Bayesian prediction of treatment outcome in anorexia nervosa : A preliminary study
: : A preliminary study
: Veera Pohjolainen, Olli-Pekka Ryynänen, Pirjo Räsänen, Risto P. Roine, Salla Koponen, Hasse Karlsson
Publisher: Informa Healthcare
: 2015
Nordic Journal of Psychiatry
: NORDIC JOURNAL OF PSYCHIATRY
: NORD J PSYCHIAT
: 69
: 3
: 210
: 215
: 6
: 0803-9488
: 1502-4725
DOI: https://doi.org/10.3109/08039488.2014.962612
Background: Knowledge of the prognostic factors predicting treatment outcome in anorexia nervosa (AN) measured with health-related quality of life (HRQoL) is limited. Aims: We performed a novel statistical analysis to identify factors predicting treatment outcome in AN. Methods: 39 patients entering treatment of an ICD-10-defined AN completed the 15D HRQoL survey, the Eating Disorder Inventory (EDI) and a questionnaire evaluating self reported health status and eating habits before and 2 years after the start of treatment. The analysis was based on a Bayesian approach, which allows analyses of small data sets, and was performed using a naive Bayes classifier. Results: An impaired follow-up HRQoL score was associated with three baseline risk factors: low self-reported vitality, high scores in eating control and a poor reported health status. Low baseline body mass index (BMI) and a high score in the eating dimension of the 15D predicted low follow-up BMI. Conclusions: In our preliminary study, we identified a set of variables predicting poor HRQoL in AN. An effort to treat these symptoms effectively in the beginning of AN treatment may influence the outcome.
Anorexia nervosa, Bayesian prediction, Bayes theorem, Eating disorders, Health-related quality of life (HRQoL)