The remarkable effectivity of current antiviral therapies has led to consider the elimination of hepatitis C virus (HCV) infection. However, HCV infection is highly underdiagnosed; therefore, a global strategy for eliminating it requires improving the effectiveness of HCV diagnosis to identify hidden cases. In this study, we assessed the effectiveness of a protocol for HCV diagnosis based on viral load reflex testing of anti-HCV antibody-positive patients (known as one-step diagnosis) by analyzing all diagnostic tests performed by a central laboratory covering an area of 1.5 million inhabitants in Barcelona, Spain, before (83,786 cases) and after (45,935 cases) the implementation of the reflex testing protocol. After its implementation, the percentage of anti-HCV-positive patients with omitted HCV RNA determination remarkably decreased in most settings, particularly in drug treatment centers and primary care settings, where omitted HCV RNA analyses had absolute reductions of 76.4 and 20.2%, respectively. In these two settings, the percentage of HCV RNA-positive patients identified as a result of reflex testing accounted for 55 and 61% of all anti-HCV-positive patients. HCV RNA results were provided in a mean of 2 days. The presence of HCV RNA and age of ≥65 years were significantly associated with advanced fibrosis, assessed using the serological FIB-4 index (odds ratio [OR], 5.92; 95% confidence interval [CI], 3.4 to 10.4). The implementation of viral load reflex testing in a central laboratory is feasible and significantly increases the diagnostic effectiveness of HCV infections, while allowing the identification of underdiagnosed cases.
Objectives Administration of busulfan is extending rapidly as a part of a conditioning regimen in patients undergoing hematopoietic stem cell transplantation (HSCT). Monitoring blood plasma levels of busulfan is recommended for identifying the optimal dose in patients and for minimizing toxicity. The aim of this research was to validate a simple, rapid, and cost-effective analytical tool for measuring busulfan in human plasma that would be suitable for routine clinical use. This novel tool was based on liquid chromatography coupled to mass spectrometry. Methods Human plasma samples were prepared using a one-step protein precipitation protocol. These samples were then resolved by isocratic elution in a C18 column. The mobile phase consisted 2 mM ammonium acetate and 0.1% formic acid dissolved in a 30:70 ratio of methanol/water. Busulfan-d8 was used as the internal standard. Results The run time was optimized at 1.6 min. Standard curves were linear from 0.03 to 5 mg/L. The coefficient of variation (%CV) was less than 8%. The accuracy of this method had an acceptable bias that fell within 85–115% range. No interference between busulfan and the interfering compound hemoglobin, lipemia, or bilirubin not even at the highest concentrations of compound was tested. Neither carryover nor matrix effects were observed using this method. The area under the plasma drug concentration-time curves obtained for 15 pediatric patients who received busulfan therapy prior to HSCT were analyzed and correlated properly with the administered doses. Conclusions This method was successfully validated and was found to be robust enough for therapeutic drug monitoring in a clinical setting.
Resumen Objetivos Durante la pandemia causada por el virus SARS-CoV-2 ha surgido la necesidad de identificar variables predictivas que permitan una rápida identificación de aquellos pacientes que desarrollarán la COVID-19 severa para una rápida intervención. Este estudio ha desarrollado y validado un modelo capaz de realizar un pronóstico de severidad de la COVID-19. Métodos A partir de datos analíticos, demográficos y comorbilidades de pacientes visitados en el Servicio de Urgencias con sintomatología compatible de COVID-19, se ha realizado un estudio descriptivo y comparativo de pacientes con PCR-RT positiva y negativa para SARS-CoV-2 y de pacientes con enfermedad COVID-19 moderada y severa. La cohorte COVID-19 positiva ha servido para el desarrollo de un modelo de regresión logística. Resultados Se han incluido 410 pacientes COVID positivo (303 con enfermedad moderada y 107 con enfermedad severa) y 81 COVID negativo. Las variables predictivas del modelo son: lactato deshidrogenasa, proteína C reactiva, proteínas totales, urea y plaquetas. La calibración interna mostró un área bajo la curva ROC (AUC) de 0,88 (IC95%: 0,85–0,92), con un porcentaje de clasificaciones correctas del 85,2% a un valor de corte de 0,5. La validación externa (100 pacientes) obtuvo un AUC de 0,79 (IC95%: 0,71–0,89), con un 73% de clasificaciones correctas. Conclusiones El modelo predictivo desarrollado permite seleccionar desde el Servicio de Urgencias, con una única extracción de sangre y con magnitudes habituales en un Laboratorio Clínico, aquellos pacientes que con mayor probabilidad desarrollarán COVID-19 severa, proporcionando una importante herramienta para la planificación y la toma de decisiones clínicas.
Objectives The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity. Methods A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort. Results The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85–0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71–0.89), with a rate of correct classifications of 73%. Conclusions The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.
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