Journal of Applied Laboratory Medicine最新文献

Background: Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself.

Methods: Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined.

Results: Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements.

Conclusions: An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.

Background: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study developed a framework for assessing the impact of biological and analytical variation on the prediction uncertainty of categorical prediction models.

Methods: Practical application was demonstrated for the prediction of renal function loss (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] equation) and 31-day mortality (advanced ML model) in 6360 emergency department patients. Model outcome was calculated in 100 000 simulations of variation in laboratory parameters. Subsequently, the percentage of discordant predictions was calculated with the original prediction as reference. Simulations were repeated assuming increasing levels of analytical variation.

Results: For the ML model, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were 0.90, 0.44, and 0.96, respectively. At base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-28.8%]. In addition, 7.2% of patients had >5% discordant predictions. At 6× base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-38.8%]. In addition, 11.7% of patients had >5% discordant predictions. However, the impact of analytical variation was limited compared with biological variation. AUROC, sensitivity, and specificity were not affected by variation in laboratory parameters.

Conclusions: The impact of biological and analytical variation on the prediction uncertainty of categorical prediction models, including ML models, can be estimated by the occurrence of discordant predictions in a simulation model. Nevertheless, discordant predictions at the individual level do not necessarily affect model performance at the population level.

Background: Cardiovascular disease, kidney health, and metabolic disease (CKM) syndrome is associated with significant morbidity and mortality, particularly from congestive heart failure (CHF). Guidelines recommend measurement of cardiac troponin (cTn) to identify subclinical heart failure (HF) in diabetics/CKM. However, appropriate thresholds and the impact from routine screening have not been elucidated.

Methods: cTnI was assessed using the Abbott high sensitivity (hs)-cTnI assay in outpatients with physician-ordered hemoglobin A1c (Hb A1c) and associated with cardiac comorbidities/diagnoses, demographics, and estimated glomerular filtration rate (eGFR). Risk thresholds used in CKM staging guidelines of >10 and >12 ng/L for females and males, respectively, were used. Multivariate logistic regression was applied. hs-cTnI was assessed in a high-fat-diet induced murine model of obesity and diabetes.

Results: Of 1304 patients, 8.0% females and 15.7% males had cTnI concentrations above the risk thresholds. Thirty-one (4.2%) females and 23 (4.1%) males had cTnI above the sex-specific 99% upper reference limit. A correlation between hs-cTnI and Hb A1c (R = 0.2) and eGFR (R = -0.5) was observed. hs-cTnI concentrations increased stepwise based on A1C of <5.7% (median = 1.5, IQR:1.3-1.8), 5.7%-6.4% (2.1, 2.0-2.4), 6.5%-8.0% (2.8, 2.5-3.2), and >8% (2.8, 2.2-4.3). Male sex (P < 0.001), eGFR (P < 0.001), and CHF (P = 0.004) predicted elevated hs-cTnI. Obese and diabetic mice had increased hs-cTnI (7.3 ng/L, 4.2-10.4) relative to chow-fed mice (2.6 ng/L, 1.3-3.8).

Conclusion: A high proportion of outpatients with diabetes meet criteria for subclinical HF using hs-cTnI measurements. Glucose control is independently associated with elevated cTnI, a finding replicated in a murine model of metabolic syndrome.

Background: Sex- and population-specific 99th percentiles of high-sensitivity cardiac troponin (hs-cTn) are recommended in guidelines although the evidence for a clinical utility is sparse. The DANSPOT trial will investigate the clinical effect of sex- and population-specific 99th percentiles of cTn. We report the 99th percentiles derived from this trial and their dependency on kidney function.

Methods: We used samples from healthy Danish blood donors and measured hemoglobin A1c, N-terminal pro-brain natriuretic peptide and creatinine, and calculated an estimated glomerular filtration rate (eGFR). We compared 2 cutoffs for the eGFR of healthy participants (60 vs 90 mL/min/1.73 m2). The cTn assays investigated were Siemens Atellica and Dimension Vista hs-cTnI, Abbott hs-cTnI, and Roche hs-cTnT.

Results: A total of 2287 participants were sampled, of which 71 (3.1%) were excluded due to a history of heart disease (n = 4), insufficient material (n = 7), or a screening biomarker (n = 60). Of the remaining 2216 participants, 1325 (59.8%) had an eGFR ≥90 mL/min/1.73 m2. Compared to a cutoff of 60 mL/min/1.73 m2 for eGFR, using 90 mL/min/1.73 m2 resulted in lower 99th percentiles for females; Siemens Vista (46 vs 70 ng/L), Abbott (14 vs 18 ng/L), and Roche cTnT (10 vs 13 ng/L), and decreased the number of measurements above the manufacturers' 99th percentiles for all assays.

Conclusions: We present reference values for 4 cTn assays for eGFR cutoffs of 60 and 90 mL/min/1.73 m2. These cutoffs differ based on the eGFR threshold for inclusion indicating that any chosen cutoff is also valuable with moderately reduced kidney function.

Background: Ceruloplasmin (Cp) is the most important serum copper transport protein playing a key role in the binding of iron to transferrin. It is a positive acute-phase response protein and the first-level diagnostic marker for Wilson disease and aceruloplasminemia. However, standardization of Cp measurement has not been successful, and assay specific reference levels of Cp are required.

Methods: From May 2019 to July 2022, we enrolled 1706 consecutive healthy Italian blood donors (1285 men and 421 women, 18 to 65 years) to identify the reference intervals of serum Cp through quantile regression and evaluate the relationship of Cp with age, sex, iron, and metabolic status through linear regression.

Results: We found that mean serum Cp was influenced by sex and slightly by age. The lower reference Cp value rose slightly with increasing age in both men and women. The upper reference value increased, reaching a plateau of about 25 mg/dL around 25 years in men, while in women it initially increased to around 45 mg/dL in young adults to fall sharply below 30 mg/dL for adults after their fifties.

Conclusions: We showed that the normal reference curves of serum Cp vary according to sex in a large population of healthy adults. While the lower reference values did not appear to be influenced by age and sex, the upper ones differed according to sex and age showing a particularly high variability in women, possibly reflecting different hormonal status.

Background: Circulating cardiac troponin-I (cTnI) plays a crucial role in biomarker staging systems, offering important information for prognostification and risk stratification of patients with AL amyloidosis. High-sensitivity cTnI (HS-cTnI) assays have been introduced in practice; however, the data on the concordance between conventional and HS-cTnI and the utility of HS-cTnI in cardiac biomarker staging are lacking.

Methods: Seventy-eight consecutive patients with AL amyloidosis who were prospectively evaluated at the Boston University Amyloidosis Center from October 2022 through March 2023 were included. cTnI was measured using the Abbott Architect cTnI chemiluminescent microparticle immunoassay (CMIA) and HS-cTnI using the Abbott Alinity HS-cTnI CMIA assay. Assay results were compared by Deming regression and Bland-Altman analyses, and cardiac biomarker stages were assigned and compared using both assay results.

Results: Median cTnI and HS-cTnI concentrations were 13.0 and 7.0 ng/L, respectively. Bland-Altman analysis demonstrated a negative bias with HS-cTnI results (mean percent difference between assays: -49.8%) and the greatest variance occurring below 50 ng/L. Deming regression supported this negative discordance (slope, 0.66; intercept, -1.9). The use of HS-cTnI assay downgraded cardiac biomarker staging assignments from stage IIIA to stage II (n = 3) and from stage IIIB to stage II (n = 1).

Conclusions: Overall agreement was demonstrated; however, a negative bias for HS-cTnI assay was noted at low concentrations. The application of the conventional cTnI threshold of >100 ng/L to HS-cTnI-based Boston University cardiac staging showed a trend toward downgraded staging assignments. The prognostic utility of HS-cTnI assay in biomarker staging warrants further investigation in patients with AL amyloidosis.

Background: Familial hypercholesterolemia (FH) is a frequently underdiagnosed genetic disorder characterized by elevated low-density lipoprotein (LDL) levels. Genetic testing of LDLR, APOB, and PCSK9 genes can identify variants in up to 80% of clinically diagnosed patients. However, limitations in time, scalability, and cost have hindered effective next-generation sequencing of these genes. Additionally, pharmacogenomic variants are associated with statin-induced adverse effects in FH patients. To address these challenges, we developed a multiplex primer-based amplicon sequencing approach for FH genetic testing.

Methods: Multiplex primers were designed for the exons of the LDLR, APOB, and PCSK9 genes, as well as for pharmacogenomic variants rs4149056 (SLCO1B1:c.521T > A), rs2306283 (SLCO1B1:c.388A > G), and rs2231142 (ABCG2:c.421C > A). Analytical validation using samples with known pathogenic variants and clinical validation with 12 FH-suspected probands were conducted. Library preparation was based on a bead-based tagmentation method, and sequencing was conducted on the NovaSeq 6000 platform.

Results: Our approach ensured no amplicon dropouts, with over 100× coverage on each amplicon. Known variants in 2 samples were successfully detected. Further, we identified one heterozygous LDLR (p.Glu228Ter) variant and 2 homozygous cases of LDLR (p.Lys294Ter) and LDLR (p.Ser177Leu) variants in patients. Pharmacogenomic analysis revealed that overall 3 patients may require reduced statin doses. Our approach offered reduced library preparation time (approximately 3 h), greater scalability, and lower costs (under $50) for FH genetic testing.

Conclusions: Our method effectively sequences LDLR, APOB, and PCSK9 genes including pharmacogenomic variants that will guide appropriate screening and statin dosing, thus increasing both efficiency and affordability.