PMID- 36628221
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230112
IS  - 1943-8141 (Print)
IS  - 1943-8141 (Electronic)
IS  - 1943-8141 (Linking)
VI  - 14
IP  - 12
DP  - 2022
TI  - Development of a nomogram to predict medication nonadherence risk in patients 
      with rheumatoid arthritis.
PG  - 9057-9065
AB  - OBJECTIVES: Poor adherence among patients with chronic diseases including 
      inflammatory rheumatic diseases (IRDs) is a complex and serious global health 
      care problem. This study aimed to develop an intelligent nomogram using 
      retrospectively collected patient clinical data for predicting nonadherence to 
      biologic treatment in rheumatoid arthritis (RA) patients. METHODS: The clinical 
      characteristics of 102 RA patients were collected from outpatients and inpatients 
      at the Orthopedic Departments of Ningxia General Hospital of Ningxia Medical 
      University and Ningxia Hui Autonomous Region People's Hospital from October 2020 
      to September 2021. Adherence was evaluated using the proportion of treatment days 
      covered within 6 months as the outcome event. A least absolute shrinkage and 
      selection operator (LASSO) regression analysis was used to identify risk 
      predictors, and then multivariate logistic regression analysis was applied to 
      construct the risk prediction model. Furthermore, the nomogram was plotted by 
      multivariable logistic regression. RESULTS: Among the 102 patients analyzed, 43 
      patients did not adhere to biologic therapy for various reasons. LASSO regression 
      analysis identified age, sex, education level, disease activity, monthly income, 
      medical insurance, and adverse drug reactions as the significant risk predictors. 
      By incorporating these factors, the nomogram was plotted which showed good 
      discrimination, calibration, and clinical value. The C-index was 0.759 (95% CI: 
      0.665-0.853), and the area under the receiver operating characteristic (ROC) 
      curve was 0.7416 with a good calibration ability. Decision curve analysis showed 
      that the prediction effect of this model could benefit about 75% of the patients 
      without compromising the interests of other patients. CONCLUSIONS: This nomogram 
      could help medical staff identify patients with higher risk of nonadherence 
      early, so that intervention measures can be taken in time.
CI  - AJTR Copyright (c) 2022.
FAU - Liu, Zige
AU  - Liu Z
AD  - School of Clinical Medicine, Guangxi Medical University Nanning 530000, Guangxi, 
      China.
FAU - Ge, Rui
AU  - Ge R
AD  - Department of Radiology, Rich Hospital of Nantong University Nantong 226000, 
      Jiangsu, China.
FAU - Yang, Tianxiang
AU  - Yang T
AD  - Department of Orthopedic Surgery, General Hospital of Ningxia Medical University 
      Yinchuan 750004, Ningxia, China.
FAU - Zhang, Jinning
AU  - Zhang J
AD  - Department of Orthopedic Surgery, General Hospital of Ningxia Medical University 
      Yinchuan 750004, Ningxia, China.
FAU - Zhang, Bowen
AU  - Zhang B
AD  - Department of Orthopedic Surgery, General Hospital of Ningxia Medical University 
      Yinchuan 750004, Ningxia, China.
FAU - Zhang, Chen
AU  - Zhang C
AD  - Department of Orthopedic Surgery, General Hospital of Ningxia Medical University 
      Yinchuan 750004, Ningxia, China.
FAU - Song, Guorui
AU  - Song G
AD  - Department of Orthopedic Surgery, General Hospital of Ningxia Medical University 
      Yinchuan 750004, Ningxia, China.
FAU - Chen, Desheng
AU  - Chen D
AD  - Department of Orthopedic Surgery, People's Hospital of Ningxia Hui Autonomous 
      Region Yinchuan 750004, Ningxia, China.
LA  - eng
PT  - Journal Article
DEP - 20221215
PL  - United States
TA  - Am J Transl Res
JT  - American journal of translational research
JID - 101493030
PMC - PMC9827297
OTO - NOTNLM
OT  - R software
OT  - Rheumatoid arthritis
OT  - chronic diseases
OT  - nomogram
OT  - nonadherence
COIS- None.
EDAT- 2023/01/12 06:00
MHDA- 2023/01/12 06:01
PMCR- 2022/12/15
CRDT- 2023/01/11 01:48
PHST- 2022/06/13 00:00 [received]
PHST- 2022/12/05 00:00 [accepted]
PHST- 2023/01/11 01:48 [entrez]
PHST- 2023/01/12 06:00 [pubmed]
PHST- 2023/01/12 06:01 [medline]
PHST- 2022/12/15 00:00 [pmc-release]
PST - epublish
SO  - Am J Transl Res. 2022 Dec 15;14(12):9057-9065. eCollection 2022.