PMID- 34328435
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210825
IS  - 2291-9694 (Print)
IS  - 2291-9694 (Electronic)
VI  - 9
IP  - 7
DP  - 2021 Jul 30
TI  - Predicting Biologic Therapy Outcome of Patients With Spondyloarthritis: Joint 
      Models for Longitudinal and Survival Analysis.
PG  - e26823
LID - 10.2196/26823 [doi]
LID - e26823
AB  - BACKGROUND: Rheumatic diseases are one of the most common chronic diseases 
      worldwide. Among them, spondyloarthritis (SpA) is a group of highly debilitating 
      diseases, with an early onset age, which significantly impacts patients' quality 
      of life, health care systems, and society in general. Recent treatment options 
      consist of using biologic therapies, and establishing the most beneficial option 
      according to the patients' characteristics is a challenge that needs to be 
      overcome. Meanwhile, the emerging availability of electronic medical records has 
      made necessary the development of methods that can extract insightful information 
      while handling all the challenges of dealing with complex, real-world data. 
      OBJECTIVE: The aim of this study was to achieve a better understanding of SpA 
      patients' therapy responses and identify the predictors that affect them, thereby 
      enabling the prognosis of therapy success or failure. METHODS: A data mining 
      approach based on joint models for the survival analysis of the biologic therapy 
      failure is proposed, which considers the information of both baseline and 
      time-varying variables extracted from the electronic medical records of SpA 
      patients from the database, Reuma.pt. RESULTS: Our results show that being a 
      male, starting biologic therapy at an older age, having a larger time interval 
      between disease start and initiation of the first biologic drug, and being human 
      leukocyte antigen (HLA)-B27 positive are indicators of a good prognosis for the 
      biological drug survival; meanwhile, having disease onset or biologic therapy 
      initiation occur in more recent years, a larger number of education years, and 
      higher values of C-reactive protein or Bath Ankylosing Spondylitis Functional 
      Index (BASFI) at baseline are all predictors of a greater risk of failure of the 
      first biologic therapy. CONCLUSIONS: Among this Portuguese subpopulation of SpA 
      patients, those who were male, HLA-B27 positive, and with a later biologic 
      therapy starting date or a larger time interval between disease start and 
      initiation of the first biologic therapy showed longer therapy adherence. Joint 
      models proved to be a valuable tool for the analysis of electronic medical 
      records in the field of rheumatic diseases and may allow for the identification 
      of potential predictors of biologic therapy failure.
CI  - (c)Carolina Barata, Ana Maria Rodrigues, Helena Canhao, Susana Vinga, Alexandra M 
      Carvalho. Originally published in JMIR Medical Informatics 
      (https://medinform.jmir.org), 30.07.2021.
FAU - Barata, Carolina
AU  - Barata C
AUID- ORCID: 0000-0003-0187-8635
AD  - Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal.
AD  - Instituto de Telecomunicacoes, Instituto Superior Tecnico, Universidade de 
      Lisboa, Lisbon, Portugal.
FAU - Rodrigues, Ana Maria
AU  - Rodrigues AM
AUID- ORCID: 0000-0003-2046-8017
AD  - Comprehensive Health Research Center, NOVA Medical School, NOVA University of 
      Lisbon, Lisbon, Portugal.
AD  - EpiDoC Unit, The Chronic Diseases Research Centre, NOVA Medical School, NOVA 
      University of Lisbon, Lisbon, Portugal.
FAU - Canhao, Helena
AU  - Canhao H
AUID- ORCID: 0000-0003-1894-4870
AD  - Comprehensive Health Research Center, NOVA Medical School, NOVA University of 
      Lisbon, Lisbon, Portugal.
AD  - EpiDoC Unit, The Chronic Diseases Research Centre, NOVA Medical School, NOVA 
      University of Lisbon, Lisbon, Portugal.
FAU - Vinga, Susana
AU  - Vinga S
AUID- ORCID: 0000-0002-1954-5487
AD  - Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal.
AD  - Instituto de Engenharia de Sistemas e Computadores: Investigacao e 
      Desenvolvimento em Lisboa (INESC-ID), Instituto Superior Tecnico, Universidade de 
      Lisboa, Lisbon, Portugal.
AD  - Lisbon Unit for Learning and Intelligent Systems, Lisbon, Portugal.
FAU - Carvalho, Alexandra M
AU  - Carvalho AM
AUID- ORCID: 0000-0001-6607-7711
AD  - Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal.
AD  - Instituto de Telecomunicacoes, Instituto Superior Tecnico, Universidade de 
      Lisboa, Lisbon, Portugal.
AD  - Lisbon Unit for Learning and Intelligent Systems, Lisbon, Portugal.
LA  - eng
PT  - Journal Article
DEP - 20210730
PL  - Canada
TA  - JMIR Med Inform
JT  - JMIR medical informatics
JID - 101645109
PMC - PMC8367135
OTO - NOTNLM
OT  - data mining
OT  - drug survival
OT  - electronic medical records
OT  - joint models
OT  - medical records
OT  - rheumatic disease
OT  - spondyloarthritis
OT  - survival analysis
COIS- Conflicts of Interest: None declared.
EDAT- 2021/07/31 06:00
MHDA- 2021/07/31 06:01
PMCR- 2021/07/30
CRDT- 2021/07/30 12:17
PHST- 2020/12/29 00:00 [received]
PHST- 2021/04/23 00:00 [accepted]
PHST- 2021/04/13 00:00 [revised]
PHST- 2021/07/30 12:17 [entrez]
PHST- 2021/07/31 06:00 [pubmed]
PHST- 2021/07/31 06:01 [medline]
PHST- 2021/07/30 00:00 [pmc-release]
AID - v9i7e26823 [pii]
AID - 10.2196/26823 [doi]
PST - epublish
SO  - JMIR Med Inform. 2021 Jul 30;9(7):e26823. doi: 10.2196/26823.