Pruthi, Rajiv K.

ABSTRACTPlatelets are small, anucleate particles derived from bone marrow megakaryocytes that circulate in the blood and are central to hemostasis, atherothrombosis, and post‐angioplasty coronary restenosis. Initially thought to involve a simple process of platelet adhesion, activation, and aggregation, recent insights reveal an important interplay with genetics and other organ systems, especially in the setting of antiplatelet drug therapy. Interaction involving the hepatic cytochrome P450 enzymes, which regulate drug metabolism, and genetic variation in those enzymes affecting drug response, underscores the liver's role in modulating platelet function by attenuating circulating active drug metabolites with eventual cardiovascular implications. Circulating adhesive proteins such as von Willebrand Factor and fibronectin also significantly regulate platelet aggregation, a process that is further influenced by varying shear conditions within the vascular system. The efficacy of antiplatelet therapies can be attenuated by such individual genetic variation, which can affect multiple cellular pathways involved not only in drug metabolism and clearance but also the drug target itself. Identifying these genetic differences allows for tailored therapy, enhancing treatment outcomes and minimizing adverse effects. Genetic variation, particularly in the CYP2C19 gene, influences individual responses to the most commonly used antiplatelet drug other than aspirin, clopidogrel, with certain variants leading to reduced drug metabolism and antiplatelet effects. Therefore, genotype‐guided antiplatelet therapy has emerged as a promising approach to reduce ischemic events and minimize bleeding events. This personalized strategy requires an understanding of genetic variation, drug‐gene interaction, and the liver's role in impacting platelet function, the coagulation system, and cardiovascular systems.

ABSTRACTIntroductionVon Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter‐rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.MethodsWe trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.ResultsThe model achieved 91% accuracy compared to Expert 1 (macro‐averaged precision = 0.851, recall = 0.757, F1‐score = 0.786) and 87% accuracy compared to Expert 2 (macro‐averaged precision = 0.653, recall = 0.653, F1‐score = 0.641). Inter‐rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.ConclusionAutomated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.

Abstract Objectives Fluorescence resonance energy transfer (FRET)–based ADAMTS13 activity assays are critical for the diagnosis of thrombotic thrombocytopenic purpura. However, these assays are susceptible to interference. As iodide has been suggested to interfere in laboratory testing via fluorophore quenching or promotion, we aimed to determine whether iodinated contrast (Omnipaque) interferes with the ATS-13 ADAMTS13 Activity Assay 2.0. Methods We evaluated the excitation, emission, and absorbance spectrum of Omnipaque alone and spiked in patient plasma with known ADAMTS13 activity and ADAMTS13 activity on Omnipaque alone, an abnormal control of patient plasma previously observed to display elevated baseline relative fluorescent units, and variable concentrations of patient plasma with known ADAMTS13 activity spiked with Omnipaque. Results No atypical fluorescent peaks were observed on any sample (Omnipaque alone or spiked in plasma) between 250 and 700 nm. There was no difference in the mean ADAMTS13 activity among the various concentrations of plasma spiked with Omnipaque or plasma spiked with saline. Conclusions Iodinated contrast does not appear to interfere—either via spectral overlap of the fluorophore or through physiologic inhibition of the ADAMTS13 enzyme—with ADAMTS13 activity FRET-based assays based on the findings from this in vitro analysis. Delaying sample collection for ADAMTS13 activity testing from suspected patients with thrombotic thrombocytopenic purpura following administration of iodinated radiocontrast agents is not necessary, and recent contrast administration should not yield erroneous ADAMTS13 activity results.

Introduction: Ticagrelor is a widely used reversible platelet adenosine 5'-diphosphate (ADP) P2Y12 receptor inhibitor used in patients with acute coronary syndrome and coronary interventions. Ticagrelor treated patients may be concomitantly exposed to heparin treatment for prophylaxis/treatment of thrombosis, and heparin induced thrombocytopenia (HIT), a thrombotic thrombocytopenic adverse event may be suspected in such patients. Objective: Here we describe the serological profile of a patient on ticagrelor treatment and strong clinical suspicion of HIT who presented with negative serotonin release assay (SRA) results. A follow up systematic study on the impact of ticagrelor on this gold standard HIT assay is presented. Methods: Remnants from 23 patient samples submitted for clinical SRA testing (5- SRA negative; 6- “low positive” [20-50%]; 5- “moderate positive” [>50 to <90%]; 7- “strong positive” [>90%]) were spiked with ticagrelor in concentrations of 100, 250, 500, and 1000 ng/mL before SRA re-testing. Testing was performed using heat inactivated serum, with low (0.1 units/mL) or high (100 units/mL) heparin and washed platelets (Leger, THSNA 2022). A positive SRA result was defined as ≥20% serotonin release with the low heparin condition and >50% inhibition of serotonin release with high concentration of heparin. A subset of SRA positive patient samples that tested falsely negative upon ticagrelor addition were subsequently incubated with activated charcoal and retested in the SRA to determine if ticagrelor interference was reversible using this methodology. Results: The index patient on ticagrelor had a high 4Ts score, ELISA optical density >1.0 but tested negative in the SRA. Re-testing of this patient with an activated charcoal treated sample demonstrated a striking change in SRA to a strong positive result (91% serotonin release). Upon testing of ticagrelor-spiked samples in the SRA negative patient cohort, as expected, the drug had no effect on results, with all 5 samples persistently staying SRA negative. Of the 6 patients that had low positive SRA results, all converted to SRA negative with ticagrelor concentrations >500 ng/mL. One of these patients became SRA negative at 100 ng/mL and 3 at 250 ng/mL. Of the 5 patients with moderately positive SRA results, 2 converted to SRA negative with ticagrelor concentrations >100 ng/mL, and 4 of 5 patients became negative with spiking at 500 ng/mL. Finally, of the 7 patients with strong positive SRA results, 1 became falsely negative in the SRA at a ticagrelor concentration of 1000 ng/mL. Activated charcoal was incubated with 6 samples that demonstrated negative SRA results with drug concentration of 500 ng/mL. Two samples reverted to SRA positive, while 4 samples remained persistently negative. Conclusion: In preliminary studies, ticagrelor has been shown to interfere with the heparin induced platelet activation (HIPA) assay but its impact on the SRA has been relatively unexplored. Here, we demonstrate that patients on ticagrelor may present with spuriously false HIT functional assay testing by the SRA, particularly if the HIT antibody is not strongly platelet activating. Activated charcoal treatment was only able to reverse drug interference in a subset of samples, even at drug concentrations below peak levels recorded in patients (approx. 800ng/mL). Given these findings, we recommend de-emphasizing the utility of the SRA in ticagrelor treated patients with reliance more on criteria such as pre-test probability of HIT (e.g. 4Ts score), and ELISA results in order to facilitate an accurate diagnosis of HIT.

Background: The term classical hematology (CH), referring to a subspecialty focusing on non-cancer blood disorders, was officially adopted by the American Society of Hematology (ASH) in 2022. There is a perceived shortage of classical hematologists, although data are limited (Winter JN, Hematologist 2022). Prior studies have shown that <6% of hematology-oncology fellows pursue a practice subspecializing in CH after training (Todd RF, Blood 2004; Marshall Al, Blood Adv 2018). Moreover, mentorship is consistently cited as the most important factor guiding fellows' career decisions (Sharma D, Blood Adv 2019; Masselink LE, Blood Adv 2019). To help bridge the CH workforce gap, ASH has supported additional hematology-focused fellowship programs (HFFP) within select hematology-oncology fellowship programs. CH constitutes a large proportion of consults in US National Cancer Institute (NCI)-designated cancer centers (West CV, JCO Oncol Pract 2024). The goals of our study were: 1) to determine the proportion of adult hematologist-oncologists within NCI cancer centers who practice CH; and 2) to describe the physician characteristics of cancer centers with HFFP and compare these centers to those without HFFP. Methods: We included all NCI-designated cancer centers with a clinical program as of April 1, 2024. The only exception was St Jude Children's Hospital due to its pediatric focus. We obtained the practice profile of each internal medicine-trained adult hematologist-oncologist (henceforth, physician) as well as the availability of HFFP from the respective cancer center websites. We also searched the corresponding department of medicine and division of hematology at each affiliated academic institution to ensure inclusion of all physicians practicing CH. Each physician was classified into one of the following categories: classical hematologist (attending to only non-cancer conditions), general hematologist-oncologist (cancer and non-cancer conditions), and oncologist (cancer only). The number of hematology-oncology fellowship programs in the US was obtained from the Accreditation Council for Graduate Medical Education. Results: We included 64 NCI-designated cancer centers, 55 of which were considered as comprehensive cancer centers. There was a total of 4,616 physicians (3,891 oncologists [84.3%], 487 hematologists-oncologists [10.6%], and 238 classical hematologists [5.1%]). The median numbers of physicians, oncologists, general hematologist-oncologists, and classical hematologists per center were 55 (range, 16-275), 45 (range, 12-270), 5 (range, 0-48), and 3 (range, 0-13), respectively. The median proportions of classical hematologists and physicians practicing CH (general hematologist-oncologists plus classical hematologists) were 5.1% (range, 0-20.0) and 14.9% (range, 0-55.6%), respectively. There were 12 HFFP within NCI-designated cancer centers (18.8%). Most (11/12) centers with HFFP had >50 physicians (median 95 [range, 38-185]) and >5% classical hematologists (median 6% [range, 3.8-13.5%]); 11 centers with similar characteristics did not have HFFP. Centers with HFFP have significantly higher numbers of physicians (median 95.0 [range, 38-185] vs 49.5 [range, 16-275]; P=0.001) and classical hematologists (median 6.0 [range, 3-13] vs 2.0 [range, 0-11]; P<0.001) but similar proportions of classical hematologists (median 6.0% [range, 3.8-13.5] vs 4.5% [range, 0-20.0]; P=0.078) and physicians practicing CH (median 12.3% [range, 6.5-47.7] vs 15.7% [range, 0-55.6]; P=0.402) compared to those without HFFP. Conclusions: Among adult hematologist-oncologists at NCI-designated cancer centers, only 1 in 20 are classical hematologists, although 1 in 7 have CH as part of their practice. Within NCI-designated cancer centers, only 1 in 5 have an HFFP. These findings suggest that there is an opportunity for NCI-designated cancer centers to encourage and recruit physicians subspecializing in CH and a rationale to create additional HFFPs. These measures may help further alleviate CH workforce shortages and expand CH mentorship to meet the needs of an increasingly complex subspecialty within hematology.

Introduction Assessing bleeding symptoms is essential for diagnosing bleeding disorders. Bleeding assessment tools (BATs) were developed to standardize the collection of bleeding symptoms and their severity, thereby enhancing the diagnostic accuracy. However, their diagnostic precision across various inherited bleeding disorders remains unclear. Therefore, we conducted a systematic literature and meta-analysis to evaluate the diagnostic accuracy of BATs in pediatric and adult populations. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of BATs in pediatric and adult populations. Objective: To evaluate the diagnostic effectiveness of BATs in identifying laboratory-confirmed inherited bleeding disorders. Methods Literature Search Strategy: A comprehensive systematic literature search was conducted across Scopus, Embase, Web of Science, MEDLINE, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials, and the World Health Organization databases from inception until January 27, 2024 Study Selection: Two independent reviewers screened titles and abstracts using Covidence (Covidence systematic review software, Veritas Health Innovation). Data extraction was performed independently by two authors reviewing and collecting data in duplicate from all selected articles. Eligibility Criteria: Included studies reported on the diagnostic accuracy of any BAT in diagnosing inherited bleeding disorders. Exclusion criteria included abstracts, review articles, case reports/series, studies focusing solely on distinguishing specific bleeding disorders, and those lacking sufficient data to assess test accuracy. Quality Assessment: Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Two authors assessed each article independently and each study was categorized as having a low, moderate, or high risk of bias. Results Of 7,009 studies identified, 31 met the inclusion criteria, comprising 6,962 patients (2,152 with bleeding disorders, 4,810 controls). Most studies (74%) were prospective. Studies assessed various bleeding disorders: 39% all types, 32% von Willebrand Disease (VWD), 10% Inherited Platelet Function Disorder (IPFD), 10% both VWD and IPFD, and 10% other rare disorders. The ISTH-BAT was the most frequently used tool (47%), followed by MCMDM-1 VWD (21%) and PBQ (18%). The pooled prevalence of confirmed bleeding disorder in all prospective studies was 32% (range, 3%-71%). The pooled sensitivity for BAT in all included studies (31 studies) was 82% (95% CI, 76.5%-86.7%) and the specificity was 66% (95% CI, 54%-76%). Subgroup analysis revealed that the ISTH-BAT (studies: BD 6, VWD 5, IPFD 3, GT/BSS 2), had a sensitivity of 77% (95% CI, 69%-84%) and a specificity of 70% (95% CI, 58%-80%), the MCMDM-1 VWD (studies: VWD 4, BD 2, GT/BSS 1) had sensitivity of 78% (95% CI, 61%-89%) and a specificity of 79% (95% CI, 54%-92%) , and the Pediatric Bleeding Questionnaire (studies: BD 3, VWD 2, VWD/IPFD 1) had a sensitivity of 93% (95% CI, 85%-97%) and a specificity of 73% (95% CI, 41%-92%) . The sensitivity and specificity of BATs for studies investigating all types of bleeding disorders (12 studies) soley VWD (10 studies), and soley IPFD (3 studies) were 80% (95% CI, 67%-88%) and 50% (95% CI, 34%-67%) , 79% (95% CI, 71%-86%) and 83% (95% CI, 59%-95%), and 85% (95% CI, 79%-89%) and 74% (95% CI, 56%-86%), respectively. Significant heterogeneity was observed across studies in terms of populations, BATs used, and diagnostic methods. This heterogeneity limits the generalizability of the results and suggests that BAT performance may vary depending on the specific clinical context and patient population. Additionally, 26% of studies were classified as having a high risk of bias, potentially affecting the reliability of some findings. Conclusion Despite heterogeneity, this meta-analysis supports the use of BATs as valuable screening tools for inherited bleeding disorders, demonstrating generally high sensitivity across various patient populations. However, the variability in specificity across different disorders and tools underscores the need for careful selection and interpretation of BATs in clinical practice. Further standardization of BATs and diagnostic criteria could improve their utility in diverse clinical settings.

Introduction. Platelet-activating anti-Platelet factor 4 (PF4) antibodies mediate a number of thrombotic thrombocytopenic syndromes, including heparin-induced thrombocytopenia (HIT), and more recently recognized conditions, vaccine-induced immune thrombotic thrombocytopenia (VITT), adenovirus-associated thrombotic thrombocytopenia, and monoclonal gammopathy of thrombotic significance (MGTS). In MGTS (Blood. 2023 Apr 6;141(14):1772-1776), a monoclonal IgG antibody to PF4 mediates a chronic prothrombotic state characterized by recurrent thrombosis. More recent evidence suggests that MGTS may present with negative results in HIT enzyme-linked immunosorbent assay (ELISA) and serotonin release assay (SRA), and positivity only in PF4-dependent functional assays such as the PF4-dependent P-selectin expression assay (PEA; submitted). The presence of MGUS in patients has long been associated with high risks of thrombotic disease (Blood. 2010 Jun 17;115(24):4991-8), but the mechanism by which they predispose to thrombosis has been relatively unexplored. In this study, we evaluated a cohort of MGUS patients who experience thrombosis for the presence of platelet-activating anti-PF4 antibodies. M ethods. An administrative database search was performed of Mayo Clinic records to identify patients who had a diagnosis of MGUS, and thrombosis. Research samples from 134 MGUS patients with IgG monoclonal protein and thrombosis were obtained from Mayo Clinic's dysproteinemia biobank and subjected to PF4 antigen-based and functional screening. Research studies were approved by the Institutional Review Board of Mayo Clinic. Results. Of the 134 patients, 17 (12.7%) activated platelets in the PEA (≥19%). To exclude non-specific activation (e.g. due to HLA antibodies), secondary PEA testing was performed to assess whether platelet activation was mediated by a HIT-like antibody (i.e., PF4- and IgG-dependent, and inhibitable with high concentrations of heparin). Four patients, hereafter referred to as patients 1-4, were identified as having a HIT-like antibody, as demonstrated by PF4-dependent platelet activation that could be inhibited with high concentrations of heparin (100 U/mL), and blockade of the platelet IgG receptor, FcγRIIa. Patients 1 and 2 had persistent, platelet-activating anti-PF4 antibodies based on PEA testing of multiple samples obtained several years apart. Patient 1 had a recurrent, multi-year history of deep venous thrombosis (DVT), while patient 2 had one DVT episode. Patient 3 demonstrated a HIT antibody-like profile in the PEA in two of three samples available for testing and had a history of thrombotic cerebrovascular accident (CVA). Patient 4 demonstrated a HIT-like platelet-activating antibody in only one of four samples tested, and consistent with this finding, had an MGUS identified on clinical testing at only one time point (“small” IgG lambda, not quantified), 3 months prior to the platelet-activating sample. This patient experienced multiple thrombotic events including DVTs, left atrial thrombus and embolic CVA, pulmonary embolism and acute limb ischemia. All patients tested negative in two FDA-approved antigen-based HIT assays tested (Immucor PF4 IgG and Zymutest HIA IgG), similar to a recent MGTS case (submitted). SRA was negative in both Patients 1 & 2, weakly positive in Patient 3 (33%, with high CV), and positive (72%) in Patient 4. Conclusions. Definitive diagnosis of patients with MGTS provides insight into a mechanism of “unexplained” thrombosis and may offer additional treatment pathways for these patients with recurrent thrombosis (New Eng J of Med, 2024 Aug 8). Serologic testing in two patients correlated strongly with observations of thrombosis, and the study confirmed recent findings that anti-PF4 antibodies may present with unusual serology that is neither HIT- nor VITT-like (with negative results in both ELISA and SRA). The incidence of confirmed/suspected MGTS was 3% (4/134) in this population of MGUS patients with thrombosis. Prospective studies are needed to confirm these observations.

Introduction: Laboratory identification of a lupus anticoagulant (LAC) requires evidence of: a) prolongation of a phospholipid-dependent screening test, usually a LAC-responsive dilute Russell's viper venom time (DRVVT) or activated partial thromboplastin time (APTT); b) inhibition on mixing with normal pooled plasma (NPP); c) confirmation of phospholipid-dependent inhibition by repeating the prolonged test with excess phospholipid. Despite decades of assay development and re-design and numerous attempts at standardization, diagnosis of a LAC remains challenging due to the heterogeneity of antiphospholipid antibodies, the marked variations in reagents, inconsistencies in post-analytic processes, and not least, the multitude of interferences, such as anticoagulant drugs, which influence test results. Guidelines recommend interruption of anticoagulation, if possible, prior to LAC testing to prevent obtaining false test results. While this is attempted in some instances for direct-acting anticoagulants, it is typically not considered for warfarin, and it is also not always possible. Our laboratory has observed a concerning pattern of potentially false-negative DRVVT screen/ confirm ratios in samples collected from patients on warfarin anticoagulation or with a factor deficiency (e.g. hypoprothrombinemia). The goal of this study is to replicate this observation using in vitro dilution experiments, mixing LAC-positive samples with either NPP or warfarin plasma. Methods: DRVVT Screen, Mix and Confirm assays (Precision Biologic, Dartmouth, NS) were performed using archived LAC-positive plasmas from 5 unique patients not on anticoagulation, and titered with normal pooled plasma (NPP) (Precision Biologic, Dartmouth, NS) to a screen/confirm ratio of 1.20-1.35, near the assay cutoff for DRVVTC (<1.20) and analyzed on the ACL TOP 750 (Werfen, Bedford, MA). Warfarin donor plasma was then mixed in the same dilution scheme with each of the LAC-positive plasmas so that the final dilution factors for LAC-NPP and LAC-Warfarin contrived samples were the same. The DRVVT screen/ confirm ratios of the LAC-NPP and LAC-Warfarin plasma contrived samples were compared. Additionally, a DRVVT Mix Screen and Mix Confirm were performed after mixing in a 1:1 ratio with NPP. Results: The average prothrombin time (PT) in the LAC-Warfarin samples was significantly longer than the LAC-NPP samples (20.7±8.5s vs 10.6±0.2s, p=0.03). All 5 LAC-NPP contrived samples had a positive DRVVT screen/ confirm ratio >1.20, while all of the LAC-Warfarin contrived samples had a negative DRVVT screen/ confirm ratio (1.26±0.04 vs 1.02±0.11, p<0.01). When the DRVVT screening time was divided by the DRVVT Mix Confirm time, 4 of the 5 LAC-Warfarin contrived samples reverted to positive using a cutoff of 1.20 (values of 1.30, 1.21, 1.21, 1.02 and 1.23 for LAC-Warfarin samples) and a similar pattern was observed with the LAC-NPP samples (values of 1.32, 1.29, 1.27, 1.14, 1.27, respectively, for LAC-NPP samples). Conclusion: This study confirms our anecdotal observation that warfarin and/ or factor deficiencies in the common pathway can result in a false-negative DRVVT screen/ confirm ratio using the Precision Biologic LA Sure and LA Check reagents, similar to findings described for apixaban. The DRVVT assay is widely considered to be the most sensitive or responsive to LAC and this in vitro observation raises questions about whether negative DRVVT screen/ confirm ratios can reliably be used to exclude the presence of a LAC in patients on warfarin using this reagent system. Larger studies are required using pre-treatment and on-treatment paired specimens to confirm these findings, and to further explore the utility of the DRVVT Screen/Mix Confirm ratio in this patient population.

Introduction Establishment of reference intervals (RI) for quantitative assays requires a minimum of 120 qualified normal donor (ND) samples for each partition of an analyte. Based on CLSI EP28 A3C guideline [CLSI. Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline-Third Edition. CLSI document EP28-A3c. Wayne, PA: Clinical and Laboratory Standards Institute; 2008.], verification of previously established and/or published RI can be performed with a minimum of 20 NDs, especially given complexities of finding qualified NDs. Reagent manufacturers typically provide adult RI, thus facilitating RI verification as a viable option. NDs for the Pediatric (Peds) (<18 years of age) population are difficult to obtain, published RIs are performed with sample cohorts less than the suggested number or only performed with specific instrument/reagent combinations. Herein, we report our experiences with Peds RI. Methods After IRB approval, healthy pediatric outpatients ages 0-18yrs were recruited and consented. Plasma obtained from umbilical cord, finger/heel/ear stick, or venipuncture blood samples (no more than 50 mL or 3 mL per kg) and collected in 3.2% sodium citrate. Assays included Protein (P) C antigen (PCAG) and chromogenic activity (PCFX); P S total (PST), free (PSF) antigen, and functional (SFX); antithrombin antigen (ATAG) and chromogenic activity (ATFX); plasminogen (PLGN) and alpha-2-plasmin inhibitor (A2PI). Assay kits were PCFX - HemosIL® (Werfen, Beford, MA) Protein C, PST - LIATEST® (Diagnostica Stago S.A.S., Asnières sur Seine, France) Protein S, PSF - HemosIL® Free Protein S, SFX - HemosIL® Protein S Activity, ATAG - LIATEST® AT III , ATFX - HemosIL® Liquid Antithrombin, PLGN - HemosIL® Plasminogen, and A2PI - HemosIL® Plasmin Inhibitor performed on the ACL TOP 700 (Werfen) instrument. PCAG - REAADS® Protein C Antigen Test Kit (Corgenix, Inc. Broomfield, CO) was performed on the Janus G3 liquid handler (Revvity, Waltham, MA) and Synergy Neo2 microplate reader (Agilent, Santa Clara, CA). Assay mean and range were calculated. Results Insufficient numbers of patients <1yr were collected. Type of assay, age range in yr and number of samples (n), assay mean in % and range results respectively were as follows: PCAG 1-5 (n=15) 87(46-146); 5-10 (n=82) 81 (45-158); 10-15 (n=169) 84 (44-157); 15-18 (n=107) 91 (57-149). PCFX 1-5 (n=20) 97 (56-140); 5-10 (n=84) 89 (62-135); 10-15 (n=188) 91 (55-140); 15-18 (n=124) 96 (54-138). PST (males): 1-5 (n=10) 120 (96-145); 5-10 (n=46) 108 (75-150); 10-15 (n=91) 111 (80-168); 15-18 (n=63) 113 (72-184). PST (females): 1-5 (n=9) 104 (67-146); 5-10 (n=37) 111 (81-159); Ages 10-15 (n=94) 107 (78-153); 15-18 (n=63) 101 (65-148). PSF (males): 1-5 (n=10) 94 (74-109); 5-10 (n=47) 101 (66-141); 10-15 (n=93) 101 (69-151); 15-18 (n=62) 111 (74-187). PSF (females): 1-5 (n=9) 78 (9-124); 5-10 (n=37) 107 (79-137); 10-15 (n=95) 99 (63-138); 15-18 (n=63) 97 (60-132). SFX (males): 1-5 (n=9) 100 (85-128); 5-10 (n=48) 104 (66-144); 10-15 (n=93) 103 (66-168); 15-18 (n=59) 109 (72-157). SFX (females): 1-5 (n=8) 94 (18-121); 5-10 (n=37) 107 (81-143); 10-15 (n=90) 101 (65-147); 15-18 (n=60) 97 (53-135). ATAG 1-5 (n=19) 104 (80-121); 5-10 (n=83) 104 (78-121); 10-15 (n=188) 104 (66-126); 15-18 (n=126) 101 (67-126). ATFX 1-5 (n=20) 112 (94-135); 5-10 (n=84) 107 (87-123); 10-15 (n=188) 105 (84-138); 15-18 (n=126) 101 (84-129). PLGN 1-5 (n=17) 96 (76-124); 5-10 (n=85) 88 (65-125); 10-15 (n=183) 90 (49-151); 15-18 (n=119) 94 (64-151). A2PI: 1-5 (n=17) 134 (118-151); 5-10 (n=85) 126 (107-139); 10-15 (n=183) 121 (104-140); 15-18 (n=118) 116 (91-143). Conclusions Based on CLSI EP28 A3C criteria for verification, no more than 10% of results should fall outside of established/published RI, for example adult RI. Thus, compared to the SCL adult RI, PLGN assay meets criteria in all age ranges and no additional ND are needed. For the remaining assays, additional ND for ages 1-5 are needed. For ages 5-18 criteria for verification are satisfied for all analytes except PCAG. PCAG has 27, 61, and 19 patients falling below lower RI for ages 5-10, 10-15, and 15-18, respectively.