Can we predict an incomplete capsule endoscopy? Results of a multivariate analysis using a logistic regression model

Rev Esp Enferm Dig. 2022 Jun;114(6):329-334. doi: 10.17235/reed.2021.7320/2020.

Abstract

Background and aims: small bowel capsule endoscopy (SBCE) does not reach the cecum within the battery lifetime in approximately 15-35 % of patients. Incomplete examinations result in diagnostic delays and increase the economic burden. To date, risk factors for incomplete examinations have been described with contradictory results. The aims of this study were to analyze the rate and identify risk factors for incomplete examinations, excluding capsule retentions, in a large cohort of patients.

Methods: data from 1,894 consecutive SBCE examinations performed from January 2009 to December 2015 were analyzed. Variables recorded included demographics, past medical and surgical history, biochemical parameters and procedure characteristics. The rate of incomplete examinations, excluding capsule retentions, was calculated and a multivariate analysis using a logistic regression model was performed in order to evaluate predictive factors.

Results: the incidence of incomplete examinations, excluding capsule retentions, was 10.1 % (187 incomplete procedures). The multivariate analysis showed that age > 65 years, gastric transit time > 41 minutes and SB transit time > 286 minutes are predictive factors for incomplete examinations, increasing the probability of this event by 199 % (OR: 1.99; 95 % CI: 1.34-2.95), 260 % (OR: 2.60; 95 % CI: 1.72-3.93) and 352 % (OR: 3.52; 95 % CI: 2.26-5.48), respectively.

Conclusions: age > 65 years, gastric transit time > 41 minutes and SB transit time > 286 minutes are predictive factors for incomplete examinations excluding capsule retentions. Both age and gastric transit time events are known before the procedure ends. Therefore, pharmacologic or endoscopic measures may be taken into account to avoid incomplete examinations.

MeSH terms

  • Aged
  • Capsule Endoscopy* / methods
  • Gastrointestinal Transit
  • Humans
  • Logistic Models
  • Multivariate Analysis
  • Retrospective Studies