Predictors of Remission and Low Disease Activity State in Systemic Lupus Erythematosus: Data from a Multiethnic,

Manuel F Ugarte-Gil1,2, Daniel Wojdyla3,4, Guillermo J Pons-Estel3,4

  • 1From the Department of Rheumatology, Hospital Guillermo Almenara Irigoyen, EsSalud; Universidad Científica del Sur, Lima, Peru; Grupo Latino Americano De Estudio de Lupus (GLADEL), Rosario, Argentina; Department of Autoimmune Diseases, Hospital Clinic, Barcelona, Spain; Centro Regional de Enfermedades Autoinmunes y Reumáticas (CREAR), Grupo Oroño, Sanatorio Parque, Rosario, Santa Fe, Argentina; Servicio de Reumatología, Hospital Clinic, Barcelona, Spain; Sección de Reumatología, Servicio de Clínica Médica, Hospital Italiano de Buenos Aires, Instituto Universitario, Escuela de Medicina Hospital Italiano and Fundación Dr. Pedro M. Catoggio para el Progreso de la Reumatología, Buenos Aires; Servicio de Reumatología, Hospital Privado, Centro Medico de Córdoba, Córdoba, Argentina; Rheumatology Division, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo; Disciplina de Reumatologia, Escola Paulista de Medicina/UNIFESP, Hospital São Paulo, Universidade Federal de São Paulo, São Paulo; Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas; Faculdade de Medicina, Universidade Federal de Goiás, Goiânia, Brazil; Clínica Saludcoop 104 Jorge Piñeros Corpas and Hospital San Juan de Dios, Universidad Nacional de Colombia, Bogotá, Colombia; Hospital del Salvador, Facultad de Medicina, Universidad de Chile, Santiago, Chile; Servicio de Reumatología, Centro de Investigaciones Médico Quirúrgicas-CIMEQ, Havana, Cuba; Centro de Investigación Clínica de Morelia SC, Morelia, Michoacán; Reumatología, Centro Médico ABC, Ciudad de México, México; Universidad Nacional Mayor de San Marcos, Lima, Peru; Hospital Central de San Cristóbal, San Cristóbal, Venezuela; Department of Medicine, Division of Clinical Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA. manuel_ugarte@yahoo.com.

Abstract

Related Concept Videos

Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
406
First Order Systems01:21

First Order Systems

First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
426
Second Order systems I01:20

Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
591
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.7K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.2K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
198