Sterling T. Bennett

Reference ranges for blood concentrations of nucleated red blood cells in neonates.

Background: Previous studies reported a relationship between high nucleated red blood cells (NRBC) in neonates and the development of intraventricular hemorrhage (IVH) and/or retinopathy of prematurity (ROP). Objective: We sought to (1) establish reference ranges for NRBC in neonates based on a large data set, (2) compare NRBC from automated versus manual counts, (3) determine the effect of an elevated NRBC, on the day of birth, on the odds of developing grade ≧3 IVH or ROP. Methods: We analyzed all NRBC obtained during 8.5 years in a multihospital system, displaying the 5th and 95th percentile limits according to gestational age and postnatal age. Results: NRBC counts were retrieved from 61,932 neonates, 26,536 of which were excluded from the data set. Comparing 9,000 samples run simultaneously on manual versus automated methods, the manual counts yielded slightly higher counts, but the difference is likely insignificant clinically. Altitude of the birth hospital did not correlate with NRBC, and no correlations were observed with cord pH or 1- or 5-min Apgar. An NRBC count >95th percentile limit was associated with higher odds of developing a grade ≧3 IVH (OR 4.28; 95% CI 3.17–5.77) and grade ≧3 ROP (OR 4.18; 95% CI 2.74–6.38). Conclusion: The figures of this report display reference ranges for NRBC according to gestational age and postnatal age. An NRBC count above the 95% limit at birth is associated with a higher risk of subsequently developing severe IVH and severe ROP. We speculate that this association is because an elevated NRBC count is a marker for prenatal hypoxia.

Reference intervals for common coagulation tests of preterm infants (CME)

Fresh‐frozen plasma (FFP) is sometimes administered to nonbleeding preterm neonates who are judged to be at risk for bleeding because they have abnormal coagulation tests. The benefits/risks of this practice are not well defined. One limitation to progress is lack of reference intervals for the common coagulation tests, thus limiting precision about whether coagulation tests are indeed abnormal.

Postponing or eliminating red blood cell transfusions of very low birth weight neonates by obtaining all baseline laboratory blood tests from otherwise discarded fetal blood in the placenta

BACKGROUND: Safely reducing the proportion of very low birth weight neonates (<1500 g) that receive a red blood cell (RBC) transfusion would be an advance in transfusion practice.

Reference Ranges for Lymphocyte Counts of Neonates: Associations Between Abnormal Counts and Outcomes

BACKGROUND AND OBJECTIVE: Both high and low lymphocyte counts at birth have been associated with adverse outcomes. However, the validity of defining a lymphocyte count as “abnormal” depends on having an accurate reference range. We established a reference range for neonatal lymphocyte counts by using multihospital data and used this to assess previously reported relationships between abnormal counts and early onset sepsis (EOS), intraventricular hemorrhage (IVH), retinopathy of prematurity (ROP), periventricular leukomalacia, and birth asphyxia. METHODS: We first created a data set that excluded counts from neonates with diagnoses previously associated with abnormal lymphocyte counts. Then the complete data (counts excluded plus included in the reference range) were used to test associations between abnormal counts and EOS, IVH, ROP, periventricular leukomalacia, and outcomes after birth asphyxia. RESULTS: Lymphocyte counts were retrieved from 40 487 neonates, 10 860 of which were excluded from the reference range. A count >95th percentile was associated with EOS (2.07; 95% confidence interval [CI]: 1.80–2.38) and IVH ≥grade 3 (2.93; 95% CI: 1.83–4.71). A count <5th percentile was associated with EOS (odds ratio:1.24; 95% CI: 1.04–1.48), IVH ≥grade 3 (3.23; 95% CI: 1.95–5.36), and ROP ≥stage 3 (4.80: 95% CI: 2.38–9.66). Among 120 meeting criteria for birth asphyxia, those with a low count and a high nucleated red cell count had higher mortality (37% vs 11%, P = .001), more transfusions (P = .000), and more neurology referrals (P < .01). CONCLUSIONS: A reference range for lymphocytes can identify neonates with abnormal counts, which can be useful because these neonates are at higher risk for certain adverse outcomes.

Red blood cell distribution width: reference intervals for neonates

Abstract Objective: To create reference intervals for red blood cell distribution width (RDW) of neonates and to use these intervals to better understand and classify hematopathology of neonates. Study design: This was a retrospective analysis of data from neonates born between 1/1/2001 and 12/31/2011, who had a complete blood cell count (CBC) in the first 14 days. The first RDW recorded from each was displayed according to gestational and postnatal age. Correlation between RDW and reticulocyte count was sought when the two tests were obtained simultaneously. Focused studies were performed on the 20 neonates with the highest and the 10 with the lowest RDW values. Results: RDW values from 165 613 CBCs were included. RDW reference intervals for neonates are higher than for older children and adults. At birth, the lower reference limit for term and late preterm neonates is 15.5%. The upper reference limit is 20%, and is slightly higher (up to 23%) in preterm neonates. For term and late preterm neonates the range does not change in the first two weeks but preterm neonates have a rise in upper reference limit concordant with erythrocyte transfusions. RDW and reticulocytes correlated positively but weakly (r2 = 0.187). Eighteen of the 20 with the highest RDW values (29.4–42.8%) at birth had anemia with prenatal hemorrhage or hemolysis. Those with the lowest RDW values (11.8–13.7%) at birth tended to have a low MCV for age (95.5 ± 11.4 fL versus.129.8 ± 19.3 fL with a high RDW, p < 0.00001). Conclusion: The RDW reference interval at birth is 15.5–20% and does not change appreciably over the first two weeks except for those receiving a transfusion where the RDW increases. High RDW values at birth indicate anisocytosis commonly due to macrocytic reticulocytosis; low values correlate with relative microcytosis.

Continuous Improvement in Continuous Quality Control

“Quality is never an accident. It is always the result of intelligent effort.” So observed John Ruskin, Victorian writer and critic of art, architecture, and society (1), unknowingly foreshadowing the emergence of quality science in the 20th century. Statistical QC came from Bell Telephone Laboratories, where Shewhart developed the statistical control chart in 1924, and Dodge and Romig pioneered statistical methods for acceptance sampling rather than 100% inspection of products. These beneficial techniques were not widespread until the manufacturing demands of World War II necessitated better control of product quality. With Demings involvement, quality science was further developed in postwar Japanese manufacturing (2). Levey and Jennings introduced clinical laboratory statistical QC in 1950 (3). The Levey–Jennings chart remains a staple of laboratory QC. Development of stabilized control materials enhanced QC practices (4), and the testing of controls at fixed intervals became and remains the QC mainstay. Control-based QC has many strengths, including sensitivity to small changes in bias and ready availability of controls. Test intervals may be adjusted to account for assay stability, regulations or a laboratorys experience. Decision rules may be selected to maximize both sensitivity and specificity of QC procedures (5, 6). Weaknesses of control-based QC include the cost of materials and labor, scope limited to the analytical component, and matrix-effect masking of clinically relevant drift. Furthermore, a risk inherent in the intermittency of control-based QC is that analytical instability goes undetected until the next QC episode, while the laboratory releases clinically significant erroneous results. Ideally, QC would be continuous. In a limited sense, continuous QC exists for sample QC through delta checks, anion gap calculations, indices of hemolysis, icterus, lipemia, etc., and for analyzer performance through function checks, temperature sensors, automated reaction pattern analysis, etc. In this issue of Clinical Chemistry , Ng et …

Twenty-Eight Grams of Prevention Is Worth a Half Kilogram of Cure

Benjamin Franklin, early American inventor, entrepreneur, author, politician, and diplomat, was known for his pithy expressions. One of his most famous, oft-quoted sayings is, “An ounce [28 g] of prevention is worth a pound [454 g] of cure.” Less well known is that Franklins sage advice about prevention was given in the context of his organizing the first fire-fighting company in Philadelphia (1). At that time, fires posed an ever-present risk for the residents of Philadelphia and other cities. Meticulous handling of candles, lanterns, embers, and ashes helped avoid property loss, financial ruin, injury, and death. The safety of the populace was inextricably tied to the fire-handling practices of every individual. Today, clinical laboratories are essential components of healthcare systems, contributing invaluable information for the diagnosis, treatment, and avoidance of disease. Decades of research and development have yielded a wide array of sophisticated assays and analyzers. Simultaneously, societal trends and improvements in disease management have fueled an increase in the prevalence of chronic disease, which is driving a clinical demand for long-term consistency in laboratory test results. Assay suppliers attempt to minimize lot-to-lot variation in reagents via manufacturing processes and by conducting lot-release testing to avoid distributing unsuitable lots. Clinical laboratories perform lot-to-lot validation testing to verify a manufacturers performance claims and assure the ongoing reliability of testing. Yet, practicing laboratorians know that far too much time is spent dealing with inconsistent results. In this issue of Clinical Chemistry , Algeciras-Schimnich and colleagues report multiple failures of lot-to-lot validation procedures to detect significant between-lot differences in an insulin-like growth factor 1 (IGF-1)3 assay over a 5-year period in 2 laboratories, the Mayo Clinic and the University of Virginia (2). During this period, the Mayo Clinic used 32 reagent lots, and the University of Virginia used 16 lots. With every …

Starting Is Half

A Korean proverb, capturing the wisdom of the ages, translates literally into English as “starting is half” (1). In a great journey, the first step is often the most difficult. In a major enterprise, just getting underway may take a tremendous effort. In physics, an object that is stationary is distinctly different from another object that is moving, irrespective of velocity or direction. In laboratory medicine, major improvements generally trace their roots to small beginnings. In this issue of Clinical Chemistry , Aarsand and colleagues, on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Biological Variation and the Task and Finish Group for the Biological Variation Database, present a standard for the critical appraisal of biological variation (BV) studies and publications, the Biological Variation Data Critical Appraisal Checklist (BIVAC), introducing a new phase in the study of BV (2). Interest in BV goes back many decades. BV estimates are used primarily to set analytical performance specifications and to identify clinically significant changes in consecutive test results from an individual, but they may also be used to assess the usefulness of population-based reference intervals, determine optimal specimen type for a particular measurand, select the best test among alternatives, select the most informative units for expressing results, determine the number of analyses needed to establish an individuals homeostatic set point, and discover optimal conditions for specimen collection in circumstances of positional, diurnal, seasonal, or other patterned variation (3, 4, 5, 6). BV estimates have been published for a …

Contents Vol. 99, 2011

S. Andersson, Helsinki E. Bancalari, Miami, Fla. J. Bhatia, Augusta, Ga. G. Buonocore, Siena W. Carlo, Birmingham, Ala. I. Choonara, Derby T. Curstedt, Stockholm C. Dani, Florence B. Darlow, Christchurch M. Fujimura, Osaka M. Hallman, Oulu W.W. Hay, Jr., Aurora, Colo. S.E. Juul, Seattle, Wash. M. Kaplan, Jerusalem B. Kramer, Maastricht R.J. Martin, Cleveland, Ohio C.J. Morley, Cambridge J. Neu, Gainesville, Fla. P.C. Ng, Hong Kong M.W. Obladen, Berlin A.G.S. Philip, Sebastopol, Calif. M. Post, Toronto E. Saliba, Tours O.D. Saugstad, Oslo M.S. Schimmel, Jerusalem B. Schmidt, Philadelphia, Pa. M.P. Sherman, Columbia, Mo. E.S. Shinwell, Rehovot K. Simmer, Perth, W.A. J. Smith, Tygerberg B. Sun, Shanghai N. Vain, Buenos Aires F. van Bel, Utrecht J.N. van den Anker, Washington, D.C. M. Vento Torres, Valencia M. Weindling, Liverpool J.A. Widness, Iowa City, Iowa Fetal and Neonatal Research