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Background: In infants and children, postoperative respiratory complications are leading causes of perioperative morbidity, mortality, and increased healthcare utilisation. We aimed to develop a novel score for prediction of postoperative respiratory complications in paediatric patients (SPORC for children)., Methods: We analysed data from paediatric patients (≤12 yr) undergoing surgery in New York and Boston, USA for score development and external validation. The primary outcome was postoperative respiratory complications within 30 days after surgery, defined as respiratory infection, respiratory failure, aspiration pneumonitis, pneumothorax, pleural effusion, bronchospasm, laryngospasm, and reintubation. Data from Children's Hospital at Montefiore were used to create the score by stepwise backwards elimination using multivariate logistic regression. External validation was conducted using a separate cohort of children who underwent surgery at Massachusetts General Hospital for Children., Results: The study included data from children undergoing 32,187‬ surgical procedures, where 768 (2.4%) children experienced postoperative respiratory complications. The final score consisted of 11 predictors, and showed discriminatory ability in development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.85 (95% confidence interval: 0.83-0.87), 0.84 (0.80-0.87), and 0.83 (0.80-0.86), respectively., Conclusion: SPORC is a novel validated score for predicting the likelihood of postoperative respiratory complications in children that can be used to predict postoperative respiratory complications in infants and children., (Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Background: An increasing number of wearable medical devices are being used for personal monitoring and professional health care purposes. These mobile health devices collect a variety of biometric and health data but do not routinely connect to a patient's electronic health record (EHR) or electronic medical record (EMR) for access by a patient's health care team., Methods: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MHBLM) developed consensus recommendations for consideration when interfacing mobile health devices to an EHR/EMR., Results: IFCC C-MHBLM recommendations cover personalized monitoring and privacy concerns, data security, quality assurance of data transfer, and incorporation of alert triggers to warn users of important health conditions., Conclusions: Considerations for interface ease-of-use, display of patient data in the EHR/EMR, and needs-based training programs for healthcare staff to understand the critical requirements, proper use, and integration of mobile health devices with EHR/EMRs are provided. Cooperation between healthcare providers, device manufacturers, and software developers is also recommended to drive future innovation in mobile health device technology development., (Copyright © 2024 International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). All rights reserved.)

Background: Preanalytical error due to hemolyzed blood samples is a common challenge in laboratory and point-of-care (POC) settings. Whole blood potassium (K+) measurements routinely measured on blood gas analyzers are particularly susceptible to hemolysis, which poses a risk for incorrect K+ results. The GEM Premier 7000 with IQM3 (GEM 7000) blood gas analyzer provides novel integrated hemolysis detection within the sample measurement process. Therefore, the GEM 7000 can detect and flag hemolyzed whole blood samples at the POC, warning the operator of potentially erroneous results., Methods: Heparinized venous or arterial whole blood samples were used for K+ interference studies and assessed for hemolysis agreement utilizing either a traditional volumetric method or chemistry analyzer serum index measurements with the Roche cobas c311 or Abbott Alinity c., Results: Hemolysis interference studies performed at 2 different K+ concentrations (3.8 and 5.3 mmol/L) identified that a plasma free hemoglobin ≥116 mg/dL can impact K+ results on the GEM 7000. Hemolysis agreement studies demonstrated an excellent agreement of >99% with the volumetric method, 98.8% with cobas H index, and 96.4% with Alinity H index. GEM 7000 K+ results were correctly flagged for both native and spiked samples., Conclusion: GEM 7000 hemolysis detection provides a novel technology to detect hemolysis in whole blood samples. Moreover, the GEM 7000 demonstrates excellent agreement with traditional laboratory hemolysis detection methods and offers an integrated technological solution for assuring the quality of whole blood K+ results in POC settings., (© Association for Diagnostics & Laboratory Medicine 2024.)

Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems. This standardization should include the comparator device, sample origin, and study procedures. With better aligned CGM readings, CGM-derived metrics will subsequently also align better between systems., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: SP and ME are employees of Institute for Diabetes Technology (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm; IfDT), Ulm, Germany.EEB has no disclosures.MF received lecture fees from Menarini.RH—Independent medical & scientific consultant, former employee of Roche Diabetes Care.JJ has been a lecturer/member of the scientific advisory boards at the following companies: Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Medtronic, Nordic InfuCare, Novo Nordisk, and Sanofi.DCK is a consultant for Afon, Better Therapeutics, Integrity, Lifecare, Nevro, Novo, Samsung, and Thirdwayv. KM has nothing to disclose.JHN has received research support from Abbott.JP—Advisory panel for ROCHE Diabetes Care and Abbott; speaker fees from Insulet and Dexcom.ES is supported by grants from the US National Institutes of Health (NIH) and has received donated materials related to NIH-supported research from Abbott Diabetes Care, Roche Diagnostics, Siemens Diagnostics, Ortho Clinical Diagnostics, Abbott Diagnostics, Asahi Kasei Pharma Corp, GlycoMark Corp.RJS is chair of the Clinical Chemistry Department of Isala which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. RJS has received speaker’s honoraria or consulting fees in the last 3 years from Roche and Menarini.AT is a freelance consultant. He has received fees for lectures or consultancy fees from Abbott, Berlin Chemie, Dexcom, Evivamed, Menarini, Novo Nordisk, Roche and Sanofi in the last 3 years.NKT is a consultant for Roche Diagnostics and Roche Molecular Systems, received honoraria from Nova Biomedical and Thermo Fisher, and Chair (2024-2026), Critical and Point-of-Care Testing (CPOCT) Division, Association for Diagnostics and Laboratory Medicine (ADLM).LW has nothing to disclose.GF is general manager and medical director of the IfDT, which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. GF/IfDT have received speakers’ honoraria or consulting fees in the last 3 years Abbott, Berlin Chemie, Boydsense, Dexcom, Glucoset, i-SENS, Lilly, Menarini, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, Ypsomed.

Introduction: Faster turnaround times can lead to rapid patient treatment. Implementing a point-of-care (POC) molecular COVID-19 test requires careful planning. In the POC setting, there are numerous operators and regular monitoring of their activities is key to the successful implementation of a POC molecular test. Test errors can arise from samples, operators, reagents, the testing system, and even from the environment. These sources of error should be considered when implementing a new test., Areas Covered: We outline the importance of establishing well-defined policies for staff to follow at the preanalytic, analytic and postanalytic phases of SARS-CoV-2 testing. As these factors are crucial for the accuracy and reliability of the test results. The key discussion points are from the CLSI EP23-Ed2 document on developing individualized quality control plans and medical literature search engines such as EMBASE, MEDLINE and MedlinePlus., Expert Opinion: The risk management principles applied when implementing nucleic acid POC tests can identify specific control processes to help mitigate common sources of error when conducting molecular testing at the POC.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs)., Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy., Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose., Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D.C.K. is a consultant for Afon, Lifecare, Novo, Samsung, embecta, Glucotrack, SynchNeuro, and Thirdwayv. G.F. is CEO of IfDT. G.F./IfDT has/have received research support, speakers’ honoraria or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Glucoset, i-SENS, Lilly, Menarini, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, Ypsomed. S.P. is an employee of IfDT. B.P.K. declares patent royalties handled by UVA from: DexCom, J&J, Novo Nordisk, and Sanofi; and research support handled by UVA: Dexcom, Novo Nordisk, Tandem Diabetes Care. D.K. has received research support from Abbott Diabetes Care. M.S.D.A. is currently receiving in kind support (CGM devices) from Dexcom Inc for an investigator-initiated clinical study. K.D. receives research funding from Dexcom, Abbott, Sanofi, Viacyte, Insulet, consulting fees from Eli Lilly, Dexcom, Insulet, Oppenheimer, Elsevier, honoraria from Academy for Continued Healthcare Learning, Med Learning Group, Medscape, Impact Education, and royalties from UptoDate. S.M. is in receipt of an investigator-initiated grant from DexCom, has received speaker fees from Sanofi & Lilly, is funded by a Wellcome Trust (223024/Z/21/Z), and is supported by the NIHR Imperial Biomedical Research Centre. A.S. was supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). V.N.S. reports receiving personal fees from Sanofi, Embecta, NovoNordisk, Dexcom, Insulet, Tandem Diabetes Care, Ascensia Diabetes Care, Genomelink and LumosFit for consulting, advising or speaking. N.K.T. is a consultant for Roche Diagnostics / Roche Molecular Systems / Radiometer. UC Davis is a Roche Diagnostics Center of Excellence. N.K.T. has received speaking honoraria for Roche Diagnostics, Nova Biomedical, Thermo Fisher, and Radiometer. N.K.T. is also Chair-Elect for the Association for Diagnostic and Laboratory Medicine (ADLM) Critical and Point-of-Care Testing Division, Co-Founder of the Machine Intelligene Learning Optimizer (MILO), Inc, and a member of the International Federation for Clinical Chemistry (IFCC) continuous glucose monitoring workgroup. T.T. is a consultant for Clinical ink. R.E.A. is a consultant for Clinical ink. D.T.A. has received speaker’s honoraria from Abbott, Ascensia Diabetes Care, Insulet, Lilly Diabetes, Mannkind, Novo Nordisk, and Xeris Pharmaceuticals. D.T.A. has received consulting fees from Ascensia Diabetes Care, Lilly Diabetes, and Senseonics. H.K.A. received research grants through University of Colorado from Dexcom, Tandem Diabetes, Medtronic, Mannkind, IM Therapeutics, IAFMS, and received honorarium through University of Colorado for consulting from Dexcom, Tandem Diabetes, and Medtronic. M.E.A.-S. has served on the advisory boards for Abbott, Medtronic, Insulet, VitalAire, Sanofi, Eli Lilly, and Dexcom; has received honoraria for speaking from Abbott, Eli Lilly, Medtronic, Novo Nordisk, Sanofi, VitalAire, and Eli Lilly; and received research support from Medtronic and Sanofi. T.S.B. has received research support from Abbott Rapid Diagnostics, Biolinq, Dexcom, Eli Lilly, Medtronic, Medtrum, Novo Nordisk, Sanofi, Senseonics, and vTv Therapeutics and consulting honoraria from Abbott Diabetes, Abbott Rapid Diagnostics, ACON, CeQur, HagarTech, Intuity Medical, Lifescan, Mannkind, Medtronic, Novo, Perspirion, Sanofi, Sequel Med Tech, and Ypsomed. M.B. currently participates on the Hospital Advisory board with Dexcom and has received research support from Dexcom. L.B. has received research/product support from Dexcom, Ypsomed, and Boehringer Ingelheim; speaker honoraria from Dexcom and Oviva; and participated in advisory boards of Dexcom, Novo Nordisk, Sanofi, Ypsomed, Oviva, and Roche Diabetes Care. Y.M.B. has received honoraria for lectures and scientific advisory from Roche and AstraZeneca in the past 12 months. J.E.B. is on the Speaker’s bureau for Insulet and on the Advisory Board for Cardinal Health and Lifescan. J.E.B. is a consultant for Embecta. J.E.B. receives research support from the Leona M. and Harry B. Helmsley Charitable Trust. E.C. is on the scientific advisory board for Novo Nordisk, Eli-Lilly, Adocia, Arecor, Proventionbio, Portal Insulin, and MannKind. D.C. was a Dexcom employee from August 2018 to May 2024. D.C. is a shareholder of Dexcom, Lilly, Abbott and Tandem. M.C. has received consulting fees from Glooko and research support from Dexcom and Abbott Diabetes Care. G.L.C. is a shareholder of BioTex, Inc., Basepair Biotechnologies Inc., Coordination Centric, Inc., and Shape Memory Medical, Inc. G.L.C. is supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). K.K.D. has received honoraria, travel, or fees for advisory boards from AstraZeneca, Novo Nordisk, Boehringer Ingelheim, Eli Lilly, Abbott Diabetes, Menarini, and Sanofi in the last 12 months. J.E. receives federal funding from FDA, NIMHD, and NCATS and is a consultant for Sanofi. C.F. receives royalties from Dexcom and Novo Nordisk managed through her institution. G.A.F. is an advisor for 180 Life Sciences, 89bio, Adocia, Abbott, AdipoPharma, Aerami, Amolyt, Carthera, Catalyst, Cohen Global, CMC Magnetics, Diasome, Eleos, Entera Bio, Enterin, Glyscend, Hagar, Hogan Lovells, IM Therapeutics, Innoneo, Intarcia, Levicure, Lilly Asia Ventures, Lumos, Mars Symbioscience, Melior, metaLead Therapeutics, Microbiotix, MMD, Modular Medical, New Amsterdam, Northwestern, NuSirt, NuVox, Oramed, Pano, Pasithea, PhylloPharma, Pleiogenix, Recordati, Regor, Remodeless, Renaissance, RenovoRx, Rivus, RxMP, Sera Biopharma, Seraxis, Serpin, SFC Fluidics, Skinject, SROne, Stalicla, Surf Bio, TIXiMED, Veroscience, Verthermia, WaveBreak, Zealand Pharma, and Zucara. M.A.L.G. is a consultant for Abbott, Medtronic, Novonordisk, and Roche. R.J.G. received research support from Novo Nordisk, Dexcom and Eli Lilly and consulting fees/advisory fees from Abbott Diabetes Care, Aztra Zeneca, Bayer, Boehringer, Dexcom, Eli Lilly, Novo Nordisk, and Medtronic, outside of this work. R.J.G. is partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Numbers P30DK111024, K23DK123384, R03DK138255, and U2CD137135. A.M.G.-M. reports speaker fees from Novo Nordisk, Sanofi, Elli Lilly, Boehringer Ingelheim, Abbott, and Medtronic. L.H. is a consultant for a number of companies that are developing innovative solutions for the treatment of people with diabetes. N.H. reports Advisory Board member fees from Abbott Diabetes Care and Insulet as well as honoraria for lectures from Berlin-Chemie AG, Becton Dickenson, Sanofi Germany, Roche Diabetes Care, and Dexcom Germany. T.H. serves on the advisory board for Acella and Horizons Therapeutics (no financial compensation). S.H. has served on the advisory board for Tandem, Dexcom, and Medtronic, and received honoraria for nonpromotional educational and/or consultancy work from Abbott, Insulet, Dexcom, Roche, and Sanofi. P.G.J. is a shareholder of Pacific Diabetes Technologies Inc. and serves on the advisory board for Eli Lilly. P.G.J. received grant support from SFC Fluidics and research support from Eli Lilly and Dexcom. J.J. has received fees from lectures and or advisory boards from Abbott, Astra Zeneca, Boeringer Ingelheim, Eli Lilly, Medtronic, Nordic Infucare, Novo Nordisk, and Sanofi. S.R.J is a consultant for USV, Marico, Glenmark, Franco Indian, Twin Health, Biocon, and Zydus Lifesciences. S.R.J. received speaking honoraria from Abbott, Novo Nordisk, MSD, Sanofi, Boehringer Ingelheim, AstraZeneca, Lupin, Bayer Zydus, USV, DRL, Meyer Organics, Servier, Natco. R.A.L. is a consultant for Abbott Diabetes Care, Biolinq, Capillary Biomedical, Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, and Tidepool. R.A.L. has served on advisory boards for. ProventionBio and Lilly. R.A.L. receives research support through his institution from Insulet, Medtronic, Tandem, and Sinocare. L.A.L. has received research funding from, has provided CME on behalf of, and/or has acted as an advisor to Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Lexicon, Merck, Novo Nordisk, Pfizer, and Sanofi. M.L. has been a consultant or received honoraria from Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Nordic InfuCare, and Novo Nordisk, and has received research grants from Eli Lilly and Novo Nordisk, all outside of the submitted work. J.K.M. is a member of advisory boards of Abbott Diabetes Care, Becton-Dickinson, Biomea Fusion, Eli Lilly, Embecta, Medtronic, NovoNordisk A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris and received speaker honoraria from A. Menarini Diagnostics, Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Medtrust, MSD, NovoNordisk A/S, Roche Diabetes Care, Sanofi, Viatris, and Ypsomed. J.K.M. is a shareholder of decide Clinical Software GmbH and elyte Diagnostics. A.M. reports speaker fees from Novo Nordisk. M.M. has an existing research & development relationship with Scientific Bioprocessing, Inc and has been a consultant for Abbott. S.-J.M. is a consultant for Abbott, Curestream, Daewoong, EOFlow, G2e, Huons, iSense, Medtronic, Novo Nordisk, and Sanofi. D.N.O. has served on advisory boards for Abbott Laboratories, Medtronic, Merck Sharp & Dohme, Novo Nordisk, Roche, and Sanofi; received research support from Medtronic, Novo Nordisk, Roche, Eli Lilly and Company, and Sanofi; and received travel support from Novo Nordisk and Merck Sharp & Dohme. F.J.P. has received research support (to Emory University) from Dexcom, Insulet, Novo Nordisk, Tandem, and Ideal Medical Technologies, and has received consulting fees from Dexcom. A.L.P. is on the advisory board of Medscape, Vertex, and Lilly; receives research support from Insulin and Abbott; and has stock options in Omada Health. R.P.-B. receives grant support from Novo Nordisk, Lexicon Pharmaceuticals, and Medtronic and received consulting fees from Bayer, Lexicon Pharmaceuticals, Novo Nordisk, and Roche. C.M.R. has received honoraria and/or grant support from AstraZeneca, Dexcom, Fresenius, and Vifor. D.B.S. is supported by the Intramural Research Program of the National Institutes of Health. S.S. was a Novo Nordisk employee from 2022-2023. S.S. has served as advisor for Novo Nordisk and has received speaker fees from Novo Nordisk and Nordic Infucare within the past three years. G.D.S’s employer, nonprofit International Diabetes Center, HealthPartners Institute, has received educational grant funds from Abbott Diabetes Care, Medscape, and Sanofi-Aventis Groupe. G.D.S receives no personal income/honorarium from these activities. K.S. received grant supports from Arkray, Terumo, and Dexcom. E.K.S. is partly supported by the VA Merit Award (1I01CX001825) from the US Department of Veterans Affairs Clinical Sciences Research and has received unrestricted research support from Dexcom and Tandem Diabetes (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. N.L.S. received funding from Novo Nordisk for an investigator-initiated research grant unrelated to the current project. G.E.U. is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). G.E.U. has received research support (to Emory University) from Abbott, Bayer, and Dexcom; and has participated in advisory boards for Dexcom and GlyCare. E.W. has received consulting fees from Abbott Diabetes Care, Ascensia, Bayer, Boehringer, Ingelheim, Embecta, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. has received honoraria from Abbott Diabetes Care, Bayer, Boehringer Ingelheim, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. is on the Speakers’ Bureau from Abbott Diabetes Care, Bayer, Boehringer, Ingelheim, GlaxoSmithKline, Lilly, Renalytix, and Sanofi. M.Z. is a consultant for DexCom, Inc. M.A.K. is Chief Medical Officer of QuesGen, Inc. C.T., C.L., B.V.H., J.S.K., W.A.L., S.Y.R., J.J.S., K.W., C.W., K.R., C.N.H., A.T.A., A.A., R.R.B., E.M.B., J.G.C., K.Y.C., A.D., N.E., S.K.K., U.M., N.M., C.M., J.H.N., A.P., P.R., S.M.S., B.S., M.V., H.W.V., J.W., A.H.B.W., and S.Y. have nothing to disclose.

Background: A new Rapid Drug Test Device (RDTD) is available for analysis of urine fentanyl. With the rise in fentanyl abuse in the United States, we evaluated the analytical performance of the RDTD test strip compared to mass spectrometry and 2 urine fentanyl immunoassays., Methods: Leftover, deidentified urine samples collected from inpatients and outpatients from our psychiatric hospital and other clinics were frozen at <-70°C, thawed at room temperature, and centrifuged. Aliquots were tested with the RDTD (CLIA Waived, Inc.) test strips and 2 urine fentanyl immunoassays: the ARK Fentanyl II assay (ARK Diagnostics Inc.) and the Immunalysis SEFRIA Fentanyl assay (Immunalysis Corporation). Both assays were conducted on the Abbott Alinity c chemistry analyzer (Abbott Laboratories). Mass spectrometry analysis was performed at ARUP Laboratories. All assays had a 1 ng/mL positive cutoff., Results: A total of 142 urine samples included 70 positive and 72 negative samples. The RDTD strips had lower sensitivity (84.3%) and efficiency (85.9%) and showed a specificity of 87.5% compared to the other assays. The ARK Fentanyl II assay showed identical sensitivity (95.7%) to the Immunalysis SEFRIA Fentanyl assay but had higher specificity (94.4% vs 81.9%) and overall efficiency (95.1% vs 88.7%)., Conclusions: Differences were noted in the number of false negatives and positives among the assays. The RDTD demonstrated acceptable performance in detecting urine fentanyl in our patient population and would provide faster test results at point-of-care testing sites in our healthcare enterprise., (© Association for Diagnostics & Laboratory Medicine 2024.)

Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer to the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, and reduction of certain pre-analytical errors, especially those related to specimen transportation. On the other hand, POCT also poses some limitations and risks, namely the risk of lower accuracy and reliability compared to traditional laboratory tests, quality control and connectivity issues, high dependence on operators (with varying levels of expertise or training), challenges related to patient data management, higher costs per individual test, regulatory and compliance issues such as the need for appropriate validation prior to clinical use (especially for rapid diagnostic tests; RDTs), as well as additional preanalytical sources of error that may remain undetected in this type of testing, which is usually based on whole blood samples (i.e., presence of interfering substances, clotting, hemolysis, etc.). There is no doubt that POCT is a breakthrough innovation in laboratory medicine, but the discussion on its appropriate use requires further debate and initiatives. This collective opinion paper, composed of abstracts of the lectures presented at the two-day expert meeting "Point-Of-Care-Testing: State of the Art and Perspective" (Venice, April 4-5, 2024), aims to provide a thoughtful overview of the state-of-the-art in POCT, its current applications, advantages and potential limitations, as well as some interesting reflections on the future perspectives of this particular field of laboratory medicine., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Background: In this study, we evaluated the impact of hemoglobin (Hb) variants on the performance of the Abbott Alinity c and Bio-Rad Variant II Turbo 2.0 HPLC Hb A1c assays., Methods: The analytical performance of the Abbott Alinity c Hb A1c (enzymatic) assay was compared to the Bio-Rad Variant II Turbo 2.0 HPLC method using leftover whole blood EDTA samples with and without the presence of a hemoglobin variant. Assay precision was determined from an analysis of controls. Bias was estimated from analysis of a set of 40 samples analyzed by a Tosoh G8 HPLC instrument at the University of Missouri Diabetes Diagnostic Laboratory, an NGSP Secondary Reference Laboratory., Results: Between-day precision was excellent for both methods (<3%). Bias met NGSP criteria of ±5% to target value. Correlation between the Alinity and Bio-Rad methods was good for patient samples without a hemoglobinopathy (y = 1.028x - 0.38, standard error of the estimate (SEE) = 0.16, n = 36, mean bias = -0.22). A total of 700 hemoglobin variant samples were evaluated on the 2 methods. Of the hemoglobin variants, 640/700 gave results on both methods: hemoglobin (Hb) S trait (n = 452), C trait (n = 131), D trait (n = 23), E trait (n = 26), and a mixture of other hemoglobinopathies (n = 8) including beta thalassemia, high hemoglobin F, transfused Hb SC, transfused Hb SD, and transfused Hb SS, or unknown variant. There was good agreement for the 640 Hb variants between the methods with a range of mean differences of -0.10 to +0.06 depending on the variant, but more variability (SEE 0.25 to 0.39). Sixty samples did not have paired results., Conclusions: To our knowledge, this study was the largest investigation of the effect of hemoglobinopathies on the Abbott Alinity c Hb A1c assay. Analytical performance varied depending on the specific hemoglobin variant trait when compared to the Bio-Rad Variant II Turbo 2.0 HPLC method., (© American Association for Clinical Chemistry 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: G.F. is the general manager and medical director of the Institute for Diabetes Technology (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. G.F./IfDT have received research support, speakers’ honoraria, or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Lilly, Metronom, Medtronic, Menarini, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, Terumo. M.E., D.W., S.P., S.W., and C.H. are employees of IfDT. R.S. is the chair of the Clinical Chemistry Department of Isala which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. R.S. has received speakers’ honoraria or consulting fees in the last three years from Roche and Menarini. J.J. has for the last three years been a lecturer/member of the scientific advisory board for Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Medtronic, Nordic InfuCare, NovoNordisk A/S, and Sanofi. R.H. is an employee of Roche Diabetes Care GmbH. L.W. has received funding from the Diabetes Center Berne. E.E.B. and K.M. have no disclosures. D.C.K. is a consultant for Atropos Health, Better Therapeutics, Eoflow, Integrity, Lifecare, Nevro, Novo, Sanofi, and Thirdwayv. N.T. is a consultant for Roche Diagnostics and Radiometer. J.H.N. has received research support from Abbott. P.D. is a board member of PharmaSens. A.T. is a freelance consultant. He has received fees for lectures or consultancy fees from Abbott, Berlin Chemie, Dexcom, Evivamed, Menarini, Novo Nordisk, Roche, and Sanofi in the last three years.

Background: Accurate glucose monitoring is vitally important in neonatal intensive care units (NICUs) and clinicians use blood glucose monitors (BGM), such as the Inform II, for bedside glucose monitoring. Studies on BGM use in neonates have demonstrated good reliability; however, most studies only included healthy-term neonates. Therefore, the applicability of results to the preterm and/or ill neonate is limited., Objectives: In preterm and ill neonates, quantify differences in glucose concentrations between (1) capillary glucose (measured by BGM) and arterial glucose (measured by YSI 2300 Stat Plus) and (2) between aliquots from the same arterial blood sample, one measured by BGM versus one by YSI., Design/methods: Forty neonates were included in the study. Using Inform II, we measured glucose concentrations on blood samples simultaneously collected from capillary circulation via heel puncture and from arterial circulation via an umbilical catheter. Plasma was then separated from the remainder of the arterial whole blood sample and a YSI 2300 Stat Plus measured plasma glucose concentration., Results: The dominant majority of arterial BGM results met the Clinical and Laboratory Standard Institute (CLSI) and Food and Drug Administration (FDA) tolerance criteria. Greater discrepancy was observed with capillary BGM values with an average of 27.5% of results falling outside tolerance criteria., Conclusions: Blood glucose monitor testing provided reliable results from arterial blood. However, users should interpret hypoglycemic results obtained from capillary blood with caution., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: David Brooks declares no conflicts of interest, James Nichols has no conflict of interest to declare, Christopther Slaughter has no conflict of interest to declare, and Justin Gregory has served as an advisory board member for Eli Lilly, Medtronic, Dompe, vTv Therapeutics, and Mannkind Corporation and in data and safety monitoring roles for vTv Therapeutics and Medtronic.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data., Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation., Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals., Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the emergence of several variants of concern (VOC) with increased immune evasion and transmissibility. This has motivated studies to assess protection conferred by earlier strains following infection or vaccination to each new VOC. We hypothesized that while NAbs play a major role in protection against infection and disease, a heterologous reinfection or challenge may gain a foothold in the upper respiratory tract (URT) and result in a self-limited viral infection accompanied by an inflammatory response. To test this hypothesis, we infected K18-hACE2 mice with SARS-CoV-2 USA-WA1/2020 (WA1) and, after 24 days, challenged with WA1, Alpha, or Delta. While NAb titers against each virus were similar across all cohorts prior to challenge, the mice challenged with Alpha and Delta showed weight loss and upregulation of proinflammatory cytokines in the URT and lower RT (LRT). Mice challenged with WA1 showed complete protection. We noted increased levels of viral RNA transcripts only in the URT of mice challenged with Alpha and Delta. In conclusion, our results suggested self-limiting breakthrough infections of Alpha or Delta in the URT, which correlated with clinical signs and a significant inflammatory response in mice.

As testing for infectious diseases moves from manual, biological testing such as complement fixation to high throughput automated autoanalyzer, the methods for controlling these assays have also changed to reflect those used in clinical chemistry. However, there are many differences between infectious disease serology and clinical chemistry testing, and these differences have not been considered when applying traditional quality control methods to serology. Infectious disease serology, which is highly regulated, detects antibodies of varying classes and to multiple and different antigens that change according to the organisms' genotype/serotype and stage of disease. Although the tests report a numerical value (usually signal to cut-off), they are not measuring an amount of antibodies, but the intensity of binding within the test system. All serology assays experience lot-to-lot variation, making the use of quality control methods used in clinical chemistry inappropriate. In many jurisdictions, the use of the manufacturer-provided kit controls is mandatory to validate the test run. Use of third-party controls, which are highly recommended by ISO 15189 and the World Health Organization, must be manufactured in a manner whereby they have minimal lot-to-lot variation and at a level where they detect exceptional variation. This paper outlines the differences between clinical chemistry and infectious disease serology and offers a range of recommendations when addressing the quality control of infectious disease serology., (© 2022 Wayne J. Dimech et al., published by De Gruyter, Berlin/Boston.)

Background: The AACC Academy revised the reproductive testing section of the Laboratory Medicine Practice Guidelines: Evidence-Based Practice for Point-of-Care Testing (POCT) published in 2007., Methods: A panel of Academy members with expertise in POCT and laboratory medicine was formed to develop guidance for the use of POCT in reproductive health, specifically ovulation, pregnancy, premature rupture of membranes (PROM), and high-risk deliveries. The committee was supplemented with clinicians having Emergency Medicine and Obstetrics/Gynecology training., Results: Key recommendations include the following. First, urine luteinizing hormone (LH) tests are accurate and reliable predictors of ovulation. Studies have shown that the use of ovulation predicting kits may improve the likelihood of conception among healthy fertile women seeking pregnancy. Urinary LH point-of-care testing demonstrates a comparable performance among other ovulation monitoring methods for timing intrauterine insemination and confirming sufficient ovulation induction before oocyte retrieval during in vitro fertilization. Second, pregnancy POCT should be considered in clinical situations where rapid diagnosis of pregnancy is needed for treatment decisions, and laboratory analysis cannot meet the required turnaround time. Third, PROM testing using commercial kits alone is not recommended without clinical signs of rupture of membranes, such as leakage of amniotic fluid from the cervical opening. Finally, fetal scalp lactate is used more than fetal scalp pH for fetal acidosis due to higher success rate and low volume of sample required., Conclusions: This revision of the AACC Academy POCT guidelines provides recommendations for best practice use of POCT in fertility and reproduction., Competing Interests: Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: Employment or Leadership: J.H. Nichols, The Journal of Applied Laboratory Medicine, AACC; Z. Shajani-Yi, labcorp. Consultant or Advisory Role: J.H. Nichols, IL/Werfen, Diabetes Technology Society, Abbott. Stock Ownership: Z. Shajani-Yi, labcorp. Honoraria: J.H. Nichols, IL/Werfen, Siemens, BioRad. Research Funding: J.H. Nichols, Roche Diagnostics, IL Werfen, Abbott Diagnostics. Expert Testimony: None declared. Patents: None declared. Other Remuneration: J.H. Nichols, support for attending meetings and/or travel from IFCC, CLSI, AACC, CAP., (© American Association for Clinical Chemistry 2022. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Conscious respiratory pattern and rate control is desired by patients with some forms of pulmonary disease that are undergoing respiratory muscle conditioning and rehabilitation, by practitioners of meditation hoping to improve mindfulness and wellbeing, by athletes striving to obtain breathing control in order to increase competitiveness, and by engineers and scientists that wish to use the data from breathing subjects to test hypotheses and develop physiological monitoring systems. Although prerecorded audio sources and computer applications are available that guide breathing exercises, they often suffer from being inflexible and allow only limited customization of the breathing cues. Here we describe a small, lightweight, battery-powered, microprocessor-based respiratory coaching device (RespiCo), which through wireless or wired connections, can be easily customized to precisely guide subjects to breathe at desired respiratory rates using specific breathing patterns through visual, auditory, or haptic cues. Digital signals can also be captured from the device to document the breathing cues provided by the device for research purposes. It is anticipated that this device will have important utility for those who wish to be guided to breathe in a precise manner or in research and development of physiologic monitoring systems., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

Background: Quality management of point-of-care (POC) blood gas testing focuses on verifying instrument accuracy and precision, in addition to performing daily quality control (QC) checks every 8 h and with each patient test (unless internal calibration is verified every 30 min). At the POC, a risk-based approach is suitable to address both systemic and transient sample-specific errors that may negatively impact patient care., Methods: We evaluated the performance of the GEM® Premier™ 5000 with next generation Intelligent Quality Management 2 (iQM®2) (Instrumentation Laboratory, Bedford, MA), from the analysis of approximately 84,000 patient samples across 4 sites. Continuous iQM2 was compared to intermittent liquid QC, either manual or automated, at 2 sites. Analysis of error flags for patient samples and statistical characteristics of QC processes, including method sigma and average detection time (ADT) for an error, were examined., Results: ADT was approximately 2 min with iQM2 and varied from hours to days with intermittent QC. iQM2 Process Control Solutions (PCS) precision was similar or better (>6 sigma for all analytes) than manual (sigma 3.0 for pO2) or automated internal QC (sigma 1.3 for tHb and sigma 3.3 for pO2). In addition, iQM2 detected errors in ∼1.4% of samples, providing an additional safeguard against reporting erroneous results., Conclusions: The findings in this study demonstrate excellent performance of the GEM Premier 5000 with iQM2 including >6 sigma precision for all analytes and faster error detection times. These benefits address risk in different phases of testing that are not easily detected by intermittent performance of liquid QC (manual or automated)., (© American Association for Clinical Chemistry 2021.)

Background: Sigma metric calculations provide laboratories an objective means to assess analytical method performance. Methods with higher sigma values are desirable because they are more reliable and may use less frequent quality control in order to maintain optimal performance. Sigma metrics can also serve as a tool when comparing method performance across assay and manufacturer platforms., Methods: Sigma values were calculated for 28 common chemistry and 24 immunoassay assays across 3 academic medical centers. Method imprecision and percent bias relative to peer group means was tabulated from Bio-Rad quality control (QC) data. Sigma values were calculated for each method using allowable total error (TEa) from either the CLIA evaluation limits or desirable biological variation. Average sigma values were generated for each site and graded as optimal: >6 sigma; good: 5-6 sigma; marginal: 3-5 sigma; or poor: <3 sigma. Analysis of NIST SRM1950 standards for a subset of analytes allowed an estimation of absolute bias., Results: Clinical chemistry assays displayed similar method performance across all 3 study sites. Immunoassays showed significant differences between manufacturers, and a majority of assays failed to meet an optimal level of performance. Different TEa values produced different sigma metrics with more stringent TEa limits based on biological variation, resulting in poorer performance estimates than the wider CLIA limits. Analysis of NIST standards revealed similar performance., Conclusions: Sigma metrics are comparable for chemistry but not immunoassay platforms. The selection of total allowable error goals led to differences in sigma metrics., (© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Rodents (order Rodentia), followed by bats (order Chiroptera), comprise the largest percentage of living mammals on earth. Thus, it is not surprising that these two orders account for many of the reservoirs of the zoonotic RNA viruses discovered to date. The spillover of these viruses from wildlife to human do not typically result in pandemics but rather geographically confined outbreaks of human infection and disease. While limited geographically, these viruses cause thousands of cases of human disease each year. In this review, we focus on three questions regarding zoonotic viruses that originate in bats and rodents. First, what biological strategies have evolved that allow RNA viruses to reside in bats and rodents? Second, what are the environmental and ecological causes that drive viral spillover? Third, how does virus spillover occur from bats and rodents to humans?

Background: A survey of IFCC members was conducted to determine current and future perspectives on digital innovations within laboratory medicine and healthcare sectors., Methods: Questions focused on the relevance of digital diagnostic solutions, implementation and barriers to adopting digital technologies, and supplier roles in supporting innovation. Digital diagnostic market segments were defined by solution recipient (laboratory, clinician, patient/consumer, payor) and proximity to core laboratory operations., Results: Digital solutions were of active interest for >90% of respondents. Although solutions to improve core operations were ranked as the most relevant currently, a future shift to technologies beyond core laboratory expertise is expected. A key area of potential differentiation for laboratory customers was clinical decision support. Currently, laboratories collaborate strongly with suppliers of laboratory integration software and information systems, with high expectations for future collaboration in clinical decision support, disease self-management, and population health management. Asia Pacific countries attributed greater importance to adopting digital solutions than those in other regions. Financial burden was the most commonly cited challenge in implementing digital solutions., Conclusions: Specialists in laboratory medicine are proactively approaching digital innovations and transformation, and there is high enthusiasm and expectation for further collaboration with suppliers and healthcare professionals beyond current core laboratory expertise., (© American Association for Clinical Chemistry 2021.)

Background: Our purpose was to evaluate the performance of the ACCU-CHEK® Inform II blood glucose monitoring system (Roche Diagnostics GmbH) compared with the perchloric acid hexokinase (PCA-HK) comparator method on the cobas® 6000 analyzer (Roche Diagnostics International Ltd) in critically ill patients., Methods: Overall, 476 arterial (376 pediatric/adult, 100 neonate), 375 venous, and 100 neonatal heel-stick whole-blood samples were collected and evaluated from critical care settings at 10 US hospitals, including the emergency department, medical and surgical intensive care units (ICUs), and neonatal and pediatric ICUs. The ACCU-CHEK Inform II system was evaluated at 2 cutoff boundaries: boundary 1 was ≥95% of results within ±12 mg/dL of the reference (samples with blood glucose <75 mg/dL) or ±12% of the reference (glucose ≥75 mg/dL), and boundary 2 was ≥98% of results within ±15 mg/dL or ±15% of the reference. Clinical performance was assessed by evaluating sample data using Parkes error grid, Monte Carlo simulation, and sensitivity and specificity analyses to estimate clinical accuracy and implications for insulin dosing when using the ACCU-CHEK Inform II system., Results: Proportions of results within evaluation boundaries 1 and 2, respectively, were 96% and 98% for venous samples, 94% and 97% for pediatric and adult arterial samples, 84% and 98% for neonatal arterial samples, and 96% and 100% for neonatal heel-stick samples. Clinical evaluation demonstrated high specificity and sensitivity, with low risk of potential insulin-dosing errors., Conclusions: The ACCU-CHEK Inform II system demonstrated clinically acceptable performance against the PCA-HK reference method for blood glucose monitoring in a diverse population of critically ill patients in US care settings., (© American Association for Clinical Chemistry 2021.)

Background: Point-of-Care Testing (POCT) is clinical laboratory testing conducted close to the site of patient care. POCT provides rapid turnaround of test results with the potential for fast clinical action that can improve patient outcomes compared to laboratory testing., Methods: Review the advantages of POCT and discuss the factors that are driving the expansion of POCT in modern healthcare., Results: Portability, ease-of-use, and minimal training are some of the advantages of POCT. The ability to obtain a fast test result and the convenience of testing close to the patient are increasing the demand for POCT. Healthcare is finding new opportunities for growth in the community and POCT is facilitating this growth., Conclusions: This article will review the advantages of POCT and how POCT is complimenting patient care in a variety of settings., (Copyright © 2021 International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). All rights reserved.)