Using Patient-Reported Outcome Measures (PROMs) Routinely: An Example in the Context of Elective Shoulder Surgery
Jill Dawson*, 1, 2, Katherine Rogers1, Helen Doll1, Ray Fitzpatrick1, Cushla Cooper3, Andrew Carr3
Identifiers and Pagination:Year: 2010
First Page: 42
Last Page: 52
Publisher Id: TOEPIJ-3-42
Article History:Received Date: 02/02/2010
Revision Received Date: 26/05/2010
Acceptance Date: 27/05/2010
Electronic publication date: 16/7/2010
Collection year: 2010
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper evaluates the usefulness of a set of PROMs data collected prospectively, in a relational database, over several years. The data were collected as part of routine practice, to audit patients undergoing shoulder surgery.
Data, rendered anonymous, which included all upper limb surgical procedures and all outcome questionnaires (including Oxford Shoulder Score, OSS) data, were downloaded, prepared and analysed to produce the shoulder surgery study population. Details of procedures and questionnaires were merged by shoulder and analysed for completeness and accuracy. Pre- and post-operative OSS scores, with repeated observations from individual patients, were analysed using maximum likelihood mixed effects linear regression models.
Data preparation and cleaning was intensive. The final dataset contained 2110 questionnaires representing 815 surgical procedures and 755 patients. In relation to procedures, only 538 (66%) had a pre- and post-operative OSS score: 78 (9.5%) a pre-operative OSS only, 199 (24.4%) a post-operative OSS only, and 31 (3.8%) no OSS completed. OSS questionnaires had been completed in varying numbers and at varying times per procedure. There was a considerable amount of ‘missing’ data that was not missing completely at random. Missing data had a significant influence on OSS scores.
In the absence of a research question (eg. exploratory research, descriptive audits, registers), a reason for collecting PROMs data should be stipulated and methods of data collection and storage standardised. Poor data cannot be ‘fixed’ in statistical analysis; statistical advice should be sought during the planning stages.