Jùlia Sanders Vegara
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jùlia Sanders Vegara has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Ana Eyo González
Servicio de Bioquímica Clínica, Hospital Universitario San Agustín, 33401 Avilés, Asturias, Spain
1 shared publication - Claudia González García
Facultad de Medicina, Universidad de Oviedo, 33006 Oviedo, Asturias, Spain
1 shared publication - Cristina Alberdi García del Castillo
Servicio de Análisis Clínicos, Hospital Universitario de Cabueñes, 33394 Gijón, Asturias, Spain
1 shared publication - Daniel Al Kassam Martínez
Servicio de Análisis Clínicos, Hospital Universitario de Cabueñes, 33394 Gijón, Asturias, Spain
1 shared publication - Esther González García
Department of Hematology, Hospital de Cabueñes, 33203 Gijón, Spain
1 shared publication - Francisco Javier Cepeda-Piorno
Cabueñes University Hospital (SESPA), Research Institute of the Principality of Asturias (ISPA), Protein Section, Clinical Laboratory , Gijón (Asturias)
1 shared publication
Latest Publications
- Open Access
- 21 February 2025
- Published by MDPI AG in Diagnostics
- Vol. 15 (5) , 525
Novel Approach to Rule-Out Unnecessary Urine Bence Jones Protein Testing: A Serum Free Light Chain Algorithm
Background/Objectives: Bence Jones proteins (BJPs) are monoclonal immunoglobulin free light chains (FLCs) that appear in the urine of patients with plasma cell disorders, including multiple myeloma (MM), Waldenström’s macroglobulinemia (WM), or light chain amyloidosis (AL). Their presence can provide valuable information about disease progression and treatment efficacy. These proteins are typically detected through a 24-h urine collection, as recommended by clinical guidelines. However, this method can be inconvenient for both patients and laboratory personnel due to its time-consuming nature and the potential for collection errors. We propose an algorithm based on serum FLC (sFLC) to rule out the presence of BJPs and diminish the need for urine testing. Methods: A retrospective data analysis of 268 serum and urine samples from 44 patients with MM was performed, and cutoffs were established to predict BJP absence: total urine protein (0.115 g/L), sFLC κ/λ ratio (>0.82 λ monoclonality and <1.99 κ monoclonality), and difference of involved–uninvolved FLC (dFLC; <11.93 mg/L). A subsequent algorithm validation was performed in 716 samples from patients who underwent the same testing in routine 2023 other laboratory activity. Results: The validation of these cutoffs to rule out the presence of BJP showed that, if the protocol based on the sFLC κ/λ ratio and dFLC had been applied, 42% of the urine studies would have been avoided, achieving a sensitivity of 93.9% and a false negative rate of 6.11%. Conclusions: We propose a laboratory work protocol that would allow for the avoidance of almost half of the 24-h urine studies based on sFLC measurement, a faster and more objective alternative to urine analysis for screening out the presence of BJP, with a good sensitivity and a low false negative rate.
- 21 February 2025
- Published by MDPI AG in Diagnostics
- Vol. 15 (5) , 525
Open AccessNovel Approach to Rule-Out Unnecessary Urine Bence Jones Protein Testing: A Serum Free Light Chain Algorithm
Background/Objectives: Bence Jones proteins (BJPs) are monoclonal immunoglobulin free light chains (FLCs) that appear in the urine of patients with plasma cell disorders, including multiple myeloma (MM), Waldenström’s macroglobulinemia (WM), or light chain amyloidosis (AL). Their presence can provide valuable information about disease progression and treatment efficacy. These proteins are typically detected through a 24-h urine collection, as recommended by clinical guidelines. However, this method can be inconvenient for both patients and laboratory personnel due to its time-consuming nature and the potential for collection errors. We propose an algorithm based on serum FLC (sFLC) to rule out the presence of BJPs and diminish the need for urine testing. Methods: A retrospective data analysis of 268 serum and urine samples from 44 patients with MM was performed, and cutoffs were established to predict BJP absence: total urine protein (0.115 g/L), sFLC κ/λ ratio (>0.82 λ monoclonality and <1.99 κ monoclonality), and difference of involved–uninvolved FLC (dFLC; <11.93 mg/L). A subsequent algorithm validation was performed in 716 samples from patients who underwent the same testing in routine 2023 other laboratory activity. Results: The validation of these cutoffs to rule out the presence of BJP showed that, if the protocol based on the sFLC κ/λ ratio and dFLC had been applied, 42% of the urine studies would have been avoided, achieving a sensitivity of 93.9% and a false negative rate of 6.11%. Conclusions: We propose a laboratory work protocol that would allow for the avoidance of almost half of the 24-h urine studies based on sFLC measurement, a faster and more objective alternative to urine analysis for screening out the presence of BJP, with a good sensitivity and a low false negative rate.