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A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer

Wong, S-L; Ricketts, K; Royle, G; Williams, M; Mendes, R; (2018) A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer. BMC Health Services Research , 18 , Article 278. 10.1186/s12913-018-3029-6. Green open access

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Abstract

BACKGROUND: Outcomes for patients in UK with locally advanced non-small cell lung cancer (LA NSCLC) are amongst the worst in Europe. Assessing outcomes is important for analysing the effectiveness of current practice. However, data quality is inconsistent and regular large scale analysis is challenging. This project investigates the use of routine healthcare datasets to determine progression free survival (PFS) and overall survival (OS) of patients treated with primary radical radiotherapy for LA NSCLC. METHODS: All LA NSCLC patients treated with primary radical radiotherapy in a 2 year period were identified and paired manual and routine data generated for an initial pilot study. Manual data was extracted information from hospital records and considered the gold standard. Key time points were date of diagnosis, recurrence, death or last clinical encounter. Routine data was collected from various data sources including, Hospital Episode Statistics, Personal Demographic Service, chemotherapy data, and radiotherapy datasets. Relevant event dates were defined by proxy time points and refined using backdating and time interval optimization. Dataset correlations were then tested on key clinical outcome indicators to establish if routine data could be used as a reliable proxy measure for manual data. RESULTS: Forty-three patients were identified for the pilot study. The manual data showed a median age of 67 years (range 46- 89 years) and all patients had stage IIIA/B disease. Using the manual data, the median PFS was 10.78 months (range 1.58-37.49 months) and median OS was 16.36 months (range 2.69-37.49 months). Based on routine data, using proxy measures, the estimated median PFS was 10.68 months (range 1.61-31.93 months) and estimated median OS was 15.38 months (range 2.14-33.71 months). Overall, the routine data underestimated the PFS and OS of the manual data but there was good correlation with a Pearson correlation coefficient of 0.94 for PFS and 0.97 for OS. CONCLUSIONS: This is a novel approach to use routine datasets to determine outcome indicators in patients with LA NSCLC that will be a surrogate to analysing manual data. The ability to enable efficient and large scale analysis of current lung cancer strategies has a huge potential impact on the healthcare system.

Type: Article
Title: A methodology to extract outcomes from routine healthcare data for patients with locally advanced non-small cell lung cancer
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s12913-018-3029-6
Publisher version: http://doi.org/10.1186/s12913-018-3029-6
Language: English
Additional information: © The Author(s). 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: LA NSCLC, Outcomes, Routine datasets
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10047326
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