A clinical score can predict associated deep infiltrating endometriosis before surgery for an endometrioma  

Oleh:

Pillet, M.C. Lafay ; Huchon, C. ; Santulli, P. ; Borghese, B. ; Chapron, C. ; Fauconnier, A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Human Reproduction vol. 29 no. 08 (Aug. 2014), page 1666-1676.
Topik: deep endometriosis; endometrioma; chronic pelvic pain; examination; clinical prediction rule

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Perpustakaan FK Nomor Panggil: H07.K.2014.02 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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STUDY QUESTION Is it possible to detect associated deep infiltrating endometriosis (DIE) before surgery for patients operated on for endometriomas using a preoperative clinical symptoms questionnaire? SUMMARY ANSWER A diagnostic score of DIE associated with endometriomas using four clinical symptoms defined a high-risk group where the probability of DIE was 88% and a low-risk group with a 10% probability of DIE. WHAT IS KNOWN ALREADY Many clinical symptoms are already known to be associated with DIE but they have not yet been used to build a clinical prediction model. STUDY DESIGN, SIZE, DURATION We built a diagnostic score of DIE based on a case control study of 326 consecutive patients operated on for an endometrioma between January 2005 and October 2011: 164 had associated DIE (DIE+) and 162 had no DIE (DIE-). We derived the score on a training sample obtained from a random selection of 2/3 of the population (211 patients, 101 DIE+, 110 DIE-), and validated the results on the remaining third (115 patients, 63 DIE+, 52 DIE-). The gold standard for the diagnosis of DIE was based on surgical exploration and histological diagnosis. PARTICIPANTS/MATERIALS, SETTING, METHODS Participants were consecutive patients aged 18–42 years who underwent surgery for an endometrioma with histological confirmation and complete treatment of their endometriotic lesions: data for these women were extracted from a prospective database including a standardized preoperative questionnaire. On the training dataset, variables associated with DIE in a univariate analysis were introduced in a multiple logistic regression and selected by a backward stepwise procedure and a Jackknife procedure. A diagnostic score of DIE was built with the scaled/rounded coefficients of the multiple regression. Two cut-off values delimitated a high and a low risk group, and their diagnostic accuracy was tested on the validation dataset. MAIN RESULTS AND THE ROLE OF CHANCE Four variables were independently associated with DIE: visual analogue scale of gastro-intestinal symptoms =5 or of deep dyspareunia >5 (adjusted diagnostic odds ratio (aDOR) = 6.0, 95% confidence interval (CI) [2.9–12.1]), duration of pain greater than 24 months (aDOR = 3.8, 95% CI [1.9–7.7]), severe dysmenorrhoea (defined as the prescription of the oral contraceptive pill for the treatment of a primary dysmenorrhoea or the worsening of a secondary dysmenorrhoea) (aDOR = 3.8, 95% CI [1.9–7.6]) and primary or secondary infertility (aDOR = 2.5, 95% CI [1.2–4.9]). The sum of these variables weighted by their rounded/scaled coefficients constituted the score ranging from 0 to 53. A score <13 defined a low-risk group where the probability of DIE was 10% (95% CI [7–15] with a sensitivity of 95% (95% CI [89–98]) and a negative likelihood ratio of 0.1 (95% CI [0.0–0.3]). A score =35 defined a high-risk group where the probability of DIE was 88% (95% CI [83–92%]), with a specificity of 94% (95% CI [87–97]), and a positive likelihood ratio of 8.1 (95% CI [3.9–17.0]). The performance of the score was confirmed on the validation dataset with 11% of DIE+ patients having a score <13 (sensibility: 95%) and 90% of DIE+ patients having a score =35 (specificity: 94%). LIMITATION, REASONS FOR CAUTION This study was performed in a department specialized in DIE management. Score accuracy could be different in less specialized centres. WIDER IMPLICATIONS OF THE FINDINGS This score could have a major clinical impact on the time of diagnosis, the management of DIE and could reduce the cost of investigations by helping to identify high-risk patients, while preserving the quality of care.

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