Vol.18 No.6

Original Article

Logistic regression models for predicting physical and mental health-related quality of life in rheumatoid arthritis patients

Authors

Gholam Hossein Alishiri3 , Noushin Bayat3 , Ali Fathi Ashtiani1 , Seyed Abbas Tavallaii1 , Shervin Assari2 , Yashar Moharamzad2

  • Behavioral Sciences Research Center, Baqiyatallah Medical Sciences University, Vanak Square, Mollasadra Avenue, Box Number: 19945/581, 1435915371, Tehran, Iran
  • Clinical Research Unit, Baqiyatallah Medical Sciences University, Tehran, Iran
  • Department of Rheumatology, Baqiyatallah Medical Sciences University, Tehran, Iran
Received:

18 February 2008

Accepted:

12 May 2008

Published online:

21 June 2008

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Abstract

The aim of this work was to develop two logistic regression models capable of predicting physical and mental health related quality of life (HRQOL) among rheumatoid arthritis (RA) patients. In this cross-sectional study which was conducted during 2006 in the outpatient rheumatology clinic of our university hospital, Short Form 36 (SF-36) was used for HRQOL measurements in 411 RA patients. A cutoff point to define poor versus good HRQOL was calculated using the first quartiles of SF-36 physical and mental component scores (33.4 and 36.8, respectively). Two distinct logistic regression models were used to derive predictive variables including demographic, clinical, and psychological factors. The sensitivity, specificity, and accuracy of each model were calculated. Poor physical HRQOL was positively associated with pain score, disease duration, monthly family income below 300 US$, comorbidity, patient global assessment of disease activity or PGA, and depression (odds ratios: 1.1; 1.004; 15.5; 1.1; 1.02; 2.08, respectively). The variables that entered into the poor mental HRQOL prediction model were monthly family income below 300 US$, comorbidity, PGA, and bodily pain (odds ratios: 6.7; 1.1; 1.01; 1.01, respectively). Optimal sensitivity and specificity were achieved at a cutoff point of 0.39 for the estimated probability of poor physical HRQOL and 0.18 for mental HRQOL. Sensitivity, specificity, and accuracy of the physical and mental models were 73.8, 87, 83.7% and 90.38, 70.36, 75.43%, respectively. The results show that the suggested models can be used to predict poor physical and mental HRQOL separately among RA patients using simple variables with acceptable accuracy. These models can be of use in the clinical decision-making of RA patients and to recognize patients with poor physical or mental HRQOL in advance, for better management.

Key words

Rheumatoid arthritis - Quality of life - Prediction model