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Open Access Research article

Bayesian predictors of very poor health related quality of life and mortality in patients with COPD

Olli-Pekka Ryynänen12*, Erkki J Soini345, Ari Lindqvist6, Maritta Kilpeläinen7 and Tarja Laitinen7

Author Affiliations

1 Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland

2 General Practice Unit, Kuopio University Hospital, Primary Health Care, Kuopio, Finland

3 ESiOR Oy, Kuopio, Finland

4 Department of Social and Health Management, University of Eastern Finland, Kuopio, Finland

5 Phoru, School of Pharmacy, University of Eastern Finland, Kuopio, Finland

6 Department of Medicine, Division of Pulmonary Medicine, Helsinki University Central Hospital.School of Pharmacy, University of Eastern Finland, Kuopio, Finland

7 Department of Pulmonary Medicine and Clinical Allergology, Turku University Central Hospital, Turku, Finland

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BMC Medical Informatics and Decision Making 2013, 13:34  doi:10.1186/1472-6947-13-34

Published: 7 March 2013

Abstract

Background

Chronic obstructive pulmonary disease (COPD) is associated with increased mortality and poor health-related quality of life (HRQoL) compared with the general population. The objective of this study was to identify clinical characteristics which predict mortality and very poor HRQoL among the COPD population and to develop a Bayesian prediction model.

Methods

The data consisted of 738 patients with COPD who had visited the Pulmonary Clinic of the Helsinki and Turku University Hospitals during 1995–2006. The data set contained 49 potential predictor variables and two outcome variables: survival (dead/alive) and HRQoL measured with a 15D instrument (very poor HRQoL < 0.70 vs. typical HRQoL ≥ 0.70).

In the first phase of model validation we randomly divided the material into a training set (n = 538), and a test set (n = 200). This procedure was repeated ten times in random fashion to obtain independently created training sets and corresponding test sets. Modeling was performed by using the training set, and each model was tested by using the corresponding test set, repeated in each training set. In the second phase the final model was created by using the total material and eighteen most predictive variables. The performance of six logistic regressions approaches were shown for comparison purposes.

Results

In the final model, the following variables were associated with mortality or very poor HRQoL: age at onset, cerebrovascular disease, diabetes, alcohol abuse, cancer, psychiatric disease, body mass index, Forced Expiratory Volume (FEV1) % of predicted, atrial fibrillation, and prolonged QT time in ECG. The prediction accuracy of the model was 77%, sensitivity 0.30, specificity 0.95, positive predictive value 0.68, negative predictive value 0.78, and area under the ROC curve 0.69. While the sensitivity of the model reminded limited, good specificity, moderate accuracy, comparable or better performance in classification and better performance in variable selection and data usage in comparison to the logistic regression approaches, and positive and negative predictive values indicate that the model has potential in predicting mortality and very poor HRQoL in COPD patients.

Conclusion

We developed a Bayesian prediction model which is potentially useful in predicting mortality and very poor HRQoL in patients with COPD.

Keywords:
Chronic obstructive pulmonary disease; Bayesian prediction; Bayesian methods; Prognosis; Mortality; Quality of life; Survival analysis