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Is it a urinary tract infection?

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Whilst the respiratory tract is the most frequent site of infection, in the developed world the urinary tract is the second most frequent site; 50% of otherwise healthy women will experience at least one UTI. The shortness of the female urethra and its proximity to both vagina and anus in women contribute to the much greater frequency of UTIs in women compared with men. Sexually active women are most vulnerable to infection, and up to 40% of women who have a UTI will experience a repeat infection within a year. Quite apart from the extreme discomfort that uncomplicated UTIs can cause, complications such as pyelonephritis can lead to pyonephrosis, sepsis and kidney failure. Thus, diagnosed UTIs are treated with antibiotics such as trimethoprim, alone or in combination with sulfamethoxazole.
However, resistance to antibiotics and the urgent need for ‘antibiotic stewardship’ is rightfully discouraging primary healthcare providers from prescribing these drugs without a diagnosis of a bacterial infection. This is especially relevant in the case of upper respiratory tract infections, which are most frequently viral and self-limiting. But in the case of UTIs the dipstick test, and even standard lab-cultured urine, can give false negative results resulting in the withholding of necessary treatment. A recently published Belgian study based at the University of Ghent carried out quantitative PCR on urine samples for two bacterial species (and four sexually transmitted pathogens), in addition to standard urine culture, in 220 women with symptoms of UTI and 86 healthy volunteers. The tested bacterial species were Escherichia coli, present in the intestinal flora and the causative organism of over two thirds of UTIs, and Staphylococcus saprophyticus, the second most common causative organism present in the normal perineal and vaginal flora. In the symptomatic group standard culture detected uropathogens in 81% of the urine samples, but with qPCR 95.9% of these samples were found positive for E.coli and 8.6% for S. saprophyticus. There were also two samples positive for sexually transmitted pathogens. Overall 98% of the symptomatic women were found to have pathogens in their urine, compared with 10% of the control women.
So standard urine culture is not as infallible as was previously thought and dipstick tests, whilst quick and cheap, cannot be used to rule out a UTI. Because of the reliance on such tests, many women presenting with a UTI are sent home again without antibiotics being prescribed. Yes, we need stewardship, but clearly women with the typical urinary symptoms have an infection that should be treated!
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Next-generation sequencing as a diagnostic tool in respiratory infections

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Viral diagnostic methods have evolved greatly in recent decades, with perhaps the most significant change being the widespread use of nucleic acid amplification tests (NAAT). The introduction of next-generation sequencing methods may see a further change in the landscape of infection diagnostics. Here we discuss some of the potential benefits and challenges with this approach.

by Dr Fiona Thorburn

Background
The term ‘acute respiratory tract infection’ (ARTI) encompasses a spectrum of conditions ranging from the common cold to pneumonia. Multiple organisms may infect the human respiratory tract, such as bacteria and fungi, but the majority of episodes are thought to be viral in origin [1]. The diagnosis of ARTI is made clinically but the lack of pathognomonic features means etiology cannot be determined clinically. Identifying causative organisms is challenging owing to the number of possibilities but is important for many reasons. In severe cases identification of an organism will guide therapeutics, although specific treatments for viral ARTI are generally limited to influenza. At the other end of the clinical spectrum identifying a viral cause in mild cases allows the physician to defer treatment with antibiotics and reassure the patient. This is an essential component of antimicrobial stewardship as high rates of antibiotic use are associated with circulating antimicrobial resistance [2]. The majority of antibiotic prescriptions issued in the UK are for respiratory tract infections [3] yet antibiotic use puts patients at risk of adverse drug reactions and in many cases will not lessen the duration of symptoms [4].

Respiratory infection diagnostics
Until recently viral diagnostics relied on cell culture or animal/egg inoculation. These time-consuming and laborious methods provided only a retrospective diagnosis and were, therefore, of little use in the management of acute infections. Nucleic acid amplification tests (NAAT), directly detecting the RNA or DNA of pathogens, have largely superseded these.

Many methods of RNA/DNA detection are available (Table 1). The most widely used is polymerase chain reaction (PCR). This has the benefit of rapid turnaround times and high levels of sensitivity and specificity in comparison to cell culture [5]. A pair of primers is required for each target but it is possible to use multiple primer sets within a single reaction (up to four) without compromising test sensitivity over the monoplex assay. This chemistry is now available as closed systems providing rapid results as a near-patient test (GeneXpert by Cepheid, Cobas by Roche).

Other examples of molecular NAATs which are available but not commonplace would be loop-mediated isothermal amplification (LAMP) and microarrays. LAMP detects nucleic acids but does not rely on thermocycling. It does, however, require multiple primers for each target (usually six) and as a consequence the sensitivity is more likely to be affected by genome mutations than standard PCR. This also makes multiplexing multiple targets within a single reaction more complicated.

Microarrays (also known as DNA chips or biochips) use a collection of oligonucleotide probes, about 70 bases in length, immobilized on a solid surface. The probes are complementary portions of DNA or RNA designed to match conserved regions of a genome; thus, if present, the target will bind to the corresponding probe which can then be quantified. Multiple probes may be attached to a single surface, screening for a large number of pathogens in a single reaction. As probes are targeted against conserved regions of the pathogen genome they may also detect related but novel pathogens.

To be used as a comprehensive diagnostic test numerous targets must be included to cover the likely pathogens. In the case of respiratory infections commercial assays are available with around 33 targets over 8 reactions [6]. Despite this approach a viral pathogen is detected in only a minority of specimens [7] and it remains the case that a pathogen will only be detected if actively sought.

Several significant respiratory viruses have been identified in recent years; those which may have circulated for many years, such as the human metapneumovirus, or emerging pathogens, such as SARS and, more recently, MERS. Whereas these are related to other known pathogens they are genetically distinct and, therefore, would evade detection with molecular methods.

What is next-generation sequencing?
The term ‘next-generation sequencing’ (NGS) refers to the practice of sequencing millions of DNA fragments in parallel. Numerous platforms are available to carry this out and the exact chemistry varies greatly between each. In practice, either all genetic material within a sample can be sequenced – metagenomics; or, hybrid capture allows a more focused approach to an area or genome of interest, this is termed ‘target enrichment’.

Advantages of NGS
Applying metagenomic NGS to clinical samples would allow an untargeted approach to identify all the genetic material contained within. This method has demonstrated potential for use in a diagnostic setting [8, 9].
The lack of pathogen targeting means that multiple pathogens can be detected without selection (Fig. 1), including novel or emerging or divergent pathogens. In the case of many viral pathogens evolution and mutations over time can reduce detection with specific PCR reactions. Mutations affecting primer binding sites may reduce binding affinity during the reaction and, for this reason, the performance of diagnostic assays must be monitored closely and at times altered. NGS could, therefore, be used as an adjunct in the quality control of PCR assays.

It is possible to detect full genome sequences from diagnostic samples and even with partial genome sequence it is feasible to subtype viral pathogens. Real-time knowledge of the circulating viral subtype is of particular importance in the management of influenza where this informs anti-viral choice, potential resistance and vaccine efficacy. This is currently carried out using additional PCR assays and Sanger sequencing, although this is not always possible in real-time.

Laboratory workflow
Currently the identification of rare or unusual pathogens using molecular methods necessitates samples to be batched to make the process cost effective; alternatively the test is centralized to a single laboratory to which samples must be sent. Either results in an increase in turn-around time. The use of NGS without any enrichment or targeting would permit samples to be treated in the same manner irrespective of type or likely pathogen.

Challenges
A major barrier in introducing NGS to the diagnostic setting is cost. Although the cost of NGS is decreasing rapidly it remains considerably more expensive than multiplex PCR. It is, therefore, unlikely to be cost effective to use this method for pathogen detection in non-severe infections for the time being. However, any cost–benefit analysis on introducing NGS to a diagnostic setting should also consider, on the positive side of the balance sheet, the likely savings NGS would offer in reductions to epidemiological and public health testing.

Complexity and turnaround time
Current methods of library preparation are complex requiring multiple user interventions and additional equipment to that found in a diagnostic laboratory with attendant implications for the time and cost of the process. To be carried out as a routine diagnostic assay these processes would need to be simplified and, ideally, automated to reduce hands-on time and the potential for contamination and human error.
The commonly used sequencing platforms take several hours or even days to generate sequence information. It should be noted that the third generation platforms that use single-molecule real-time (SMRT) technology are rapid and, as the name suggests, can be analysed in almost real-time.

Data analysis
Data analysis and storage is a major bottleneck in the NGS process. The computational power required for analyses would be beyond the current capabilities of diagnostic services. The methods used in data analysis pose a further challenge. Currently there is no agreed method as to the best approach for data analysis; indeed this is an entire specialty in itself, bioinformatics. Development of software programmes will both make the analysis more feasible in a diagnostic service to non-bioinformaticians and will lead to standardization of data processing.

Discussion
NGS undoubtedly has potential to dramatically change the landscape of infection diagnostics. Whether it will replace current molecular methods remains to be seen. The cost and complex sample processing remains prohibitive but these novel technologies are still in an exponential phase of development. Even current methodologies are yielding promising results in this field. The lack of pathogen targeting means that there is potential for a single work flow to be applied to all specimens, no matter what the syndrome which could even be extended to non-viral pathogens, resulting in a pan-microbial diagnostic test.

The generation of virus sequence as part of a diagnostic assay has substantial management and epidemiological benefits. In terms of respiratory infections this is currently limited to resistance testing and strain analysis of influenza. However, in the management of blood-borne viruses, particularly HIV and hepatitis C virus (HCV), point mutations and minor populations may impact greatly on the management and prognosis of patients. With the introduction of novel therapies or vaccines against viral respiratory infections NGS will have an even greater clinical benefit.

Acknowledgements
I would like to thank Dr Rory Gunson and Dr Emma Thomson for reviewing the manuscript.

References
1. Clark TW, Medina MJ, Batham S, Curran MD, Parmar S, Nicholson KG. Adults hospitalised with acute respiratory illness rarely have detectable bacteria in the absence of COPD or pneumonia; viral infection predominates in a large prospective UK sample. J Infect 2014; 69(5): 507–515.
2. Linares J, Ardanuy C, Pallares R, Fenoll A. Changes in antimicrobial resistance, serotypes and genotypes in Streptococcus pneumoniae over a 30-year period. Clin Microbiol Infect 2006; 16(5): 402–410.
3. Lindbaek M. Prescribing antibiotics to patients with acute cough and otitis media. Br J Gen Pract 2010; 56(524): 164–166.
4. Butler CC, Hood K, Verheij T, Little P, Melbye H, Nuttall J, Kelly MJ, Mölstad S, Godycki-Cwirko M, Almirall J, Torres A, Gillespie D, Rautakorpi U, Coenen S, Goossens H. Variation in antibiotic prescribing and its impact on recovery in patients with acute cough in primary care: prospective study in 13 countries. BMJ 2009; 338: b2242.
5. van Elden LJ, van Kraaij MG, Nijhuis M, Hendriksen KA, Dekker AW, Rozenberg-Arska M, van Loon AM. Polymerase chain reaction is more sensitive than viral culture and antigen testing for the detection of respiratory viruses in adults with hematological cancer and pneumonia. Clin Infect Dis 2002; 34(2): 177–183.
6. FTD Respiratory Pathogens 33. Fast-track Diagnostics 2016. (http://www.fast-trackdiagnostics.com/products/ftd-respiratory-pathogens-33/)
7. Nickbakhsh S, Thorburn F, von Wissmann B, McMenamin J, Gunson RN, Murcia PR. Extensive multiplex PCR diagnostics reveal new insights into the epidemiology of viral respiratory infections. Epidemiol Infect 2016; 144(10): 2064–2076.
8. Thorburn F, Bennett S, Modha S, Murdoch D, Gunson R, Murcia PR. The use of next generation sequencing in the diagnosis and typing of respiratory infections. J Clin Virol 2015; 69: 96–100.
9. Prachayangprecha S, Schapendonk CM, Koopmans MP, Osterhaus AD, Schürch AC, Pas SD, van der Eijk AA, Poovorawan Y, Haagmans BL, Smits SL. Exploring the potential of next-generation sequencing in detection of respiratory viruses. J Clin Microbiol 2014; 52(10): 3722–3730.

The author
Fiona Thorburn PhD
NHS Greater Glasgow and Clyde, Glasgow G12 0XH, UK


E-mail: Fionathorburn@nhs.net

C303 Adeli Figure 1 crop

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

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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.
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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

27066 Adaltis 92X270 FEB 2017 new

EXTRAlab – Advanced robotic systems

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C282 E77 UriSed3

Evaluation of UriSed 3 automated urine microscopy analyser

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Urinalysis may provide evidence of significant renal disease in asymptomatic patients. The microscopic urinalysis is vital to making diagnoses in many asymptomatic cases, including urinary tract infection, urinary tract tumors, occult glomerulonephritis, and interstitial nephritis.

Presence or absence of different particles in urine sediment is crucial for clinical decision making. Urine sediment cells or particles provide important information for the diagnosis of renal or urinary diseases [1]. The patented UriSed Technology was developed to reduce the shortcomings of manual microscopy through automation. [2]. The UriSed analysers provide a reliable and reproducible solution since 2007 [3]. The new generation instrument based on the improved UriSed Technology, UriSed 3 was introduced in the market in 2015. UriSed 3 is an automated urine microscopy analyser with a revolutionary particle visualization utilizing both bright-field and phase-contrast microscopy. In the present study, we evaluated the analytical performance of UriSed 3 Automated Urine Microscopy Analyser (Manufactured by 77 Elektronika Kft., Budapest) and compared the results to those from manual microscopy using standardized KOVA counting chambers.

UriSed 3 provides quantitative Red Blood Cell (RBC) and White Blood Cell (WBC) results, and semi-quantitative results for all other particle types: WBC Clumps (WBCc), Squamous Epithelial Cells (EPI), Non-squamous Epithelial Cells (renal tubular and urothelium cells) (NEC), Crystals (CRY): Calcium oxalate dihydrate (CaOxd), Calcium oxalate monohydrate (CaOxm), Uric acid (URI), and Triple-phosphate crystals (TRI), Hyaline casts (HYA), Pathological casts (PAT), Bacteria (cocci-like and rod-like) (BACc, BACr), Yeasts (YEA), Spermatozoa (SPRM) and Mucus (MUC) [4].

Phase-contrast microscopy by UriSed 3
Phase-contrast microscopy is an optical microscopy technique that converts phase shifts in light passing through a transparent specimen to brightness changes in the image. Phase shifts themselves are invisible, but become visible when shown as brightness variations. In particular, for urinary sediment examination, phase-contrast supplies an optimal identification of particles with a low refractive index (e.g., hyaline casts and RBC devoid of their hemoglobin content, the so-called “ghost RBC”) and of cellular morphological details) [9, 10]. Therefore the use of phase-contrast microscopy is encouraged also by international guidelines on urinalysis [5, 6].

The measurement technique of the UriSed 3 instrument is a combination of a bright-field microscope and a phase-contrast microscope in one optical system. Preparation of the UriSed 3 analyser for measurement takes only a few minutes. It needs distilled water for washing its pipette, and patented disposable plastic cuvettes for sample investigation. The instrument throughput is up to 120 samples per hour. The whole measurement process is completely automatic: 200 µl of urine sample is dispensed into the cuvette, then spinning the cuvette for a few seconds gently deposits formed elements into a monolayer at the bottom of the cuvette. The built-in digital camera takes and saves both a bright-field and a phase-contrast microscopic image from the same view-field at 15 different positions of the sediment layer. Information from both whole view-field images are evaluated by a neural network based image processing software.

Material and methods
Analysis of 311 samples was performed to evaluate UriSed 3 analytical performance compared to the manual microscopy urine examination method. Both measurements were carried out with the same anonymous urine samples. Fresh, native urine samples were used, that were typically held for no more than 4 hours before being analysed, as recommended in the relevant guidelines [5,6] to prevent change in the morphology of the particles. Samples were mixed until homogeneous and then split and run on each measuring procedure as close to the same time as possible. The standardized microscopic urinalysis of native samples (Level 3) was followed by using a KOVA counting chamber. The particle concentration for all particle types was evenly distributed in the evaluated urine samples. Carry-over, precision, diagnostic tests such as sensitivity, specificity, diagnostic accuracy, concordance and one category concordance were investigated according to well-established protocols [7].

Results
No carry-over was detected in any of the samples. UriSed 3 has better precision than microscopy at all of the tested RBC and WBC concentrations. The majority of all coefficients of variation obtained for within series imprecision (CV) using UriSed 3 was 7-16% versus 5,5-67% in case of manual microscopy [8]. Good correlation can be found between UriSed 3 and manual counting chamber for formed elements. The Pearson correlation of quantitative parameters are 0.91 (RBC), 0.93 (WBC). The clinical evaluation of UriSed 3 was based on McNemar test and concordance study. The results are shown in the table above.

Conclusion
UriSed 3 instruments utilize phase-contrast and bright-field microscopy to combine original and innovative technologies whose aim is the progressive improvement of automated urinary sediment examination and the progressive approach to the gold standard manual microscopy method. The automated measurement process of UriSed 3 is reproducible and operator-independent. Those sediment particles that are mostly transparent become visible with phase-contrast microscopy by UriSed 3, which is a spectacular advantage and leads to specific improvement in recognition at several particle types.

References
1. Fogazzi GB, The Urinary Sediment an Integrated View Third Edition. Milano: Elsevier, 2010.
2. Barta Z, Kránicz T, Bayer G. UriSed Technology – A Standardised Automatic Method of Urine Sediment Analysis. European Infectious Disease 2011;5:139–42.
3. Zaman Z, Fogazzi GB, Garigali G, Croci MD, Bayer G, Kránicz T. Urine sediment analysis: analytical and diagnostic performances of sediMAX – a new automated microscopy image-based urine sediment analyser. Clin Chim Acta 2010; 411: 147-154.
4. Fogazzi GB, Garigali G. The Urinary Sediment by UriSed Technology. A New Approach to Urinary Sediment Examination. Milano: Elsevier, 2013.
5. Kouri T, Fogazzi G, Hallander H, Hofmann W, Guder WG, editors. European Urinalysis Guidelines. Scand J Clin Lab Invest 2000; 60 (Suppl 231): 1-96.
6. Clinical and Laboratory Standard Institute (ex NCCLS). Document GP16-A3 – Urinalysis; Approved guideline, 3rd ed. Wayne, PA: CLSI, 2009.
7. T. Kouri, A. Gyory, R.M. Rowan. ISLH Recommended Reference Procedure for the enumeration of Particles in Urine. Laboratory Hematology 9:58-63, 2003.
8. Haber MH, Galagan K, Blomberg D, Glassy EF, Ward PCJ, editors. Color Atlas of Urinary Sediment; An Illustrated Field Guide Based on Proficiency Testing. Chicago: CAP Press, 2010.
9. Brody L, Webster MC, Kark RM. Identification of elements of urinary sediment with phase-contrast microscopy. JAMA 1968; 206: 1777-1781.
10. Spencer E. and Pedersen Ib. Hand Atlas of the Urinary Sediment. Bright-field, Phase-Contrast, and Polarized Light. Copenhagen: Munksgaard, 1971.

More information on UriSed 3 is available from the manufacturer:
77 Elektronika Kft., Budapest, HUNGARY
Email: sales@e77.hu, web: en.e77.hu

The author
Erzsébet Nagy MD,
Honorary Associate Professor
Head Phisician of Central Laboratory; Hospitaller Brothers of St. John of God Hospital, Budapest

27473 Coris Insertion CLI 2017 04 24

RESIST – Antibio-Resistance is rising up

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C295 Rossen Figure 1

Molecular diagnostics of pathogens using next-generation sequencing

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Current molecular diagnostics of pathogens using traditional typing methods gives limited information for outbreak investigation. Next-generation sequencing determines the DNA sequence of a complete genome and reveals information on resistance and virulence. Furthermore, it allows typing with a higher discriminatory power, which is essential for outbreak and transmission investigations.

by S. Rosema, Dr R. H. Deurenberg, Dr M. A. Chlebowicz, Dr S. García-Cobos, Dr A. C. M. Veloo, Prof. Dr A. W. Friedrich and Dr J. W. A. Rossen

Introduction
The correct identification and characterization of pathogens is essential for the successful treatment of infections and safety of patients. However, not every pathogen can be successfully cultured and the available molecular tests, mainly focusing on specific pathogens, are inadequate to detect novel genetic features in emerging pathogens. Undetected pathogens can spread easily through a hospital, resulting in a possible outbreak and putting patients admitted to hospitals at a higher risk for infections.
In recent decades, molecular diagnostic tests have improved rapidly and their role in clinical microbiology laboratories became progressively more important [1]. The turnaround time from receiving a sample to the final diagnostic result has been drastically reduced. Molecular methods, such as real-time polymerase chain reaction (PCR), Sanger sequencing and next-generation sequencing (NGS), make it possible to detect non-culturable micro-organisms. Nevertheless, some of these technologies, such as real-time PCR, require knowledge of the genomes of the micro-organisms. In addition, bioinformatics expertise is often needed to interpret the results. 
This paper addresses the use of Sanger sequencing and whole genome sequencing (WGS) in the clinical microbiology laboratory for the characterization of pathogens and outbreak management, as it is used in the University Medical Center Groningen (UMCG), one of the largest university hospitals in The Netherlands. The clinical microbiology laboratory at the UMCG receives around 5750 samples per year for detailed molecular analysis, of which approximately 1500 samples are analysed by NGS [2].

Sanger sequencing
Sanger sequencing is used to answer different molecular questions, such as the identification of bacteria and fungi in patient material or pure cultures, and the identification of mutations in specific genomic regions of interest in bacteria or viruses. In general, Sanger sequencing is used to investigate a short DNA sequence (± 500 bp) after amplification of the region of interest by PCR. After amplification, two different sequence reactions (forward and reverse) are performed and can be used to identify bacterial or fungal species based on the analyses of the sequenced 16S ribosomal DNA (rDNA) and 18S rDNA of the internal transcribed spacer (ITS) region, respectively [2]. 
One of the disadvantages of Sanger sequencing is that species identification in clinical materials containing more than one species is difficult, if not impossible. Furthermore, the costs and the labour needed for investigating multiple genomic regions of interest makes this method of limited use in modern clinical microbiology laboratories.

Next-generation sequencing (NGS)
NGS determines the whole genome sequence of different pathogens in one single sequencing run. This technology allows sample multiplexing and, thus, simultaneously provides genomic sequence information on diverse pathogens isolated from different patients. NGS also allows determination of microbial genomes in complex multi-species patient samples by shotgun metagenomics (third generation sequencing) [3]. In comparison to Sanger sequencing, NGS is a considerable improvement owing to the usage of one protocol for all pathogens [4]. A schematic overview of the general workflow used for the sequence analysis in the UMCG is shown in Figure 1.
Using NGS, the whole genome of a pathogen is sequenced in a random way. As benchtop next-generation sequencers can sequence DNA fragments between 100 and 1000 bases, the genome is fragmented before sequencing [5, 6]. Third generation sequencers are an exception to this, as they can handle larger fragments of over 200 kb [2]. NGS requires the preparation of libraries, in which fragments of DNA or RNA are linked to adapters and barcodes. At a later stage, this enables the identification of the sequenced fragments (reads) to the pathogens. After fragmentation, clonal amplification, normalization and a sequencing run is performed. For this, a robust preparation of libraries and standardized protocols are key [3].

Software for data analysis
A huge challenge for the introduction of NGS in a clinical setting is the data analysis. This requires specific software as well as scientific knowledge to interpret the results. There are, so far, only a few user-friendly software packages available to perform data analyses with little bioinformatics knowledge. However, the costs of these software packages is relatively high. However, there a numerous freely available software packages to answer different scientific questions, but knowledge of bioinformatics is often required [2]. 
After high-throughput sequencing, the reads can be assembled, either by mapping or de novo assembly [2]. Software packages, such as CLC Genomics Workbench (Qiagen), SPAdes and Velvet, can be used for assembly. The genetic relatedness between isolates can be investigated using a gene-by-gene approach using multi-locus sequence typing (MLST), core genome MLST (cgMLST) or whole genome MLST (wgMLST) using SeqSphere+ (Ridom), Bionummerics (Biomérieux), or online tools, such as Enterobase (https://enterobase.warwick.ac.uk) and BIGSdb (http://bigsdb.readthedocs.io). Currently, it is still a matter of debate how many alleles two genomes may differ by to call them genetically related. The same problem applies for comparing two genomes by single nucleotide polymorphism (SNP) typing.
There are a number of web-based tools to perform additional NGS analysis [2]. One of them is the website of the Centre for Genomic Epidemiology (www.genomicepidemiology.org) that can be used for the detection of resistance and virulence genes. Another web-based tool is the Rapid Annotation using Subsystem Technology (RAST) website (http://rast.nmpdr.org) for annotating bacterial genomes.
One of the advantages of web-based tools is that, in general, no knowledge of bioinformatics is necessary. However, a disadvantage may be the lack of tweaking the software settings while performing the analysis. In addition, it may be necessary to confirm the results obtained through web-based tools using other methods [2].

NGS in clinical microbiology
NGS is already applied in several medical microbiology laboratories where it is used for outbreak management, molecular case findings, characterization and surveillance of pathogens, for example [2].
Indeed NGS can be extremely useful in outbreak detection, by monitoring the evolution and dynamics of multi-drug resistant pathogens [7]. A number of studies have highlighted the effectiveness of WGS-based typing for assessing of (newly) emerging pathogens. In our hospital, NGS was used for the characterization of a newly emerging CTX-M-15 producing Klebsiella pneumoniae clone [8]. Transmission of this K. pneumoniae strain between patients has been traced using genomic phylogenetic analysis (Fig. 2). In addition, the study showed the usefulness of a unique marker PCR, in which a clone-specific PCR was developed to investigate the transmission between patients [4]. 
In addition to tracing and characterizing outbreaks, NGS can be used for the implementation of control measures to avoid the spread of resistance bacteria [9]. An outbreak of a colistin-resistant carbapenemase-producing K. pneumoniae (KPC) with inter-institutional spread in The Netherlands was identified and characterized using NGS and, partially based on these findings, controlled by transferring all positive patients to a separate location [9].
Furthermore, NGS data stored in databases can be used to search retrospectively for molecular case studies. A study from Bathoorn et al. showed that a New Delhi Metallo-?-lactamas-5 (NDM-5)-producing K. pneumoniae was isolated from a Dutch patient. Molecular case findings showed that the Dutch strain is clonally related to strains isolated from four Danish patients in 2014. There was no obvious epidemiological link between the cases in the Dutch and Danish hospitals [10].
These studies and many others highlight the importance of NGS in clinical microbiology. NGS can be used either as a highly discriminatory tool to discriminate between bacterial clones with specific features and to use the information for patient management, infection prevention and evolutionary studies [2] or to characterize bacterial isolates in more detail [8]. Furthermore, web-based databases can be in silico screened retrospectively for the presence of novel (antibiotic-resistance) genes.

Conclusion and outlook
Using NGS, one laboratory protocol can be used to generate sequencing data from samples obtained from different sources. After data analysis, information on the presence of virulence factors and antibiotic resistance genes, as well as other relevant genes are obtained. In addition, NGS makes it possible to standardize typing methods, although cut-off values regarding cgMLST, wgMLST and SNP analysis have to be established internationally in order to distinguish related or unrelated isolates and being able to compare results between laboratories. In the next few years, the role of NGS will surely increase in medical microbiology laboratories, both for research as well as for molecular diagnostic purposes, infection prevention and molecular-epidemiological investigations.
Nonetheless, improvement of the NGS workflow is still needed, focusing on easier and faster ways of library preparation, shorter run-times and further reduction in costs. Furthermore, automatic pipelines for data analyses and easy to use software have to be developed. In addition, the development of proficiency testing panels are important for external quality controls. Only with implementation of the above items at local, (inter)regional and international level will broad use of NGS be allowed in clinical microbiological laboratories for patient and infection control management, including defining a tailor-made antibiotic therapy for each patient, leading to personalized microbiology.

Acknowledgement
A full version of this work is published in the review ‘Application of next generation sequencing in clinical microbiology and infection prevention’, Journal of biotechnology 2017; 243: 16–24.

References
1. Buchan BW, Ledeboer NA. Emerging technologies for the clinical microbiology laboratory. Clin Microbiol Rev 2014; 27(4): 783–822.
2. Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, Garcia-Cobos S, Kooistra-Smid AM, Raangs EC, Rosema S, Veloo AC, Zhou K, Friedrich AW, Rossen JW. Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017; 243: 16–24.
3. Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, Ordoukhanian P. Library construction for next-generation sequencing: overviews and challenges. Biotechniques 2014; 56(2): 61–64, 6, 8, passim.
4. Zhou K, Lokate M, Deurenberg RH, Tepper M, Arends JP, Raangs EG, Lo-Ten-Foe J, Grundmann H, Rossen JW, Friedrich AW. Use of whole-genome sequencing to trace, control and characterize the regional expansion of extended-spectrum beta-lactamase producing ST15 Klebsiella pneumoniae. Sci Rep 2016; 6: 20840.
5. Junemann S, Sedlazeck FJ, Prior K, Albersmeier A, John U, Kalinowski J, Mellmann A, Goesmann A, von Haeseler A, Stoye J, Harmsen D. Updating benchtop sequencing performance comparison. Nat Biotechnol 2013; 31(4): 294–296.
6. Loman NJ, Misra RV, Dallman TJ, Constantinidou C, Gharbia SE, Wain J, Pallen MJ. Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 2012; 30(5): 434–439.
7. ECDC. Expert opinion on whole genome sequencing for public health surveillance. 2016.
8. Zhou K, Lokate M, Deurenberg RH, Arends J, Lo-Ten Foe J, Grundmann H, Rossen JW, Friedrich AW. Characterization of a CTX-M-15 producing Klebsiella pneumoniae outbreak strain assigned to a novel sequence type (1427). Front Microbiol 2015; 6: 1250.
9. Weterings V, Zhou K, Rossen JW, van Stenis D, Thewessen E, Kluytmans J, Veenemans J. An outbreak of colistin-resistant Klebsiella pneumoniae carbapenemase-producing Klebsiella pneumoniae in the Netherlands (July to December 2013), with inter-institutional spread. Eur J Clin Microbiol Infect Dis 2015; 34(8): 1647–1655.
10. Bathoorn E, Rossen JW, Lokate M, Friedrich AW, Hammerum AM. Isolation of an NDM-5-producing ST16 Klebsiella pneumoniae from a Dutch patient without travel history abroad, August 2015. Euro Surveill 2015; 20(41).

The authors
Sigrid Rosema BSc; Ruud H. Deurenberg PhD; Monica A. Chlebowicz PhD; Silvia García-Cobos PhD; Alida C. M. Veloo PhD; Alexander W. Friedrich MD, PhD; John W. A. Rossen PhD, MMM
Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, The Netherlands

*Corresponding author
E-mail: j.w.a.rossen@rug.nl

27350 Bio Data Qtr pg CLI Ad 101716 2

IN PLATELET FUNCTION TESTING

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C302 Palmer thematic new

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

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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.
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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.
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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

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