Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer.

Importance: Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater). Objective: To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers. Design, Setting, and Participants: RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023. Exposure: Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy. Main Outcomes and Measures: Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily. Results: Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater. Conclusions and Relevance: In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

BACKGROUND: Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts. METHODS: To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. RESULTS: GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. CONCLUSIONS: GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Baseline prostate health index risk category and risk category changes during active surveillance predict grade reclassification.

BACKGROUND: It is not known whether baseline prostate health index (PHI) at the initiation of active surveillance (AS) or repeated PHI testing during AS is of clinical value after confirmatory biopsy in AS men followed with multiparametric magnetic resonance imaging (mpMRI). METHODS: We identified 382 AS patients with no greater than Grade Group 1 (GG1) prostate cancer on diagnostic and confirmatory biopsy, at least one mpMRI and PHI test, of which 241 had at least 2 PHI tests. Grade reclassification (GR) was defined as ≥GG2 on surveillance biopsy. PHI risk categories 1 to 4 were as defined by the manufacturer. Associations between baseline PHI risk category or baseline PSA density (PSAD), change in PHI risk categories over time or PSAD changes over time and GR were evaluated with multivariable Cox proportional hazard regression models adjusted for age, Prostate Imaging-Reporting and Data System score and number of positive cores. RESULTS: Men with baseline PHI scores in the highest risk categories had lower rates of GR-free survival (log-rank P < 0.001), as did those who increased in PHI risk category or remained in a high PHI risk category during surveillance (log-rank P = 0.032). On multivariable regression, baseline PHI risk category was a predictor of GR (risk category 4 [vs. 1] hazard ratio [HR] 2.74, 95% confidence interval [CI] 1.32-5.66, P = 0.002, model C-index 0.764, Akaike Information Criterion [AIC] 797), as were PHI risk category changes over time (risk category 4 [vs. 1] HR 4.20, 95% CI 1.76-10.05, P = 0.002, C-index 0.759, AIC 489). Separate models with baseline PSAD and PSAD changes over time yielded C-indices of 0.709 (AIC 809) and 0.733 (AIC 495) respectively. CONCLUSIONS: Baseline PHI risk category and PHI changes over time were both independent predictors of GR after confirmatory biopsy, but the added benefit over PSAD seemed modest. However, baseline PHI and PHI risk category changes provided clinically useful risk stratification for time to GR, so further evaluation of PHI's ability to help reduce the frequency of mpMRI and/or surveillance biopsies with more PHI data points over time may be warranted.

A novel internal training program using Kern's 6-step approach to curriculum development for medical laboratory scientists training to be international quality assurance/quality control coordinators.

OBJECTIVE: Patient Safety Monitoring in International Laboratories (pSMILE) is a resource ensuring quality testing in clinical laboratories performing National Institutes of Health-funded HIV research requiring specific staff training. We demonstrate the development of an online asynchronous training model using Kern's 6-step approach to support pSMILE functions. METHODS: An existing curriculum was revamped to incorporate Kern's approach. Metrics for success were described in rubrics with feedback guiding improvements and updates. RESULTS: Curriculum updates took more than a year. Direct observations of skills informed curriculum changes. Module self-evaluations were reviewed to assess performance and the overall curriculum. The content, curriculum, and training documentation were deemed compliant with International Organization for Standardization (ISO) 9001:2015. CONCLUSION: Asynchronous training for highly skilled and self-directed staff is a novel way to deploy training while maintaining productivity of existing staff. Feedback and evaluation allowed for curriculum updates including previously underdeveloped topics. Kern's approach ensured that the needs of the sponsor, management, laboratories, and learners were met.

VCAM-1 complements CA-125 in detecting recurrent ovarian cancer.

BACKGROUND: Close to three-quarters of ovarian cancer cases are frequently diagnosed at an advanced stage, with more than 70% of them failing to respond to primary therapy and relapsing within 5 years. There is an urgent need to identify strategies for early detection of ovarian cancer recurrence, which may lead to earlier intervention and better outcomes. METHODS: A customized magnetic bead-based 8-plex immunoassay was evaluated using a Bio-Plex 200 Suspension Array System. Target protein levels were analyzed in sera from 58 patients diagnosed with advanced ovarian cancer (including 34 primary and 24 recurrent tumors) and 46 healthy controls. The clinical performance of these biomarkers was evaluated individually and in combination for their ability to detect recurrent ovarian cancer. RESULTS: An 8-plex immunoassay was evaluated with high analytical performance suitable for biomarker validation studies. Logistic regression modeling selected a two-marker panel of CA-125 and VCAM-1 that improved the performance of CA-125 alone in detecting recurrent ovarian cancer (AUC: 0.813 versus 0.700). At a fixed specificity of 83%, the two-marker panel significantly improved sensitivity in separating primary from recurrent tumors (70.8% versus 37.5%, P = 0.004), demonstrating that VCAM-1 was significantly complementary to CA-125 in detecting recurrent ovarian cancer. CONCLUSIONS: A two-marker panel of CA-125 and VCAM-1 showed strong diagnostic performance and improvement over the use of CA-125 alone in detecting recurrent ovarian cancer. The experimental results warrant further clinical validation to determine their role in the early detection of recurrent ovarian cancer.

Urinary PSA and Serum PSA for Aggressive Prostate Cancer Detection.

Serum PSA, together with digital rectal examination and imaging of the prostate gland, have remained the gold standard in urological practices for the management of and intervention for prostate cancer. Based on these adopted practices, the limitations of serum PSA in identifying aggressive prostate cancer has led us to evaluate whether urinary PSA levels might have any clinical utility in prostate cancer diagnosis. Utilizing the Access Hybritech PSA assay, we evaluated a total of n = 437 urine specimens from post-DRE prostate cancer patients. In our initial cohort, PSA tests from a total of one hundred and forty-six (n = 146) urine specimens were obtained from patients with aggressive (Gleason Score ≥ 8, n = 76) and non-aggressive (Gleason Score = 6, n = 70) prostate cancer. A second cohort, with a larger set of n = 291 urine samples from patients with aggressive (GS ≥ 7, n = 168) and non-aggressive (GS = 6, n = 123) prostate cancer, was also utilized in our study. Our data demonstrated that patients with aggressive disease had lower levels of urinary PSA compared to the non-aggressive patients, while the serum PSA levels were higher in patients with aggressive prostate disease. The discordance between serum and urine PSA levels was further validated by immuno-histochemistry (IHC) assay in biopsied tumors and in metastatic lesions (n = 62). Our data demonstrated that aggressive prostate cancer was negatively correlated with the PSA in prostate cancer tissues, and, unlike serum PSA, urinary PSA might serve a better surrogate for capitulating tissue milieus to detect aggressive prostate cancer. We further explored the utility of urine PSA as a cancer biomarker, either alone and in combination with serum PSA, and their ratio (serum to urine PSA) to predict disease status. Comparing the AUCs for the urine and serum PSA alone, we found that urinary PSA had a higher predictive power (AUC= 0.732) in detecting aggressive disease. Furthermore, combining the ratios between serum to urine PSA with urine and serum assay enhanced the performance (AUC = 0.811) in predicting aggressive prostate disease. These studies support the role of urinary PSA in combination with serum for detecting aggressive prostate cancer.

Differentially expressed glycoproteins in pre- and post-digital rectal examination urine samples for detecting aggressive prostate cancer.

Urinary glycoproteins associated with aggressive prostate cancer (AG-PCa) were previously reported using post-digital rectal examination (DRE) urine specimens. To explore the potential of using pre-DRE urine specimens for detecting AG-PCa, we compared glycoproteins between pre- and post-DRE urine specimens, verified the previously identified post-DRE AG-PCa-associated urinary glycoproteins in pre-DRE urine specimens, and explored potential new glycoproteins for AG-PCa detection in pre-DRE urine specimens. Quantitative glycoproteomic data were acquired for 154 pre-DRE urine specimens from 41 patients with no cancer at biopsy, 48 patients with non-AG-PCa (Gleason score = 6), and 65 patients with AG-PCa (Gleason score 7 or above). Compared to glycopeptides from the post-DRE urine data, humoral immunity-related proteins were enriched in pre-DRE urine samples, whereas cell mediated immune response proteins were enriched in post-DRE urine samples. Analyses of AG-PCa-associated glycoproteins from pre-DRE urine revealed that the three urinary glycoproteins, prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97) that were previously identified in post-DRE urine samples, were also observed as AG-PCa associated glycoproteins in pre-DRE urine. In addition, we identified three new glycoproteins, fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2), to be potentially associated with AG-PCa in pre-DRE urine specimens. In summary, glycoprotein profiles differ between pre- and post-DRE urine specimens. The identified AG-PCa-associated glycoproteins may be further evaluated in large cohort of pre-DRE urine specimens for detecting clinically significant PCa.

Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy.

BACKGROUND: Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy. METHODS: We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC. RESULTS: The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS. CONCLUSIONS: RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

Specificity and Confirmation of SARS-CoV-2 Serological Test Methods in Emergency Department Populations across the United States.

BACKGROUND: Serological testing for SARS-CoV-2 is integral for understanding prevalence of disease, tracking of infections, confirming humoral response to vaccines, and determining timing and efficacy of boosters. The study objective was to compare the specificity of serology assays in emergency department populations across the United States in 2019 (pre-pandemic) and early 2020, incorporating an automated confirmatory assay. METHODS: Patient specimens (n = 1954) were from 4 regions in the United States: New York, NY; Milwaukee, WI; Miami, FL; and Los Angeles, CA. Specimens were tested with SARS-CoV-2 anti-spike receptor-binding domain assays: SARS-CoV-2 IgG on the Abbott Alinity i (AdviseDx SARS-Cov-2 IgG II) and Beckman Coulter Access 2 (SARS-CoV-2 IgG II), and SARS-CoV-2 IgM on the Abbott Alinity i (AdviseDx SARS-CoV-2 IgM). Reactive samples were tested with a research use only angiotensin-converting enzyme 2 binding inhibition assay (Abbott ARCHITECT) for confirmation of SARS-CoV-2 neutralizing antibodies. Assay specificity was determined and comparisons performed with Fisher's exact test. RESULTS: Overall SARS-CoV-2 IgG specificity was 99.28% (95% confidence interval, 98.80%-99.61%), 99.39% (98.93%-99.68%), and 99.44% (98.99%-99.72%) for SARS-CoV-2 IgG by Abbott and Beckman, and SARS-CoV-2 IgM, respectively. Overall agreement for the two IgG assays was 99.28% (range for the 4 sites: 98.21% to 100%). There were no specificity differences between assays or sites. CONCLUSIONS: The specificity of the serological assays evaluated in a large, diverse emergency department population was >99% and did not vary by geographical site. A confirmatory algorithm with an automated pseudo-neutralization assay allowed testing on the same specimen while reducing the false positivity rate and increasing the value of serology screening methods.

Impact of Prostate Health Index Results for Prediction of Biopsy Grade Reclassification During Active Surveillance.

PURPOSE: We assessed whether Prostate Health Index results improve prediction of grade reclassification for men on active surveillance. METHODS AND MATERIALS: We identified men in Canary Prostate Active Surveillance Study with Grade Group 1 cancer. Outcome was grade reclassification to Grade Group 2+ cancer. We considered decision rules to maximize specificity with sensitivity set at 95%. We derived rules based on clinical data (R1) vs clinical data+Prostate Health Index (R3). We considered an "or"-logic rule combining clinical score and Prostate Health Index (R4), and a "2-step" rule using clinical data followed by risk stratification based on Prostate Health Index (R2). Rules were applied to a validation set, where values of R2-R4 vs R1 for specificity and sensitivity were evaluated. RESULTS: We included 1,532 biopsies (n = 610 discovery; n = 922 validation) among 1,142 men. Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among the discovery set, at 95% sensitivity, R2 yielded highest specificity at 27% vs 17% for R1. In the validation set, R3 had best performance vs R1 with Δsensitivity = -4% and Δspecificity = +6%. There was slight improvement for R3 vs R1 for confirmatory biopsy (AUC 0.745 vs R1 0.724, ΔAUC 0.021, 95% CI 0.002-0.041) but not for subsequent biopsies (ΔAUC -0.012, 95% CI -0.031-0.006). R3 did not have better discrimination vs R1 among the biopsy cohort overall (ΔAUC 0.007, 95% CI -0.007-0.020). CONCLUSIONS: Among active surveillance patients, using Prostate Health Index with clinical data modestly improved prediction of grade reclassification on confirmatory biopsy and did not improve prediction on subsequent biopsies.

Specificity and Confirmation of SARS-CoV-2 Serological Test Methods in Emergency Department Populations Across the United States in 2019 and Early 2020.

BACKGROUND: Serological testing for SARS-CoV-2 is integral for understanding prevalence of disease, tracking of infections, confirming humoral response to vaccines, and determining timing and efficacy of boosters. The study objective was to compare the specificity of serology assays in emergency department populations across the United States in 2019 (pre-pandemic) early 2020 incorporating an automated confirmatory assay. METHODS: Patient specimens (n = 1954) were from four regions in the United States: New York, NY; Milwaukee, WI; Miami, FL; and Los Angeles, CA. Specimens were tested with SARS-CoV-2 anti-spike receptor binding domain assays: SARS-CoV-2 IgG on the Abbott Alinity i (AdviseDx SARS-Cov-2 IgG II) and Beckman Coulter Access 2 (SARS-CoV-2 IgG II), and SARS-CoV-2 IgM on the Abbott Alinity i (AdviseDx SARS-CoV-2 IgM). Reactive samples were tested with a research use only ACE2 binding inhibition assay (Abbott ARCHITECT) for confirmation of SARS-CoV-2 neutralizing antibodies. Assay specificity was determined and comparisons performed with Fisher's Exact Test. RESULTS: Overall SARS-CoV-2 IgG specificity was 99.28% (95% confidence interval: 98.80%-99.61%), 99.39% (98.93%-99.68%), and 99.44% (98.99%-99.72%) for SARS-CoV-2 IgG by Abbott and Beckman, and SARS-CoV-2 IgM, respectively. Overall agreement for the two IgG assays was 99.28% (range for the four sites: 98.21%-100%). There were no specificity differences between assays or sites. CONCLUSIONS: The specificity of the serological assays evaluated in a large diverse emergency department population was >99% and did not vary by geographical site. A confirmatory algorithm with an automated pseudo-neutralization assay allowed testing on the same specimen while reducing the false positivity rate and increasing the value of serology screening methods.

Free Thyroxine Distinguishes Subclinical Hypothyroidism From Other Aging-Related Changes in Those With Isolated Elevated Thyrotropin.

Background: Although a finding of isolated elevated thyrotropin (TSH) often leads to treatment with thyroid hormone, it is not specific to a diagnosis of subclinical hypothyroidism, particularly in older adults. We have previously used longitudinal assessment of TSH and free thyroxine (FT4) to distinguish primary and secondary changes in the hypothalamic-pituitary-thyroid (HPT) axis, an approach which is impractical for clinical diagnosis. Objective: Identify contemporaneous clinical tests and criteria that predict the longitudinally-derived HPT axis phenotype in those with isolated elevated TSH. Methods: Using data from Baltimore Longitudinal Study of Aging, participants with over three years of follow up not on thyroid hormone replacement, with a TSH above the reference range and an in-range FT4 at the current visit, and at least 1% per year increase in TSH (mean 6.9% annual increase; n=72), we examined correlations between various clinical factors and the change in FT4 across the phenotypic range from emerging hypothyroidism, with falling FT4, to adaptive stress-response, with rising FT4. Results: Current FT4 level, but not TSH, Free T3, anti-TPO antibody status, age or sex, was significantly associated with phenotype, determined by the annual rate of change in FT4 in those with elevated and rising TSH, both as a continuous variable (ß=0.07 per ng/dL increase in FT4; p<0.001) and in quartiles (p<0.001). We estimated a threshold for FT4 of less than 0.89 ng/dL (11.45 pmol/L; the 24th percentile of the reference range), as predictive of a phenotype in the first quartile, consistent with subclinical hypothyroidism, while a FT3:FT4 ratio below 2.77 predicted a phenotype in the fourth quartile, more consistent with adaptive stress-response. Conclusions: In those with isolated elevated TSH, a FT4 in the lowest quartile of the reference range differentiates those with developing hypothyroidism from other HPT-axis aging changes.

Validation of Serum Biomarkers That Complement CA19-9 in Detecting Early Pancreatic Cancer Using Electrochemiluminescent-Based Multiplex Immunoassays.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy; its early detection is critical for improving prognosis. Electrochemiluminescent-based multiplex immunoassays were developed with high analytical performance. All proteins were analyzed in sera of patients diagnosed with PDAC (n = 138), benign pancreatic conditions (111), and healthy controls (70). The clinical performance of these markers was evaluated individually or in combination for their complementarity to CA19-9 in detecting early PDAC. Logistic regression modeling including sex and age as cofactors identified a two-marker panel of CA19-9 and CA-125 that significantly improved the performance of CA19-9 alone in discriminating PDAC (AUC: 0.857 vs. 0.766), as well as early stage PDAC (0.805 vs. 0.702) from intraductal papillary mucinous neoplasm (IPMN). At a fixed specificity of 80%, the panel significantly improved sensitivities (78% vs. 41% or 72% vs. 59%). A two-marker panel of HE4 and CEA significantly outperformed CA19-9 in separating IPMN from chronic pancreatitis (0.841 vs. 0.501). The biomarker panels evaluated by assays demonstrated potential complementarity to CA19-9 in detecting early PDAC, warranting additional clinical validation to determine their role in the early detection of pancreatic cancer.

Cerebrospinal fluid in COVID-19 neurological complications: Neuroaxonal damage, anti-SARS-Cov2 antibodies but no evidence of cytokine storm.

OBJECTIVE: To study in cerebrospinal fluid (CSF) of COVID-19 subjects if a "cytokine storm" or neuroinflammation are implicated in pathogenesis of neurological complications. METHODS: Cross-sectional study of CSF neuroinflammatory profiles from 18 COVID-19 subjects with neurological complications categorized by diagnosis (stroke, encephalopathy, headache) and illness severity. COVID-19 CSF was compared with CSF from healthy, infectious and neuroinflammatory disorders and stroke controls (n = 82). Cytokines (IL-6, TNFα, IFNγ, IL-10, IL-12p70, IL-17A), inflammation and coagulation markers (high-sensitivity-C Reactive Protein [hsCRP], ferritin, fibrinogen, D-dimer, Factor VIII) and neurofilament light chain (NF-L), were quantified. SARS-CoV2 RNA and SARS-CoV2 IgG and IgA antibodies in CSF were tested with RT-PCR and ELISA. RESULTS: CSF from COVID-19 subjects showed absence of pleocytosis or specific increases in pro-inflammatory markers (IL-6, ferritin, or D-dimer). Although pro-inflammatory cytokines (IL-6, TNFα, IL-12p70) and IL-10 were increased in CSF of stroke COVID-19 subjects, a similar increase was observed in non-COVID-19 stroke subjects. Anti-SARS-CoV2 antibodies in CSF of COVID-19 subjects (77%) were observed despite no evidence of SARS-CoV2 viral RNA. CSF-NF-L was elevated in subjects with stroke and critical COVID-19 as compared to controls and other COVID-19 severity categories. CSF-hsCRP was present in all subjects with critical stages of COVID-19 (7/18) but only in 1/82 controls. CONCLUSION: The paucity of neuroinflammatory changes in CSF of COVID-19 subjects and lack of SARS-CoV2 RNA do not support the presumed neurovirulence of SARS-CoV2 or neuroinflammation in pathogenesis of neurological complications in COVID-19. The role of CSF SARS-CoV2 IgG antibodies and mechanisms of neuronal damage are still undetermined.

A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer.

Background: Current PSA-based tests used to detect prostate cancer (PCa) lack sufficient specificity, leading to significant overdetection and overtreatment. Our previous studies showed that serum fucosylated PSA (Fuc-PSA) and soluble TEK receptor tyrosine kinase (Tie-2) had the ability to predict aggressive (AG) PCa. Additional biomarkers are needed to address this significant clinical problem. Methods: A comprehensive Pubmed search followed by multiplex immunoassays identified candidate biomarkers associated with AG PCa. Subsequently, multiplex and lectin-based immunoassays were applied to a case-control set of sera from subjects with AG PCa, low risk PCa, and non-PCa (biopsy negative). These candidate biomarkers were further evaluated for their ability as panels to complement the prostate health index (phi) in detecting AG PCa. Results: When combined through logistic regression, two panel of biomarkers achieved the best performance: 1) phi, Fuc-PSA, SDC1, and GDF-15 for the detection of AG from low risk PCa and 2) phi, Fuc-PSA, SDC1, and Tie-2 for the detection of AG from low risk PCa and non-PCa, with noticeable improvements in ROC analysis over phi alone (AUCs: 0.942 vs 0.872, and 0.934 vs 0.898, respectively). At a fixed sensitivity of 95%, the panels improved specificity with statistical significance in detecting AG from low risk PCa (76.0% vs 56%, p=0.029), and from low risk PCa and non-PCa (78.2% vs 65.5%, p=0.010). Conclusions: Multivariate panels of serum biomarkers identified in this study demonstrated clinically meaningful improvement over the performance of phi, and warrant further clinical validation, which may contribute to the management of PCa.

Quality Improvement in Critical Value Delivery at a Tertiary Care Center.

BACKGROUND: Prompt notification of critical laboratory values to providers is essential for effective patient care. To improve the delivery of these critical values, a quality improvement project was initiated to determine the obstacles to prompt notification and to identify possible interventions to improve this process. METHODS: Critical value call logs were retrieved, and delivery time, patient location, test name, and call time were abstracted and analyzed. All critical values with delivery times greater than 60 min were reviewed by 2 authors for 1 representative month in both the pre- and postintervention period. RESULTS: Based on the results of the data review, a modification to the laboratory information system call center color-coded alerts was introduced to address delays attributable to the laboratory. The overall rate of calls greater than 60 min decreased from 3.4% ± 0.8% in the preintervention study period to 1.3 ± 0.3%, postintervention. The average number of values not delivered within 60 min decreased by 64% across all locations, following with an 82% decrease for values originating from inpatient locations, and a 39% decrease for outpatient values. CONCLUSIONS: Low complexity interventions to critical value callback protocols can significantly increase the efficacy of communication between the laboratory and providers.

Prevalence of Detectable Biotin in Five US Emergency Department Patient Cohorts.

BACKGROUND: The objective of this study was to estimate the prevalence of biotin supplementation in United States emergency department patients using a multi-site, geographically distributed sampling model. METHODS: Biotin was measured using an Abbott ARCHITECT Biotin research use only assay in 7118 emergency department patient serum or plasma samples from five US medical centers. Samples with biotin ≥10 ng/mL underwent additional LC-MS/MS confirmatory testing for biotin and its primary metabolites. The overall and site-specific prevalence of detectable biotin was determined using the screening assay while biotin speciation (i.e., prevalence of detectable metabolites) was determined using LC-MS/MS. RESULTS: Of 7118 samples screened, 291 (4.1%) had biotin ≥10 ng/mL and were considered positive. Across five medical centers, the fraction of positive samples ranged from 2.0% to 5.4%. The maximum biotin concentration observed was 355 ng/mL. Of the 285 positive screens that underwent additional LC-MS/MS testing, 89 (31%) showed detectable biotin, bisnorbiotin, and/or biotin sulfoxide. Biotin, bisnorbiotin, and biotinsulfoxide were detected in 82/89 (92.1%), 61/89 (68.5%), and 18/89 (20.2%) samples, respectively; biotin was detected in the absence of either metabolite in 18/89 (20.2%) samples. CONCLUSIONS: Using a screening assay, 4.1% of emergency department patient samples were found to be potentially susceptible to interference from biotin. Confirmatory testing showed detectable biotin and/or biotin metabolites in 31% of positive screens (1.3% overall). The prevalence of biotin ≥10 ng/mL varied 2-3-fold across US emergency department patient cohorts. Biotin metabolites were observed in 80% of samples confirmed to have detectable biotin species by LC-MS/MS, suggesting that rigorous assessments of assay susceptibility to biotin interference, often performed using in vitro studies, should consider the potential role of biotin metabolites present in vivo.

Cerebrospinal fluid in COVID-19 neurological complications: no cytokine storm or neuroinflammation.

BACKGROUND: Neurological complications occur in COVID-19. We aimed to examine cerebrospinal fluid (CSF) of COVID-19 subjects with neurological complications and determine presence of neuroinflammatory changes implicated in pathogenesis. METHODS: Cross-sectional study of CSF neuroinflammatory profiles from 18 COVID-19 subjects with neurological complications categorized by diagnosis (stroke, encephalopathy, headache) and illness severity (critical, severe, moderate, mild). COVID-19 CSF was compared with CSF from healthy, infectious and neuroinflammatory disorders and stroke controls (n=82). Cytokines (IL-6, TNFα, IFNγ, IL-10, IL-12p70, IL-17A), inflammation and coagulation markers (high-sensitivity-C Reactive Protein [hsCRP], ferritin, fibrinogen, D-dimer, Factor VIII) and neurofilament light chain (NF-L), were quantified. SARS-CoV2 RNA and SARS-CoV2 IgG and IgA antibodies in CSF were tested with RT-PCR and ELISA. RESULTS: CSF from COVID-19 subjects showed a paucity of neuroinflammatory changes, absence of pleocytosis or specific increases in pro-inflammatory markers or cytokines (IL-6, ferritin, or D-dimer). Anti-SARS-CoV2 antibodies in CSF of COVID-19 subjects (77%) were observed despite no evidence of SARS-CoV2 viral RNA. A similar increase of pro-inflammatory cytokines (IL-6, TNFα, IL-12p70) and IL-10 in CSF of COVID-19 and non-COVID-19 stroke subjects was observed compared to controls. CSF-NF-L was elevated in subjects with stroke and critical COVID-19. CSF-hsCRP was present almost exclusively in COVID-19 cases. CONCLUSION: The paucity of neuroinflammatory changes in CSF of COVID-19 subjects and lack of SARS-CoV2 RNA do not support the presumed neurovirulence of SARS-CoV2 or neuroinflammation in pathogenesis of neurological complications in COVID-19. Elevated CSF-NF-L indicates neuroaxonal injury in COVID-19 cases. The role of CSF SARS-CoV2 IgG antibodies is still undetermined. FUNDING: This work was supported by NIH R01-NS110122 and The Bart McLean Fund for Neuroimmunology Research.

Myocardial Injury in Severe COVID-19 Compared With Non-COVID-19 Acute Respiratory Distress Syndrome.

BACKGROUND: Knowledge gaps remain in the epidemiology and clinical implications of myocardial injury in coronavirus disease 2019 (COVID-19). We aimed to determine the prevalence and outcomes of myocardial injury in severe COVID-19 compared with acute respiratory distress syndrome (ARDS) unrelated to COVID-19. METHODS: We included intubated patients with COVID-19 from 5 hospitals between March 15 and June 11, 2020, with troponin levels assessed. We compared them with patients from a cohort study of myocardial injury in ARDS and performed survival analysis with primary outcome of in-hospital death associated with myocardial injury. In addition, we performed linear regression to identify clinical factors associated with myocardial injury in COVID-19. RESULTS: Of 243 intubated patients with COVID-19, 51% had troponin levels above the upper limit of normal. Chronic kidney disease, lactate, ferritin, and fibrinogen were associated with myocardial injury. Mortality was 22.7% among patients with COVID-19 with troponin under the upper limit of normal and 61.5% for those with troponin levels >10 times the upper limit of normal (P<0.001). The association of myocardial injury with mortality was not statistically significant after adjusting for age, sex, and multisystem organ dysfunction. Compared with patients with ARDS without COVID-19, patients with COVID-19 were older and had higher creatinine levels and less favorable vital signs. After adjustment, COVID-19-related ARDS was associated with lower odds of myocardial injury compared with non-COVID-19-related ARDS (odds ratio, 0.55 [95% CI, 0.36-0.84]; P=0.005). CONCLUSIONS: Myocardial injury in severe COVID-19 is a function of baseline comorbidities, advanced age, and multisystem organ dysfunction, similar to traditional ARDS. The adverse prognosis of myocardial injury in COVID-19 relates largely to multisystem organ involvement and critical illness.

Urinary glycoproteins associated with aggressive prostate cancer.

Background: There is an urgent need for the detection of aggressive prostate cancer. Glycoproteins play essential roles in cancer development, while urine is a noninvasive and easily obtainable biological fluid that contains secretory glycoproteins from the urogenital system. Therefore, here we aimed to identify urinary glycoproteins that are capable of differentiating aggressive from non-aggressive prostate cancer. Methods: Quantitative mass spectrometry data of glycopeptides from a discovery cohort comprised of 74 aggressive (Gleason score ≥8) and 68 non-aggressive (Gleason score = 6) prostate cancer urine specimens were acquired via a data independent acquisition approach. The glycopeptides showing distinct expression profiles in aggressive relative to non-aggressive prostate cancer were further evaluated for their performance in distinguishing the two groups either individually or in combination with others using repeated 5-fold cross validation with logistic regression to build predictive models. Predictive models showing good performance from the discovery cohort were further evaluated using a validation cohort. Results: Among the 20 candidate glycoproteins, urinary ACPP outperformed the other candidates. Urinary ACPP can also serve as an adjunct to serum PSA to further improve the discrimination power for aggressive prostate cancer (AUC= 0.82, 95% confidence interval 0.75 to 0.89). A three-signature panel including urinary ACPP, urinary CLU, and serum PSA displayed the ability to distinguish aggressive prostate cancer from non-aggressive prostate cancer with an AUC of 0.86 (95% confidence interval 0.8 to 0.92). Another three-signature panel containing urinary ACPP, urinary LOX, and serum PSA also demonstrated its ability in recognizing aggressive prostate cancer (AUC=0.82, 95% confidence interval 0.75 to 0.9). Moreover, consistent performance was observed from each panel when evaluated using a validation cohort. Conclusion: We have identified glycopeptides of urinary glycoproteins associated with aggressive prostate cancer using a quantitative mass spectrometry-based glycoproteomic approach and demonstrated their potential to serve as noninvasive urinary glycoprotein biomarkers worthy of further validation by a multi-center study.