Table 7

Hierarchical regression modelling of adenoma detection

(1) Unadjusted model
 Sensitivity76.1%(72.8% to 79.1%)
 Specificity77.5%(71.0% to 82.8%)
Sensitivity (%)(95% CI)
(2) Model adjusted by polyp characteristics and combinations
 None6.5(3.6% to 11.2%)
 T60.0(31.5% to 83.0%
 O68.0(44.5% to 85.0%)
 B56.8(33.2% to 77.7%)
 OB94.9(80.8% to 98.8%)
 TO96.7(76.0% to 99.6%)
 TB97.3(80.3% to 99.7%)
 TOB99.9(97.8% to 100.0%)
(3) Model adjusted by number of polyp characteristics*
 None6.5(3.6% to 11.4%)
 1 of TOB62.3(42.7% to 78.6%)
 2 of TOB96.3(88.5% to 98.9%)
 3 of TOB99.9(97.9% to 100.0%)
  • Hierarchical regression models of polyps nested within patients (see Methods section).

  • Polyp categories:

  • None denotes a polyp without T, O or B observed.

  • T=Thick brown vessels surrounding white structures.

  • O=Oval, tubular or branched white structures surrounded by brown vessels.

  • B=Browner relative to background.

  • *Model 3 was rerun with two or three polyp characteristics combined giving a test sensitivity of 99.4% (95% CI 98.2% to 99.8%) when two or more characteristics were present.