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Details

Autor(en) / Beteiligte
Titel
Prediction of patency capsule retention in known Crohn's disease patients by using magnetic resonance imaging
Ist Teil von
  • Gastrointestinal endoscopy, 2016-01, Vol.83 (1), p.182-187
Ort / Verlag
United States: Elsevier Inc
Erscheinungsjahr
2016
Quelle
MEDLINE
Beschreibungen/Notizen
  • Background and Aims Evaluation of small-bowel patency is recommended before swallowing video capsule endoscopy to prevent capsule retention. This study aimed to evaluate the ability of magnetic resonance enterography (MRE) to predict patency capsule (PC) retention in patients with Crohn’s disease and to identify the most predictive imaging features for retention. Methods Fifty-seven patients prospectively underwent MRE and PC. Two radiologists predicted PC retention. Interrater reliability was determined by using Cohen’s κ coefficient. The sensitivity, specificity, and positive and negative predictive values were calculated for the predictions. Evaluation of the imaging features was done using the t test and receiver-operating characteristics; t-tests were also performed on the clinical parameters. Results The κ value for interrater reliability was 0.58. The sensitivity, specificity, PPV, and NPV for the predictions by the 2 radiologists were 92.3%, 59%, 40%, 96.3%, and 100%, 52.3%, 38.2%, 100%, respectively. The maximal stricture length (9.7 ± 3.66 cm vs 7.0 ± 3.08 cm, P  = .04) and the number of prestenotic dilations (1.9 ± 1.07 vs 1.0 ± 1.38, P  = .03) were associated with PC retention. The area under the receiver-operating characteristic curves was 0.69 for the maximal stricture length and 0.751 for the number of prestenotic dilations. The phenotype of the disease was the only clinical parameter significantly correlated with PC retention. Conclusions MRE has a high NPV and sensitivity for PC retention. When capsule retention is suggested by MRE, PC should be performed before the video capsule endoscopy examination. The maximal stricture length and the number of prestenotic dilations were found to be the most predictive imaging features for PC retention.

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