Endoscopy 2020; 52(S 01): S198-S199
DOI: 10.1055/s-0040-1704620
ESGE Days 2020 ePoster Podium presentations
Friday, April 24, 2020 15:30 – 16:00 Do we need BIG DATA for quality assurance? ePoster Podium 4
© Georg Thieme Verlag KG Stuttgart · New York

ACCEPTABILITY OF KEY PERFORMANCE INDICATORS (KPI) IN THE NATIONAL ENDOSCOPY DATABASE (NED) AUTOMATED PERFORMANCE REPORTS TO IMPROVE QUALITY OUTCOMES TRIAL (APRIQOT), A DELPHI PROCESS

NED APRIQOT Delphi Panel
J Catlow
1   Newcastle University, Department Gastroenterology, Stockton on Tees, UK
,
L Sharp
2   Newcastle University, Population Health Sciences, Newcastle Upon Tyne, UK
,
M Rutter
1   Newcastle University, Department Gastroenterology, Stockton on Tees, UK
› Author Affiliations
Further Information

Publication History

Publication Date:
23 April 2020 (online)

 
 

    Aims APRIQOT uses NED to provide endoscopists feedback on colonic detection KPI. Traditional adenoma detection rate is dependent on unavailable histological data. Our aim was to gain expert consensus on which available KPI are acceptable to endoscopists.

    Methods A Delphi panel of UK expert endoscopists was recruited online, purposively sampling to match clinical background to census data.

    Panellists interacted using an online form. In round one we provided a summary and acceptability statement for each KPI, participants rated agreement with a five-point Likert scale and free-text comments. Responses were analysed anonymously. In subsequent rounds participants reviewed all graded consensus statements and comments. Statements were accepted with ≥80% consensus (Agree and Strongly Agree) or redrafted. HRA ethical approval was granted within APRIQOT. Rounds ran January to April 2019.

    Results

    Tab. 1

    to ePP228 KPI definitions, accepted statements and consensus.

    KPI

    Definition

    Statement Accepted

    Round Consensus Reached

    Agree/Neutral/Disagree

    Polyp Detection Rate

    Procedures where at least one polyp is detected, displayed as a percentage.

    Polyp detection rate (PDR) is an acceptable detection measure in colonoscopy in the absence of a link to histological polyp data. Procedure adjusted polypectomy rate may be used to account for variables which may affect polyp detection, such as the procedure indication and patient demographics.

    2

    95%/0%/5%

    Mean Number Polyps

    Number of polyps detected, displayed as a rate per 100 colonoscopies.

    Using mean number of polyps (MNP) detected is an acceptable detection measure in colonoscopy. Procedure adjusted polypectomy rate may be used to account for variables which may affect polyp detection, such as the procedure indication and patient demographics.

    3

    81%/14%/5%

    Proximal Polypectomy Rate

    Procedure where at least one polyp is removed proximal to the splenic flexure.

    Proximal polypectomy rate (PPR) is an acceptable secondary measure to the primary KPI. Procedure adjusted polypectomy rate may be used to account for variables which may affect polyp detection, such as the procedure indication and patient demographics.

    3

    86%/9%/5%

    We recruited 21 UK expert endoscopists. Twelve were female, 48% gastroenterology background, 29% nursing, 14% surgical and 9% trainees. All statements reached consensus after three rounds.

    The panel agreed that each KPI may be adjusted for polyp associated ‘case mix’ variables, such as indication, making KPI ‘more acceptable’. Benefits of encompassing non-adenomatous polyps were highlighted versus ‘gaming’ and distal hyperplastic polyp over-reporting.

    Mean number of polyps (MNP) reached consensus after discussing reduction of the ‘one and done’ phenomenon and using a cap of five polyps/colon to mitigate skew from polyposis.

    Proximal polypectomy rate (PPR) was accepted as a secondary ‘tool to improve right sided … detection’ and could reduce ‘gaming’, despite concerns around contraindications to polypectomy and endoscopists polypectomy skills.

    Conclusions All adjusted KPI were accepted, MNP was selected for trial with robust data to model case-mix.


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