Development and validation of a simple HPLC-MS/MS method for the quantification of methylmalonic acid in human serum without a derivatization step.

A simple and rapid HPLC-MS/MS analytical method was developed and validated for the determination of methylmalonic acid (MMA) in human serum without a derivatization step. Serum samples (200 µl) were pretreated using a simple method based on ultrafiltration using a VIVASPIN 500 ultrafiltration column. Chromatographic separation was achieved on a Luna Omega C18 column with a PS C18 precolumn guard by gradient elution using 0.1% (v/v) formic acid in water (mobile phase A) and 0.5% (v/v) formic acid in acetonitrile (mobile phase B) at a flow rate of 0.2 ml min-1 . The total run time of the analysis was 4.5 min. Negative electrospray ionization and multiple reaction monitoring mode were used. The lower limit of detection and lower limit of quantification for MMA were determined to be 13.6 and 42.3 nmol L-1 , respectively. The developed method enabled the quantification of MMA in a wide linear range of 42.3-4230 nmol L-1 with a correlation coefficient of 0.9991.

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

OBJECTIVES: SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results. METHODS: In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1st 2020 and January 31st 2021 were retrospectively analyzed regarding clinical findings. Variables included demographics, laboratory values and specific symptoms. Three different models were evaluated using Bayesian logistic regression. RESULTS: The initial dataset contained 4,076 patients. Overall sensitivity and specificity of RAT was 62.3% and 97.6%. 2,997 cases with negative RAT results (FN: 120; true negative: 2,877; reference: RT-PCR) underwent further evaluation after removal of cases with missing data. The best-performing model for predicting FN RAT results containing 10 variables yielded an area under the curve of 0.971. Sensitivity, specificity, PPV and NPV for 0.09 as cut-off value (probability for FN RAT) were 0.85, 0.99, 0.7 and 0.99. CONCLUSION: FN RAT results can be accurately identified through ten routinely available variables. Implementation of a prediction model in addition to RAT testing in clinical care can provide decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.