Institute (CLSI); 2008.
2. Jung B, Adeli K. Clinical laboratory RIs in pediatrics: the CALIPER initiative. Clin Biochem 2009; 42(16–17): 1589–1595.
3. Adeli K, Higgins V, Nieuwesteeg M, Raizman JE, Chen Y, Wong SL, Blais D. Biochemical marker reference values across pediatric, adult, and geriatric ages: establishment of robust pediatric and adult RIs on the basis of the Canadian Health Measures Survey. Clin Chem 2015; 61(8): 1049–1062.
4. Adeli K, Higgins V, Nieuwesteeg M, Raizman JE, Chen Y, Wong SL, Blais D. Complex reference values for endocrine and special chemistry biomarkers across pediatric, adult, and geriatric ages: establishment of robust pediatric and adult RIs on the basis of the Canadian Health Measures Survey. Clin Chem 2015; 61(8): 1063–1074.
5. Shaw JLV, Binesh Marvasti T, Colantonio D, Adeli K. Pediatric RIs: challenges and recent initiatives. Crit Rev Clin Lab Sci 2013; 50(2): 37–50.
6. Kohse KP. KiGGS – the German survey on children’s health as data base for RIs and beyond. Clin Biochem 2014; 47(9): 742–743.
7. Southcott EK, Kerrigan JL, Potter JM, Telford RD, Waring P, Reynolds GJ, Lafferty ARA, Hickman PE. Establishment of pediatric RIs on a large cohort of healthy children. Clin Chim Acta 2010; 411(19–20): 1421–1427.
8. Flanders MM, Crist RA, Roberts WL, Rodgers GM. Pediatric RIs for seven common coagulation assays. Clin Chem 2005; 51(9): 1738–1742.
9. Clifford SM, Bunker AM, Jacobsen JR, Roberts WL. Age and gender specific pediatric RIs for aldolase, amylase, ceruloplasmin, creatine kinase, pancreatic amylase, prealbumin, and uric acid. Clin Chim Acta 2011; 412(9–10): 788–790.
10. Johnson-Davis KL, Moore SJ, Owen WE, Cutler JM, Frank EL. A rapid HPLC method used to establish pediatric RIs for vitamins A and E. Clin Chim Acta 2009; 405(1–2): 35–38.
11. Hilsted L, Rustad P, Aksglæde L, Sørensen K, Juul A. Recommended Nordic paediatric RIs for 21 common biochemical properties. Scand J Clin Lab Invest 2013; 73(1): 1–9.
12. Colantonio DA, Kyriakopoulou L, Chan MK, Daly CH, Brinc D, Venner AA, Pasic MD, Armbruster D, Adeli K. Closing the gaps in pediatric laboratory RIs: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clin Chem 2012; 58(5): 854–868.
13. Dortschy R, Schaffarth Rosario A, Scheidt-Nave C, Thierfelder W, Thamm M, Gutsche J. Bevölkerungsbezogene Verteilungswerte ausgewählter Laborparameter aus der Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland (KiGGS). Beiträge zur Gesundheitsberichterstattung des Bundes. Berlin: Robert Koch-Institut; 2009.
14. Bailey D, Colantonio D, Kyriakopoulou L, Cohen AH, Chan MK, Armbruster D, Adeli K. Marked biological variance in endocrine and biochemical markers in childhood: establishment of pediatric RIs using healthy community children from the CALIPER cohort. Clin Chem 2013; 59(9): 1393–1405.
15. Konforte D, Shea JL, Kyriakopoulou L, Colantonio D, Cohen AH, Shaw J, Bailey D, Chan MK, Armbruster D, Adeli K. Complex biological pattern of fertility hormones in children and adolescents: a study of healthy children from the CALIPER cohort and establishment of pediatric RIs. Clin Chem 2013; 59(8): 1215–1227.
16. Bevilacqua V, Chan MK, Chen Y, Armbruster D, Schodin B, Adeli K. Pediatric population reference value distributions for cancer biomarkers and covariate-stratified RIs in the CALIPER cohort. Clin Chem 2014; 60(12): 1532–1542.
17. Raizman JE, Cohen AH, Teodoro-Morrison T, Wan B, Khun-Chen M, Wilkenson C, Bevilaqua V, Adeli K. Pediatric reference value distributions for vitamins A and E in the CALIPER cohort and establishment of age-stratified RIs. Clin Biochem 2014; 47(9): 812–815.
18. Teodoro-Morrison T, Kyriakopoulou L, Chen YK, Raizman JE, Bevilacqua V, Chan MK, Wan B, Yazdanpanah M, Schulze A, Adeli K. Dynamic biological changes in metabolic disease biomarkers in childhood and adolescence: a CALIPER study of healthy community children. Clin Biochem 2015; 48(13–14): 828–836.
19. Raizman JE, Quinn F, Armbruster DA, Adeli K. Pediatric RIs for calculated free testosterone, bioavailable testosterone and free androgen index in the CALIPER cohort. Clin Chem Lab Med 2015; 53(10): e239–243.
20. Kelly J, Raizman JE, Bevilacqua V, Chan MK, Chen Y, Quinn F, Shodin B, Armbruster D, Adeli K. Complex reference value distributions and partitioned RIs across the pediatric age range for 14 specialized biochemical markers in the CALIPER cohort of healthy community children and adolescents. Clin Chim Acta 2015; 450: 196–202.
21. Karbasy K, Lin DCC, Stoianov A, Chan MK, Bevilacqua V, Chen Y, Adeli K. Pediatric reference value distributions and covariate-stratified RIs for 29 endocrine and special chemistry biomarkers on the Beckman Coulter Immunoassay Systems: a CALIPER study of healthy community children. Clin Chem Lab Med 2016; 54(4): 643–657.
22. Estey MP, Cohen AH, Colantonio DA, Chan MK, Marvasti TB, Randell E, Delvin E, Cousineau J, Grey V, et al. CLSI-based transference of the CALIPER database of pediatric RIs from Abbott to Beckman, Ortho, Roche and Siemens Clinical Chemistry Assays: direct validation using reference samples from the CALIPER cohort. Clin Biochem 2013; 46(13–14): 1197–1219.
23. Higgins V, Chan MK, Nieuwesteeg M, Hoffman BR, Bromberg IL, Gornall D, Randell E, Adeli K. Transference of CALIPER pediatric RIs to biochemical assays on the Roche cobas 6000 and the Roche Modular P. Clin Biochem 2016; 49(1–2): 139–149.
24. Araújo PAT, Thomas D, Sadeghieh T, Bevilacqua V, Chan MK, Chen Y, Randell E, Adeli K. CLSI-based transference of the CALIPER database of pediatric RIs to Beckman Coulter DxC biochemical assays. Clin Biochem 2015; 48(13–14): 870–880.
25. Abou El Hassan M, Stoianov A, Araújo PAT, Sadeghieh T, Chan MK, Chen Y, Randell E, Nieuwesteeg M, Adeli K. CLSI-based transference of CALIPER pediatric RIs to Beckman Coulter AU biochemical assays. Clin Biochem 2015; 48(16–17): 1151–1159.
26. Method comparison and bias estimation using patient samples; approved guidelines – second edition CLSI document EP9-A2. Clinical and Laboratory Standards Institute (CLSI) 2002.
27. Solberg HE, Stamm D. IFCC recommendation: The theory of reference values. Part 4. Control of analytical variation in the production, transfer and application of reference values. J Autom Chem 1991; 13(5): 231–234.
28. Brewster LM, Mairuhu G, Sturk A, van Montfrans GA. Distribution of creatine kinase in the general population: implications for statin therapy. Am Heart J 2007; 154(4): 655–661.
29. Lim E, Miyamura J, Chen JJ. Racial/ethnic-specific RIs for common laboratory tests: a comparison among Asians, Blacks, Hispanics, and White. Hawaii J Med Public Health 2015; 74(9): 302–310.
30. Jones GR, Koetsier SD. RCPAQAP first combined measurement and RI survey. Clin Biochem Rev 2014; 35(4): 243–250.
31. Rustad P, Felding P, Franzson L, Kairisto V, Lahti A, Mårtensson A, et al. The Nordic Reference Interval Project 2000: recommended RIs for 25 common biochemical properties. Scand J Clin Lab Invest 2004; 64(4): 271–284.
32. Berg J, Lane V. Pathology harmony; a pragmatic and scientific approach to unfounded variation in the clinical laboratory. Ann Clin Biochem 2011; 48(3): 195–197.
33. Tate JR, Sikaris KA, Jones GR, Yen T, Koerbin G, Ryan J, Reed M, Gill J, Koumantakis G, et al. Harmonising adult and paediatric RIs in Australia and New Zealand: an evidence-based approach for establishing a first panel of chemistry analytes. Clin Biochem Rev 2014; 35(4): 213–235.
34. Yamamoto Y, Hosogaya S, Osawa S, Ichihara K, Onuma T, Saito A, Banba K, Araki H, Nagamine Y, et al. Nationwide multicenter study aimed at the establishment of common RIs for standardized clinical laboratory tests in Japan. Clin Chem Lab Med 2013; 51(8): 1663–1672.

The authors
Victoria Higgins PhD candidate; Khosrow Adeli* PhD, FCACB, DABCC, FACB
CALIPER program, Pediatric Laboratory Medicine, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada

*Corresponding author
E-mail: khosrow.adeli@sickkids.ca

New screening strategies in prenatal care by the introduction of non-invasive prenatal testing for fetal aneuploidies

, 26 August 2020/in Featured Articles /by 3wmedia

The identification of cell-free fetal DNA (cffDNA) in maternal plasma has led to the development of non-invasive prenatal testing (NIPT) for fetal aneuploidies risk assessment. The high accuracy of NIPT has profoundly influenced the field of prenatal care.

by F. Gerundino, Dr C. Giachini, C. Giuliani, E. Contini and Dr E. Pelo

Background
Several well-established screening approaches to estimate the personal risk for common autosomal aneuploidies such as trisomy 21 (T21, Down syndrome), trisomy 18 (T18, Edwards syndrome) and trisomy 13 (T13, Patau syndrome) are part of the standard prenatal care in many countries. These approaches are based on the combination of different parameters such as maternal age, markers in maternal serum and ultrasound findings in the first or second trimester of pregnancy. Overall, conventional screening tests show a detection rate (DR) of 80–95% with a high false positive rate (FPR) (3–5%). Pregnancies identified to be at high risk (using locally established cut-off values) are offered invasive prenatal diagnosis (IPD) to provide a definitive result. IPD, carried out using either chorionic villus sampling (CVS) or amniocentesis to obtain fetal cells, is associated with an estimated miscarriage risk of 0.5–1% [1]. Given the FPR of conventional screening protocols a not negligible number of pregnancies undergo unnecessary IPD. The identification of cell-free fetal DNA (cffDNA) in plasma of pregnant women [2] has opened new possibilities to improve non-invasive prenatal screening of common fetal aneuploidies. In the last decade, several groups developed massively parallel sequencing (MPS) – using targeted or whole genome approaches – of cell-free DNA (cfDNA) from maternal plasma to detect fetal aneuploidies [3, 4]. These approaches, referred to as non-invasive prenatal testing (NIPT) or non-invasive prenatal screening (NIPS), have been shown to outperform traditional screening protocols. According to a recent meta-analysis, the DR of NIPT was 99.2% for T21, 96.3%, 91.0% and 90.3% for T18, T13 and monosomy X, respectively; the FPR was below 1% for all of these aneuploidies [5]. Since 2011, NIPT became commercially available in the USA and China and was rapidly introduced into standard prenatal care in many countries.

Cell-free-DNA-based screening: validation of a method for fetal aneuploidies risk
The conventional first-trimester screening (FTS) is currently offered to all pregnant women by the public health system in Tuscany. Recently, we validated a NIPT method based on whole genome MPS approach [6], in order to introduce a more robust screening test within the public health system. In whole genome approach, maternal and fetal DNA fragments (called reads) are sequenced simultaneously in a single run. Sequence reads were aligned to specific chromosome locations within the human genome and the number of reads mapped to the chromosome of interest are counted. A relative increase or decrease in the number of reads respect to a predefined threshold value (Z-score) reveals a potential risk of aneuploidy for a specific chromosome. In particular, a trisomy was called when Z-score >3 (Fig. 1). MPS was performed on a total of 381 cfDNA samples isolated from maternal plasma by two steps: a first set of 186 euploid samples was analysed to generate a preliminary reference dataset (group A) and a second set of 195 samples (group B) – enriched by 69 aneuploid cases – was analysed in blind versus the reference dataset to verify the reliability of our sequencing protocol as well as the analysis method. One hundred and fifty samples from group A (80.6%) and 177 samples from group B (90.8%) gave resulted suitable (>10×106 mapped reads) for downstream data analysis. The two groups (A and B) were then merged to generate a definitive dataset (n=327), which was then used to re-analyse the whole study population. Since the fetal fraction (FF) (i.e. the proportion of fetal DNA to the total cfDNA in maternal plasma) is a parameter that strictly influences NIPT performance [7], a droplet digital PCR (ddPCR) protocol has been validated for its assessment [8]. FF quantification by ddPCR was performed in 178/381 (46.7%) samples after methylation-sensitive DNA digestion. Absolute quantification of both fetal (on digested RASSF1A) and total DNA (on TERT and undigested beta-actin/RASSF1A) was calculated as the ratio between the average copies/µL of fetal DNA and total DNA. An SRY assay was used for fetal gender assessment [6].

Results of the validation study
Considering the performance of the definitive reference dataset, all positive samples for T21 (n=43), T18 (n=6) and T13 (n=7) were correctly identified (sensitivity 99.9%). Five false positive (FP) results were observed: three for T21 (specificity 98.9%) and two for T13 (specificity 99.4%).

Z-score values of true positive (TP) cases for T21 and T13 were always higher than 4.6 and 6.6, respectively. Conversely, all Z-score values of FP cases for T21 and T13 lay within 3.0 and 4.0 (the so-called Z-score ‘grey zone’). Sex chromosome status was correctly assigned in 317/324 (97.8%) cases: 166 males, 149 females and 2 cases with monosomy X. In 3/327 (0.9%) samples fetal gender could not be assigned because of an inconclusive result in data analysis. Seven discordant cases between MPS and follow-up data were observed. The only case of false negative (FN) male has been explained by a low FF (0.3%), underling the importance of FF determination. Only two out of four cases with monosomy X were correctly identified by NIPT, while the remaining two cases were erroneously classified as male.

Discussion
NIPT is an accurate screening test without associated risk for the mother and/or the fetus and it can be performed early in pregnancies, starting from 9–10 weeks of gestation. It is suitable both in low- and high-risk pregnancies, even if the positive predictive value (PPV) of the test (the chance that the positive result is a true positive) is lower in low risk cohorts. Two large studies show that in the general population NIPT outperforms conventional screening tests for T21 with a PPV ranging from 45.5 to 80.9% versus a PPV of 3.4–4.2% [9, 10]. Pre- and post-test counselling to inform patients about benefits, risks, test failure and testing alternatives should be provided before offering cfDNA screening. Owing to a series of intrinsic limitations, NIPT cannot be considered a diagnostic tool, despite its high performance. In the management of pregnancies with a high-risk NIPT, the possibility of FP results should always be taken into account and IPD should be recommended after a NIPT-positive result. cffDNA derives from the apoptosis of the placental cytotrophoblast cells, therefore in rare cases it may not represent the genetic constitution of the fetus. FP results may arise from confined placental mosaicism (CMP) in which some or all trophoblastic cells are trisomic, whereas the fetus is normal (1–2% of first-trimester placentas) [11]. FN results are a very rare occurrence and can be explained by fetoplacental discrepancies, in which the fetus shows an abnormal karyotype but the chromosome aberration is absent in the cytotrophoblast and, therefore, in the cffDNA. Additional sources of FP results can be unanticipated finding such as maternal chromosome abnormalities (maternal mosaicisms, microdeletions and other copy-number variations) or maternal malignancy, or the presence of a vanishing twin with an early loss of a trisomic fetus.

Failure to provide a result occurs in 1.6–6.4% of NIPT. Both laboratory technical issues or low FF can cause the failure [12]. Low FF in frequently found in overweight pregnant women, in which the low FF could be due to a dilution effect of an increased blood volume or to the high turn-over of adipocytes [13]. In these cases it is not advisable to repeat the test on a new sample because the probability of a second test failure is quite high. An accurate clinical management of cases with low FF and normal maternal weight is instead recommended, because FF is lower in pregnancies with aneuploid fetuses (T18, T13, monosomy X and triploidy) compared to euploid pregnancies [14].

NIPT has rapidly spread in many countries through commercial provider, leading to a re-examination of current screening methods, and several models of implementation of NIPT have been proposed with pros and cons. Current guidelines recommend that “in countries where prenatal screening is offered as a public health service, governments and public health authorities should assume an active role to ensure the responsible introduction of NIPT” [15].

Our study represents the first experience of NIPT within the Italian public health system. Following our validation study, NIPT testing for T21, T13 and T18 has been introduced as a clinical service for all pregnant women after 10+4 week of gestation upon payment. Our regional health system has planned a pilot study of two years to evaluate the benefit-to-cost ratio of NIPT introduction into routine prenatal care to support the current screening strategy based on nuchal translucency measurement and maternal serum biomarker quantification. NIPT will be offered in an adequate context of pre- and post-test counselling as an alternative option to IPD in pregnant women with high risk after FTS and applying the national cut-off of 1:250. We expect that this strategy would lead to a significant reduction in unnecessary IPD due to FP results of FTS with a reduction in fetal losses associated to diagnostic procedures among high-risk women, allowing us to offer the best screening strategy currently available.

References
1. Tabor A, Alfirevic Z. Update on procedure-related risks for prenatal diagnosis techniques. Fetal Diagn Ther 2010; 27(1): 1–7.
2. Lo YM, Corbetta N, Chamberlain PF, Rai V, Sargent IL, Redman CW, Wainscoat JS. Presence of fetal DNA in maternal plasma and serum. Lancet 1997; 350(9076): 485–487.
3. Chiu RW, Chan KC, Gao Y, Lau VY, Zheng W, Leung TY, Foo CH, Xie B, Tsui NB, et al. Noninvasive prenatal diagnosis of fetal chromosomal aneuploidy by massively parallel genomic sequencing of DNA in maternal plasma. Proc Natl Acad Sci U S A 2008; 105(51): 20458–20463.
4. Fan HC, Blumenfeld YJ, Chitkara U, Hudgins L, Quake SR. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci U S A 2008; 105(42): 16266–16271.
5. Gil MM, Quezada MS, Revello R, Akolekar R, Nicolaides KH. Analysis of cell-free DNA in maternal blood in screening for fetal aneuploidies: updated meta-analysis. Ultrasound Obstet Gynecol 2015; 45(3): 249–266.
6. Gerundino F, Giachini C, Contini, E Benelli M, Marseglia G, Giuliani C, Marin F, Nannetti G, Lisi E, et al. Validation of a method for noninvasive prenatal testing for fetal aneuploidies risk and considerations for its introduction in the Public Health System. J Matern Fetal Neonatal Med 2017; 30(6): 710–716.
7. Palomaki GE, Kloza EM, Lambert-Messerlian GM, Haddow JE, Neveux LM, Ehrich M, van den Boom D, Bombard AT, Deciu C, et al. DNA sequencing of maternal plasma to detect Down syndrome: an international clinical validation study. Genet Med 2011; 13(11): 913–920.
8. Chan KC, Ding C, Gerovassili A, Yeung SW, Chiu RW, Leung TN, Lau TK, Chim SS, Chung GT, et al. Hypermethylated RASSF1A in maternal plasma: A universal fetal DNA marker that improves the reliability of noninvasive prenatal diagnosis. Clin Chem 2006; 52(12): 2211–2218.
9. Bianchi DW, Parker RL, Wentworth J, Madankumar R, Saffer C, Das AF, Craig JA, Chudova DI, Devers PL, et al. DNA sequencing versus standard prenatal aneuploidy screening. N Engl J Med 2014; 370(9): 799–808.
10. Norton ME, Jacobsson B, Swamy GK, Laurent LC, Ranzini AC, Brar H, Tomlinson MW, Pereira L, Spitz JL, et al. Cell-free DNA analysis for noninvasive examination of trisomy. N Engl J Med 2015; 372(17): 1589–1597.
11. Kalousek DK, Vekemans M. Confined placental mosaicism. J Med Genet 1996; 33(7): 529–533.
12. Yaron Y. The implications of non-invasive prenatal testing failures: a review of an under-discussed phenomenon. Prenat Diagn 2016; 36(5): 391–396.
13. Haghiac M, Vora NL, Basu S, Johnson KL, Presley L, Bianchi DW, Hauguel-de Mouzon S. Increased death of adipose cells, a path to release cell-free DNA into systemic circulation of obese women. Obesity 2012; 20(11): 2213–2219.
14. Rava RP, Srinivasan A, Sehnert AJ, Bianchi DW. Circulating fetal cell-free DNA fractions differ in autosomal aneuploidies and monosomy X. Clin Chem 2014; 60(1): 243–250.
15. Dondorp W, de Wert G, Bombard Y, Bianchi DW, Bergmann C, Borry P, Chitty LS, Fellmann F, Forzano F, et al. Non-invasive prenatal testing for aneuploidy and beyond: challenges of responsible innovation in prenatal screening. Summary and recommendations. Eur J Hum Genet 2015; doi: 10.1038/ejhg.2015.56.

The authors
Francesca Gerundino BS, Claudia Giachini PhD, Costanza Giuliani BS, Elisa Contini BS, Elisabetta Pelo* MD
Diagnostic Genetic Unit, Careggi University Hospital, Florence, Italy

*Corresponding author
E-mail: peloe@aou-careggi.toscana.it

Pediatric reference intervals: tailoring reference intervals to the target population

, 26 August 2020/in Featured Articles /by 3wmedia

The use of suitably matched reference values derived from well-characterized individuals is critical to avoid misdiagnosis. Developing reference intervals in pediatric populations presents unique challenges. Recognizing these issues and bridging international biorepository efforts is essential to improving pediatric healthcare.

by Dr Carmen Gherasim, Sonia L. La’ulu, Sara P.Wyness and Dr Joely A. Straseski

Introduction
Children are unique individuals with dynamic developmental physiology which must be accounted for during clinical evaluation. In addition to a medical history and clinical examination, laboratory investigations constitute an integral part of diagnosis and therapeutic management. Understandably, children have limited ability to describe symptoms and medical providers must rely strongly on clinical and laboratory investigations. The availability of pediatric reference intervals (PRI) is, therefore, essential to avoid missed opportunities for treatment of preventable conditions and adverse consequences due to wrong diagnosis.

Depending on the analyte, PRI can differ substantially from RI in adult populations. Marked differences can even be observed within the pediatric population, with profound changes occurring during timeframes such as the first year of life or puberty. The dynamic process of growth, from the initial adaptation of infants living outside the womb to full sexual maturation associated with adulthood, is accompanied by changes in body composition and metabolic, immune, and hormonal fluctuations [1]. Furthermore, developmental stages in children, particularly during puberty, do not always correlate with age and can be affected by factors including nutritional status, body mass index (BMI), medications, or therapies (i.e. growth hormone therapy) [2]. It is therefore advantageous to develop PRI using clearly defined demographics such as age, sex, and race, along with stratifications accounting for physiological and sexual development to assist in clinical decision making.

From a clinical laboratory perspective, the dynamics of the sampled population along with regulatory and administrative requirements for determining PRI pose unique challenges for establishing high quality RI for pediatric populations [3]. Such challenges include: (1) defining ‘healthy’ in children; (2) obtaining research ethics board approval for sample collections; (3) obtaining informed parental and/or child consent for participation; (4) accommodating small sample volumes; and (5) partitioning of data. Collectively, these can hamper access to a sufficient number of specimens from healthy children, precluding many laboratories from performing PRI studies.

Origination of PRI
While RI studies may be performed by in vitro diagnostic manufacturers for commercially available assays, these often lack pediatric data or have minimal stratification reflecting children’s physiological variability. Although not unique to the pediatric population, recycling of RI determined using obsolete methods/instrumentation or adoption of RI from previously published sources without study traceability are often used to report and interpret laboratory data. Caveats in PRI adoption include the limited number of studies performed in pediatric populations, inclusion of data from hospitalized patients, data analysis techniques used, and lack of detailed information from existing studies including population tested and certain patient demographics. Therefore, careful examination of subjects and methods used in existing studies is critical.

Given the complexity of establishing new PRI, clinical laboratories should carefully consider the option to: (1) transfer, (2) verify or (3) establish PRI based on requirements addressed in guidelines (e.g. Clinical and Laboratory Standards Institute (CLSI) EP28-A3c) [4]. When a laboratory changes analytical methods for measuring an analyte for which they have previously established a RI, that RI may be ‘transferred’ following an acceptable method comparison study. This strategy may prove particularly useful when obtaining pediatric specimens is difficult. Other pre-determined RI must be ‘verified’ before their adoption by examining whether results from a minimum of 20 healthy individuals fall between the proposed limits. Finally, ‘establishing’ new RI requires examining a minimum of 120 samples for each statistically distinct group (partition) selected from qualified, healthy individuals chosen using well-defined inclusion/exclusion criteria (Fig. 1). This is an enormous effort, but particularly onerous in pediatric populations due to limited samples and numerous partitions. Due to this, routine clinical laboratories are often limited to verifying rather than establishing PRI.

Selection of reference populations for PRI
Ideally, reference studies should be conducted using ‘healthy’ volunteers but the identification and recruitment of a healthy representative pediatric population is complex. Health status of recruited children may be assessed by physical examination and health-related questionnaires; however, a definitive delineation of ‘healthy’ is challenging in pediatric populations, as subclinical problems may go unrecognized. Obtaining a sufficient number of pediatric specimens from truly healthy children remains a rate-limiting step of this process.

One strategy that circumvents this challenge is the use of residual blood samples from hospitalized children, deemed healthy due to unrelated or inconsequential medical conditions. This approach eliminates the undesirable blood collection procedure but can be affected by the lack of medical history that can influence test results and overall quality of the PRI established. Additionally, data mining methods exploiting laboratory results from hospitalized children can be used. First introduced by Robert Hoffmann, this approach assumes that the majority of specimens measured in the clinical laboratory represent ‘normal’ values, whereas the most extreme values account for the sickest populations [5].
The effect of common physiological variables such as age and sex on pediatric biomarkers is often evaluated using specific RI partitions. Complex factors such as BMI, Tanner staging, and race may also influence the concentration of select analytes. Despite the rising incidence of pediatric obesity in many developed countries, the effect of BMI on PRIs has been largely overlooked [6]. Challenges in designing studies to address this issue include susceptibility of analytes to BMI variability and delineation between BMI and changes in body composition throughout development. Also, physical and pubertal development in children is not always parallel. Surges in hormone concentrations trigger sexual development and their concentrations vary with the presence of secondary sexual characteristics (described by Tanner stages) rather than age. Finally, race-specific differences can also influence concentrations of pediatric analytes and covariates such as BMI, but their contribution could be underestimated due to their multifactorial etiology.

Statistical methods for PRI analysis
Laboratory data is interpreted in the context of RI, which typically describe the central 95% of results from a population of healthy volunteers. One particular concern with PRI is the availability of sufficient numbers of specimens to allow for statistical significance of each partition [7]. Selection of an appropriate statistical method to compute RI is dependent on the number of specimens in each partition and overall distribution of the data. A small number of pediatric specimens can result in sampling variability which decreases the confidence that a normal result will fall in the established RI. General statistical methods can be employed including parametric (smaller specimen numbers following Gaussian/normal distribution) and nonparametric methods (minimum 120 reference observations per partition). Current CLSI guidelines recommend the use of nonparametric approaches for estimation of RI as they do not make assumptions regarding the distribution of the data [4]. Despite the easy access to data, assumptions regarding data distribution and weak correlation studies often reveal that Hoffmann statistical approaches may be unreliable for establishing new RI in pediatric populations [8]. Increasing the statistical power of PRI determinations and understanding the effect of covariates remains challenging due to the large number of specimens required that can only be reasonably addressed in multicentre RI initiatives.

Current studies emphasizing the need for PRI
The importance of well-defined PRI is highlighted by our recent studies investigating a number of analytes with different concentrations in pediatric populations as compared to adults. A valuable component of our studies is the use of large numbers of well-characterized subjects selected using clearly defined inclusion/exclusion and partition criteria. Whereas some analytes show significant differences between age and sexes (bone markers osteocalcin, procollagen type 1N-terminal propeptide, bone-specific alkaline phosphatase, and C-telopeptide; thyroglobulin and free triiodothyronine), others do not (free thyroxine) [9–11]. PRI for 5α-dihydrotestosterone identified significant differences between sexes and Tanner stages [12]. These differences highlight the need for PRI for individual analytes and the large dataset for each partition in these studies was critical for appropriate data analysis and delineation of proper PRI. As the majority of clinical laboratories verify rather than establish PRI, reported studies should be carefully reviewed for study design, selection of reference individuals, methods used, analytical quality, and appropriate statistical analysis of the data before being considered for PRI adoption.

PRI initiatives
There are a number of initiatives around the world striving to improve the quality and accuracy of PRI (Table 1). Their major advantage is the recruitment of large cohorts of healthy children and adolescents using well-defined selection and partition criteria. Understandably, recruitment strategies vary between initiatives but generally include soliciting members of local communities, organizing clinics at schools and community centres, and/or enrolment prior to undergoing elective, non-invasive, outpatient surgeries. Although each initiative has focused on different biomarkers, most focus on common biochemical markers, blood analytes, vitamins, and/or hormones [13–16]. All studies included partitions for pediatric populations by age and sex and most reference values were analysed using nonparametric statistics to define central 95% PRI. Additional covariates such as Tanner staging, race, or BMI were addressed only by select initiatives including CHILDx, CALIPER and IDEFICS, respectively. A shortcoming of many of the studies was the relatively small representation of other races. Multicentre studies could address this problem, thereby promoting harmonization and diversity amongst PRI. In a recent position statement, the American Association for Clinical Chemistry expressed their support for the foundation of a national repository, enabling a comprehensive evaluation of all PRI covariates and provide and maintain up-to-date PRI databases [17].

Concluding remarks
Reference intervals for pediatric populations weigh heavily in the interpretation of laboratory results and can impact the outcome of clinical decisions. In recent years, we have witnessed an increased awareness of the ‘malpractices’ in PRI including adoption from suboptimal RI studies or from populations that do not mirror the healthy state in children. Recommendations to establish individual PRI that adequately represent the numerous variables in children’s development remain hard to meet by most laboratories, reinforcing a need for a collective effort in establishing PRI. Concentrating national and international efforts can support PRI initiatives and improve pediatric healthcare overall.

References
1. Coffin CM, Hamilton MS, Pysher TJ, Bach P, Ashwood E, Schweiger J, Monahan D, Perry D, Rogers BB, et al. Pediatric laboratory medicine: current challenges and future opportunities. Am J Clin Pathol 2002; 117(5): 683-690.
2. Sikaris KA. Physiology and its importance for reference intervals. Clin Biochem Rev 2014; 35(1): 3-14.
3. Tahmasebi H, Higgins V, Fung AWS, Truong D, White-Al Habeeb NMA, Adeli K. Pediatric reference intervals for biochemical markers: gaps and challenges, recent national initiatives and future perspectives. EJIFCC 2016; 28(1): 43–63.
4. Defining, establishing, and verifying reference intervals in the clinical laboratory. Approved guideline-third edition. CLSI document EP28-A3c. Clinical Laboratory Standards Institute 2008.
5. Hoffmann RG. Statistics in the practice of medicine. JAMA 1963; 185: 864–873.
6. Erhardt E, Foraita R, Pigeot I, Barba G, Veidebaum T, Tornaritis M, Michels N, Eiben G, Ahrens W, et al. Reference values for leptin and adiponectin in children below the age of 10 based on the IDEFICS cohort. Int J Obes (Lond) 2014; 38 Suppl 2: S32–38.
7. Daly CH, Liu X, Grey VL, Hamid JS. A systematic review of statistical methods used in constructing pediatric reference intervals. Clin Biochem 2013; 46(13–14): 1220–1227.
8. Shaw J, Cohen A, Konforte D, Binesh-Marvasti T, Colantonio DA, Adeli K. Validity of establishing pediatric reference intervals based on hospital patient data: a comparison of the modified Hoffmann approach to CALIPER reference intervals obtained in healthy children. Clin Biochem 2014; 47(3): 166–172.
9. Wyness SP, Roberts WL, Straseski JA. Pediatric reference intervals for four serum bone markers using two automated immunoassays. Clin Chim Acta 2013; 415: 169–172.
10. Owen WE, Bunker AM, Straseski JA. Pediatric reference intervals for thyroglobulin using the Beckman Coulter Access 2 immunoassay. Clin Chim Acta 2014; 435: 40–41.
11. La’ulu SL, Rasmussen KJ, Straseski JA. Pediatric reference intervals for free thyroxine and free triiodothyronine by equilibrium dialysis-liquid chromatography-tandem mass spectrometry. J Clin Res Pediatr Endocrinol 2016; 8(1): 26–31.
12. Lin DC, Straseski JA. Tanner stage-stratified pediatric reference intervals for dihydrotestosterone [Abstract]. Clin Chem 2016; 62(10): S188.
13. Colantonio DA, Kyriakopoulou L, Chan MK, Daly CH, Brinc D, Venner AA, Pasic MD, Armbruster D, Adeli K. Closing the gaps in pediatric laboratory reference intervals: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clin Chem 2012; 58(5): 854–868.
14. Kohse KP, Thamm M. KiGGS-the German survey on children’s health as data base for reference intervals. Clin Biochem 2011; 44(7): 479.
15. Kant AK, Graubard BI. Race-ethnic, family income, and education differentials in nutritional and lipid biomarkers in US children and adolescents: NHANES 2003-2006. Am J Clin Nutr 2012; 96(3): 601–612.
16. Ridefelt P. Population-based pediatric reference intervals in general clinical chemistry: a Swedish survey. J Med Biochem 2015; 34(1): 64–65.
17. Pediatric lab results: the need for “normal.” AACC Position Statement. AACC 2016; 1–3.

The authors
Carmen Gherasim¹ PhD, Sonia L. La’ulu² BS, Sara P. Wyness² BA, and Joely A. Straseski*^1,2 PhD
¹Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA
²ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA