References
- Schmidt RL, Pearson LN. Quality control optimization part II: a method to optimize the accuracy of laboratory quality control. Clin Chim Acta. 2019;495:233–238.
- Schmidt RL, Pearson LN. Quality control optimization part I: metrics for evaluating predictive performance of quality control. Clin Chim Acta. 2019;495:174–184.
- Westgard JO. Perspectives on quality control, risk management, and analytical quality management. Clin Lab Med. 2013;33(1):1–14.
- Westgard JO. Statistical quality control procedures. Clin Lab Med. 2013;33(1):111–124.
- Hoorn EJ, Tuut MK, Hoorntje SJ, et al. Dutch guideline for the management of electrolyte disorders – 2012 revision. Neth J Med. 2013;71(3):153–165.
- Delanghe JR. Management of electrolyte disorders: also the method matters! Acta Clin Belg. 2019;74(1):2–6.
- Meng QH, Wagar EA. Pseudohyperkalemia: a new twist on an old phenomenon. Crit Rev Clin Lab Sci. 2015;52(2):45–55.
- Montford JR, Linas S. How dangerous is hyperkalemia? JASN. 2017;28(11):3155–3165.
- Lippi G, Blanckaert N, Bonini P, et al. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories. Clin Chem Lab Med. 2008;46(6):764–772.
- Singer AJ, Thode HC, Jr, Peacock WF. A retrospective study of emergency department potassium disturbances: severity, treatment, and outcomes. Clin Exp Emerg Med. 2017;4(2):73–79.
- Ranjitkar P, Greene DN, Baird GS, et al. Establishing evidence-based thresholds and laboratory practices to reduce inappropriate treatment of pseudohyperkalemia. Clin Biochem. 2017;50(12):663–669.
- Mays JA, Greene DN, Merrill AE, et al. Evidence-based validation of hemolysis index thresholds by use of retrospective clinical data. J App Lab Med. 2018;3(1):109–114.
- Kormoczi GF, Saemann MD, Buchta C, et al. Influence of clinical factors on the haemolysis marker haptoglobin. Eur J Clin Invest. 2006;36(3):202–209.
- Marchand A, Galen RS, Van Lente F. The predictive value of serum haptoglobin in hemolytic disease. JAMA. 1980;243(19):1909–1911.
- Katkish L, Rector T, Ishani A, et al. Incidence and severity of pseudohyperkalemia in chronic lymphocytic leukemia: a longitudinal analysis. Leuk Lymphoma. 2016;57(8):1952–1955.
- Bellevue R, Dosik H, Spergel G, et al. Pseudohyperkalemia and extreme leukocytosis. J Lab Clin Med. 1975;85(4):660–664.
- Cao J, Karger AB. Critically elevated potassium in a 55-year-old female with chronic lymphocytic leukemia. Lab Med. 2018;49(3):280–283.
- Colussi G, Cipriani D. Pseudohyperkalemia in extreme leukocytosis. Am J Nephrol. 1995;15(5):450–452.
- Stankovic AK, Smith S. Elevated serum potassium values: the role of preanalytic variables. Am J Clin Pathol. 2004;121(Suppl):S105–S12.
- Graber M, Subramani K, Corish D, et al. Thrombocytosis elevates serum potassium. Am J Kidney Dis. 1988;12(2):116–120.
- Thurlow V, Ozevlat H, Jones SA, et al. Establishing a practical blood platelet threshold to avoid reporting spurious potassium results due to thrombocytosis. Ann Clin Biochem. 2005;42(3):196–199.
- Lee HK, Brough TJ, Curtis MB, et al. Pseudohyperkalemia – is serum or whole blood a better specimen type than plasma? Clin Chim Acta. 2008;396(1–2):95–96.
- Sturgeon CM, Viljoen A. Analytical error and interference in immunoassay: minimizing risk. Ann Clin Biochem. 2011;48(5):418–432.
- Jacobs JF, van der Molen RG, Bossuyt X, et al. Antigen excess in modern immunoassays: to anticipate on the unexpected. Autoimmun Rev. 2015;14(2):160–167.
- Ward G, Simpson A, Boscato L, et al. The investigation of interferences in immunoassay. Clin Biochem. 2017;50(18):1306–1311.
- Garcia-Casal MN, Pasricha SR, Martinez RX, et al. Are current serum and plasma ferritin cut-offs for iron deficiency and overload accurate and reflecting iron status? A systematic review. Arch Med Res. 2018;49(6):405–417.
- Kernan KF, Carcillo JA. Hyperferritinemia and inflammation. Int Immunol. 2017;29(9):401–409.
- Ranjitkar P, Turtle CJ, Harris NS, et al. Susceptibility of commonly used ferritin assays to the classic hook effect. Clin Chem Lab Med. 2016;54(2):e41–e43.
- Pullan NJ, Hitch T. Development of an automatic laboratory computer flagging system to identify urine albumin samples potentially affected by antigen excess ('hooking'). Ann Clin Biochem. 2012;49(3):289–291.
- Lo SY, Baird GS, Greene DN. A roadmap to defining the clinical reportable ranges of chemistry analytes: increasing automation efficiency and decreasing manual dilutions. Clin Chim Acta. 2015;451(Pt B):257–262.
- Gallego DF, Lo SY, Greene DN. Prospective evaluation of dilution parameters optimized for 53 chemistry analytes measured using the AU instrument series. Clin Chim Acta. 2017;464:202–203.
- Solberg HE. The IFCC recommendation on estimation of reference intervals. The RefVal program. Clin Chem Lab Med. 2004;42(7):710–714.
- CLSI. EP29-A3c Defining, establishing, and verifying reference intervals in the clinical laboratory. Approved Guideline – Third edition. Wayne (PA): Clinical and Laboratory Standards Institute. 2010.
- Horowitz GL. Reference intervals: practical aspects. EJIFCC. 2008;19(2):95–105.
- Bhattacharya CG. A simple method of resolution of a distribution into Gaussian components. Biometrics. 1967;23(1):115–135.
- Hoffmann RG. Statistics in the practice of medicine. JAMA. 1963;185(11):864–873.
- Drees JC, Huang K, Petrie MS, et al. Reference intervals generated by electronic medical record data mining with clinical exclusions: age-specific intervals for thyroid-stimulating hormone from 33038 euthyroid patients. J App Lab Med. 2018;3(2):231–239.
- Aoki Y, Belin RM, Clickner R, et al. Serum TSH and total T4 in the United States population and their association with participant characteristics: National Health and Nutrition Examination Survey (NHANES 1999–2002). Thyroid. 2007;17(12):1211–1223.
- Boucai L, Surks MI. Reference limits of serum TSH and free T4 are significantly influenced by race and age in an urban outpatient medical practice. Clin Endocrinol (Oxf). 2009;70(5):788–793.
- Surks MI, Hollowell JG. Age-specific distribution of serum thyrotropin and antithyroid antibodies in the US population: implications for the prevalence of subclinical hypothyroidism. J Clin Endocrinol Metab. 2007;92(12):4575–4582.
- Jonklaas J, Bianco AC, Bauer AJ, et al. Guidelines for the treatment of hypothyroidism: prepared by the American thyroid association task force on thyroid hormone replacement. Thyroid. 2014;24(12):1670–1751.
- Drees JC, Stone JA, Reamer CR, et al. Falsely undetectable TSH in a cohort of South Asian euthyroid patients. J Clin Endocrinol Metab. 2014;99(4):1171–1179.
- CLSI. CLSI C62-A Liquid Chromatography–Mass Spectrometry Methods; Approved Guideline. Wayne, PA: Clinical and Laboratory Standards Institute. 2014.
- Lynch KL. Accreditation and quality assurance for clinical liquid chromatography–mass spectrometry laboratories. Clin Lab Med. 2018;38(3):515–526.
- Vogeser M, Seger C. Quality management in clinical application of mass spectrometry measurement systems. Clin Biochem. 2016;49(13–14):947–954.
- Christians U, Vinks AA, Langman LJ, et al. Impact of laboratory practices on interlaboratory variability in therapeutic drug monitoring of immunosuppressive drugs. Ther Drug Monit. 2015;37(6):718–724.
- Zabell AP, Foxworthy T, Eaton KN, et al. Diagnostic application of the exponentially modified Gaussian model for peak quality and quantitation in high-throughput liquid chromatography–tandem mass spectrometry. J Chromatogr A. 2014;1369:92–97.
- Lytle FE, Julian RK. Automatic processing of chromatograms in a high-throughput environment. Clin Chem. 2016;62(1):144–153.
- Henderson CM, Shulman NJ, MacLean B, et al. Skyline performs as well as vendor software in the quantitative analysis of serum 25-hydroxy vitamin D and vitamin D binding globulin. Clin Chem. 2018;64(2):408–410.
- Stone J. High-throughput serum 25-hydroxy vitamin D testing with automated sample preparation. Methods Mol Biol. 2016;1378:301–320.
- Schifman RB, Talbert M, Souers RJ. Delta check practices and outcomes: a Q-probes study involving 49 health care facilities and 6541 delta check alerts. Arch Pathol Lab Med. 2017;141(6):813–823.
- Strathmann FG, Baird GS, Hoffman NG. Simulations of delta check rule performance to detect specimen mislabeling using historical laboratory data. Clin Chim Acta. 2011;412(21–22):1973–1977.
- Ovens K, Naugler C. How useful are delta checks in the 21 century? A stochastic–ynamic model of specimen mix-up and detection. J Pathol Inform. 2012;3:5.
- Randell EW, Yenice S. Delta checks in the clinical laboratory. Crit Rev Clin Lab Sci. 2019;56(2):75–97.
- Albeiroti S, Alberti MO, Buggs V, et al. Evaluation of 2 batched pretreatment systems for the measurement of whole blood tacrolimus on the ARCHITECT immunoassay analyzer. Lab Med. 2016;47(4):268–274.
- Badrick T, Bietenbeck A, Cervinski MA, et al. Patient-based real-time quality control: review and recommendations. Clin Chem. 2019;65(8):962–971.
- Green SF. The cost of poor blood specimen quality and errors in preanalytical processes. Clin Biochem. 2013;46(13–14):1175–1179.
- Lee VS, Kawamoto K, Hess R, et al. Implementation of a value-driven outcomes program to identify high variability in clinical costs and outcomes and association with reduced cost and improved quality. JAMA. 2016;316(10):1061–1072.
- Seegmiller AC, Kim AS, Mosse CA, et al. Data-driven iterative refinement of bone marrow testing protocols leads to progressive improvement in cytogenetic and molecular test utilization. Am J Clin Pathol. 2016;146(5):585–593.
- Rush B, Celi LA, Stone DJ. Applying machine learning to continuously monitored physiological data. J Clin Monit Comput. 2019;33(5):887–893.
- Saria S, Butte A, Sheikh A. Better medicine through machine learning: what's real, and what's artificial? PLoS Med. 2018;15(12):e1002721.