Using a graph of the abnormal predictive value versus the positive predictive value for the determination of outlier laboratories in the National Health Service cervical screening programme
Article first published online: 9 NOV 2010
© 2010 Blackwell Publishing Ltd
Volume 21, Issue 6, pages 379–388, December 2010
How to Cite
Blanks, R. G. (2010), Using a graph of the abnormal predictive value versus the positive predictive value for the determination of outlier laboratories in the National Health Service cervical screening programme. Cytopathology, 21: 379–388. doi: 10.1111/j.1365-2303.2010.00771.x
- Issue published online: 9 NOV 2010
- Article first published online: 9 NOV 2010
- Accepted for publication 20 April 2010
- cervical screening;
- positive predictive value;
- abnormal predictive value;
- performance measures
R.G. Blanks Using a graph of the abnormal predictive value versus the positive predictive value for the determination of outlier laboratories in the National Health Service cervical screening programme.
Objective: The positive predictive value (PPV) for the detection of cervical intraepithelial neoplasia (CIN) grade 2 or worse of referral to colposcopy from moderate dyskaryosis or worse (equivalent to high-grade squamous intraepithelial lesion or worse) is a standard performance measure in the National Health Service cervical screening programme. The current target is to examine ‘outlier’ laboratories with PPVs outside the 10th–90th percentile, which automatically identifies 20% of laboratories for further investigation. A more targeted method of identifying outliers may be more useful.
Methods: A similar measure to the PPV, the abnormal predictive value (APV), can be defined as the predictive value for CIN2 or worse for referrals from borderline (includes atypical squamous and glandular cells) and mild dyskaryosis (equivalent to low-grade squamous intraepithelial lesion) combined. A scatter plot of the APV versus the PPV can be produced (the APV-PPV diagram). Three kinds of ‘outlier’ can be defined on the diagram to help determine laboratories with unusual data. These are termed a true outlier value (TOV) or an extreme value (EV) for either PPV or APV, or a residual extreme value (REV) from the APV-PPV best line of fit.
Results: Using annual return information for 2007/8 from 124 laboratories, two were defined as having EVs for PPV (both had a relatively low PPV of 62%). For APV, four laboratories were considered to have EVs of 34%, 34%, 34% and 4% and one was considered to be a TO with an APV of 45%. Five were identified as REV laboratories, although three of these were also identified as having extreme or outlier values, leaving two that had not been identified by the other methods. A total of eight (6%) laboratories were therefore identified as meriting further investigation using this methodology.
Conclusions: The method proposed could be a useful alternative to the current method of identifying outliers. Slide exchange studies between the identified laboratories, particularly those at opposing ends of the diagram, or other further investigations of such laboratories, may be instructive in understanding why such variation occurs, and could therefore potentially, lead to improvements in the national programme.