HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Jaroslav Stark William G. Williams Permission Requests Google Scholar Articles by Gallivan, S. Articles by Williams, W. G. PubMed Articles by Gallivan, S. Articles by Williams, W. G. Related Collections Cardiac - other

Dead reckoning: can we trust estimates of mortality rates in clinical databases?

^a Clinical Operational Research Unit, University College, London, UK
^b Cardiothoracic Unit, Great Ormond Street Hospital for Children NHS Trust, Great Ormond Street, London, UK
^c Cardiovascular Surgery Database (CVSDB) of the Division of Cardiac Surgery at the Hospital for Sick Children, Toronto, Ontario, Canada
^d Hospital for Sick Children and the University of Toronto, Ontario, Canada

Received 5 September 2007; received in revised form 15 November 2007; accepted 20 November 2007.

^_* Corresponding author. Address: Clinical Operational Research Unit, University College, 4 Taviton Street, London WC1H OBT, UK. Tel.: +44 207 6794508/9; fax: +44 207 813 2814. (Email: s.gallivan{at}ucl.ac.uk).

Objectives: Databases almost invariably contain some errors and improvements to the quality of recorded data are costly. We sought to assess the extent to which given levels of error in a clinical database can lead to misleading mortality rates being derived. Methods: We deliberately seeded a large database concerning congenital heart surgery involving over 17,600 operations, which we assumed to be error free, with errors at known rates of 0–20%. The effects of three different types of random error were explored: data omission, outcome miscoding (alive or dead) and the miscoding of procedures. For each error type, we compared the mortality rates calculated from the ‘seeded’ database to those calculated from the pristine database. Results: Outcome miscoding typically results in overestimated mortality rates which for low-risk procedures may well give estimates over double the true value. Random data omission has relatively little effect. If procedure types are miscoded, procedure-specific mortality estimates for high-risk operations tend to be underestimates and those for low-risk operations overestimates. A mathematical model developed to examine these effects accurately forecasted the results of such error-seeding experiments. Software to implement this model is available free of charge on the Internet. Conclusion: Even small levels of data error can substantially affect the accuracy of mortality rate estimates, especially for low-risk operations. Such inaccuracy could lead to misleading analysis of institutional and individual surgeons’ results. Our results suggest that caution is warranted in interpreting the mortality estimates derived from clinical databases. Our analysis goes beyond the realms of surgical mortality and concerns all adverse events whose frequency is rare.

Key Words: Mortality • Errors • Simulation