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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
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Primed Mycobacterial Uveitis PMU as a Model for Post-Infectious Uveitis
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A joint model for dynamic prediction in uveitis.

Mia Klinten Grand1,2, Koenraad Arndt Vermeer2, Tom Missotten3

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.

Statistics in Medicine
|December 21, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint model for predicting uveitis (eye inflammation) and visual acuity. The model allows for dynamic, updated predictions based on patient history, improving patient care.

Keywords:
clustersdynamic predictioninterval censoringjoint modelmultistate model

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Area of Science:

  • Ophthalmology
  • Biostatistics
  • Medical Informatics

Background:

  • Uveitis is recurrent eye inflammation impacting visual acuity.
  • The Rotterdam Eye Hospital has collected extensive uveitis patient data since 2000.
  • Existing models lack dynamic prediction capabilities for uveitis progression.

Purpose of the Study:

  • To develop a joint model for predicting uveitis inflammation and visual acuity.
  • To enable dynamic predictions updated with patient disease history.
  • To reverse the typical relationship where longitudinal outcomes influence event history.

Main Methods:

  • A two-state reversible multistate model for inflammation with interval-censored transitions.
  • Correlated log-normal frailties to address within-eye and within-patient correlation.
  • A linear mixed model for longitudinal visual acuity data.
  • A two-stage fitting procedure for the joint model.

Main Results:

  • The proposed joint model successfully integrates inflammation and visual acuity data.
  • Dynamic predictions can be generated and updated throughout patient follow-up.
  • Simulation studies confirmed the performance of the developed method.
  • The model extends previous work by accommodating multistate outcomes.

Conclusions:

  • The novel joint model offers enhanced dynamic prediction for uveitis patients.
  • This approach provides a more comprehensive understanding of uveitis progression.
  • The reversed relationship between visual acuity and inflammation offers new insights.