Body Image Concern Inventory (BICI) for identifying patients with BDD seeking rhinoplasty: using a Persian (Farsi) version

Overview

People with body dysmorphic disorder (BDD) are preoccupied with a flaw others barely see, and rhinoplasty rarely helps them. The authors tested whether a short self-report questionnaire, the Body Image Concern Inventory translated into Persian, can screen for BDD among people seeking rhinoplasty in Iran, comparing it with a psychiatrist's structured interview.

Sections of note

• The surgeon rated defect severity: 1 for no defect or one defect, 2 for more than one. The 117 subjects scoring 1 were studied.
• Each completed demographic questions and the Persian BICI; a psychiatrist blind to the score interviewed each using a DSM-IV based semistructured tool.
• Validity 85%; internal reliability (Cronbach's alpha) 0.90.
• Best cutoff by ROC analysis: total score 42 (sensitivity 93.5%, specificity 80.8%).
• Area under the ROC curve 91.4% (95% CI 86 to 97%, P < 0.0001); agreement with interview by kappa 61.7% (P < 0.001).
• Odds ratio for a BDD diagnosis with a score of 42 or more: 47.7 (95% CI 43 to 52).
• BDD prevalence in the group: 12.2%.

What it means for a patient

• About 1 in 8 people seeking rhinoplasty for a minimal defect met criteria for BDD in this sample.
• A brief questionnaire can flag most of those patients before surgery, though about 1 in 5 without BDD would also screen positive.
• Single center, Persian-language version only; the abstract does not report how the surgeons acted on the results.

Why this paper matters

BDD screening before rhinoplasty is widely recommended but rarely done with a validated tool. This is the first use of the BICI in a rhinoplasty setting. Whether screening changes surgical decisions or outcomes is not tested.

Terms

• Body dysmorphic disorder (BDD): a psychiatric condition of preoccupation with a slight or imagined defect in appearance.
• Body Image Concern Inventory (BICI): a 19-item self-report questionnaire on appearance concern.
• Sensitivity: the share of true cases a test detects.
• Specificity: the share of people without the condition a test correctly clears.
• ROC analysis: a method for choosing the score cutoff that best balances sensitivity and specificity.
• Cronbach's alpha: a measure of whether a questionnaire's items consistently measure the same thing.