Prozone masks elevated SARS-CoV-2 antibody level measurements.

We report a prozone effect in measurement of SARS-CoV-2 spike protein antibody levels from an antibody surveillance program. Briefly, the prozone effect occurs in immunoassays when excessively high antibody concentration disrupts the immune complex formation, resulting in a spuriously low reported result. Following participant inquiries, we observed anomalously low measurement of SARS-CoV-2 spike protein antibody levels using the Roche Elecsys® Anti-SARS-CoV-2 S immunoassay from participants in the Texas Coronavirus Antibody Research survey (Texas CARES), an ongoing prospective, longitudinal antibody surveillance program. In July, 2022, samples were collected from ten participants with anomalously low results for serial dilution studies, and a prozone effect was confirmed. From October, 2022 to March, 2023, serial dilution of samples detected 74 additional cases of prozone out of 1,720 participants' samples. Prozone effect may affect clinical management of at-risk populations repeatedly exposed to SARS-CoV-2 spike protein through multiple immunizations or serial infections, making awareness and mitigation of this issue paramount.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.

Rationale & Objective: Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression. Study Design: Retrospective observational study. Setting & Participants: The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2. Predictors: Patient demographic and laboratory characteristics. Outcomes: Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years. Analytical Approach: Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline. Results: The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex. Limitations: The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model. Conclusions: Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression. Plain-Language Summary: Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

Prevalence of BRCA mutations in an unselected population of triple-negative breast cancer.

BACKGROUND: This study assessed BRCA1 and BRCA2 mutation prevalence in an unselected cohort of patients with triple-negative breast cancer (BC). METHODS: One hundred ninety-nine patients were enrolled. Triple negativity was defined as <1% estrogen and progesterone staining by immunohistochemistry and HER-2/neu not overexpressed by fluorescence in situ hybridization. Having given consent, patients had BRCA1 and BRCA2 full sequencing and large rearrangement analysis. Mutation prevalence was assessed among the triple-negative BC patients and the subset of patients without a family history of breast/ovarian cancer. Independent pathological review was completed on 50 patients. RESULTS: Twenty-one deleterious BRCA mutations were identified--13 in BRCA1 and 8 in BRCA2 (prevalence, 10.6%). In 153 patients (76.9%) without significant family history (first-degree or second-degree relatives with BC aged <50 years or ovarian cancer at any age), 8 (5.2%) mutations were found. By using prior National Comprehensive Cancer Network (NCCN) guidelines recommending testing for triple-negative BC patients aged <45 years, 4 of 21 mutations (19%) would have been missed. Two of 21 mutations (10%) would have been missed using updated NCCN guidelines recommending testing for triple-negative BC patients aged <60 years. CONCLUSIONS: The observed mutation rate was significantly higher (P = .0005) than expected based on previously established prevalence tables among patients unselected for pathology. BRCA1 mutation prevalence was lower, and BRCA2 mutation prevalence was higher, than previously described. Additional mutation carriers would have met new NCCN testing guidelines, underscoring the value of the updated criteria. Study data suggest that by increasing the age limit to 65 years, all carriers would have been identified.