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Quality of Life Research
Published in: Quality of Life Research 9/2017

Open Access 01-09-2017

Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery

Authors: Nils Gutacker, Andrew Street

Published in: Quality of Life Research | Issue 9/2017

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Abstract

Purpose

The English NHS has mandated the routine collection of health-related quality of life (HRQoL) data before and after surgery, giving prospective patient information about the likely benefit of surgery. Yet, the information is difficult to access and interpret because it is not presented in a lay-friendly format and does not reflect patients’ individual circumstances. We set out a methodology to generate personalised information to help patients make informed decisions.

Methods

We used anonymised, pre- and postoperative EuroQol-5D-3L (EQ-5D) data for over 490,000 English NHS patients who underwent primary hip or knee replacement surgery or groin hernia repair between April 2009 and March 2016. We estimated linear regression models to relate changes in EQ-5D utility scores to patients’ own assessment of the success of surgery, and calculated from that minimally important differences for health improvements/deteriorations. Classification tree analysis was used to develop algorithms that sort patients into homogeneous groups that best predict postoperative EQ-5D utility scores.

Results

Patients were classified into between 55 (hip replacement) to 60 (hernia repair) homogeneous groups. The classifications explained between 14 and 27% of variation in postoperative EQ-5D utility score.

Conclusions

Patients are heterogeneous in their expected benefit from surgery, and decision aids should reflect this. Large administrative datasets on HRQoL can be used to generate the required individualised predictions to inform patients.
Footnotes
1
We did not include varicose vein patients since the number of complete data points is substantially lower and a large number of patients report pre-operative EQ-5D-3L health profiles as 11111, i.e. there is no capacity to improve.
 
2
In doing so, we generated an update to their MID estimates obtained from a much smaller sample.
 
3
In some cases, missing information was collected but not released by the HSCIC as part of their publicly available dataset to ensure that patients cannot be re-identified. See also FN2.
 
Literature
1.
Bansback, N., Trenaman, L., Bryan, S., & Johnson, J. (2015). ‘Using routine patient reported outcome measures to enhance patient decision making: a proof of concept study (published conference abstract)’. Quality of Life Research, 24(Suppl 1), A1080.
2.
Basu, A., & Meltzer, D. O. (2007). ‘Value of information on preference heterogeneity and individualized care’. Medical Decision Making, 27(2), 112–127.CrossRef
3.
Brooks, R. (1996). EuroQol: The current state of play. Health Policy, 37, 53–72.CrossRef
4.
Browne, J. P., van der Meulen, J. H., Lewsey, J. D., Lamping, D. L., & Black, N. (2010). Mathematical coupling may account for the association between baseline severity and minimally important difference values. Journal of Clinical Epidemiology, 63(8), 865–874.CrossRef
5.
Department of Health. (2008). Guidance on the routine collection of patient reported outcome measures (PROMs). London: The Stationary Office.
6.
Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35, 1095–1108.CrossRef
7.
Feng, Y., Devlin, N., & Herdman, M. (2015). Assessing the health of the general population in England: How do the three- and five-level versions of EQ-5D compare? Health and Quality of Life Outcomes, 13, 171.CrossRef
8.
Fuller, R., Dudley, N., & Blacktop, J. (2001). Risk communication and older people - understanding of probability and risk information by medical inpatients aged 75 years and older. Age Ageing, 30, 473–476.CrossRef
9.
Gutacker, N., Bojke, C., Daidone, S., Devlin, N., & Street, A. (2013). Hospital variation in patient- reported outcomes at the level of EQ-5D dimensions: evidence from England. Medical Decision Making, 33(6), 804–818.CrossRef
10.
Gutacker, N., Street, A., Gomes, M., & Bojke, C. (2015). Should English healthcare providers be penalised for failing to collect patient-reported outcome measures (PROMs)? Journal of the Royal Society of Medicine, 108(8), 304–316.CrossRef
11.
Hibbard, J. H., & Peters, E. (2003). Supporting informed consumer health care decisions: Data presentation approaches that facilitate the Use of Information in choice. Annual Review of Public Health, 24(1), 413–433.CrossRef
12.
Hildon, Z., Allwood, D., & Black, N. (2012a). Impact of format and content of visual display of data on comprehension, choice and preference: a systematic review. International Journal for Quality in Health Care, 24(1), 55–64.CrossRef
13.
Hildon, Z., Allwood, D., & Black, N. (2012b). Making data more meaningful: Patients’ views of the format and content of quality indicators comparing health care providers. Patient Education and Counseling, 88(2), 298–304.CrossRef
14.
Hildon, Z., Neuburger, J., Allwood, D., van der Meulen, J., & Black, N. (2012). Clinicians’ and patients’ views of metrics of change derived from patient reported outcome measures (PROMs) for comparing providers’ performance of surgery. BMC Health Services Research, 12, 171.CrossRef
15.
Huang, E. S., A. G. Nathan, J. M. Cooper, S. M. Lee, N. Shin, P. M. John, W. Dale, N. F. Col, D. O. Meltzer and M. H. Chin (online first). ‘Impact and Feasibility of Personalized Decision Support for Older Patients with Diabetes: A Pilot Randomized Trial’. Medical Decision Making. [Epub ahead of print]
16.
Janssen, M., Pickard, A., Golicki, D., Gudex, C., Niewada, M., Scalone, L., et al. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study. Quality of Life, 22(7), 1717–1727.CrossRef
17.
Lipkovich, I., Dmitrienko, A., & B’Agostino, R, Sr. (2017). Tutorial in biostatistics: Data-driven subgroup identification and analysis in clinical trials. Statistics in Medicine, 36(1), 136–196.CrossRef
18.
Mann, R., Brazier, J., & Tsuchiya, A. (2009). A comparison of patient and general population weightings of EQ-5D dimensions. Health Economics, 18, 363–372.CrossRef
19.
McCarthy, I. M. (2016). Eliminating composite bias in treatment effects estimates: Applications to quality of life assessment. Journal of Health Economics, 50, 47–58.CrossRef
20.
McCarthy, I. M. (2015). Putting the patient in patient reported outcomes: A robust methodology for health outcomes assessment. Health Economics, 24(12), 1588–1603.CrossRef
21.
Nemes, S., O. Rolfson and G. Garellick (online first). ‘Development and validation of a shared decision-making instrument for health-related quality of life one year after total hip replacement based on quality registries data’. Journal of Evaluation in Clinical Practice. doi:10.​1111/​jep.​12603
22.
Parkin, D., Rice, N., & Devlin, N. (2010). Statistical analysis of EQ-5D profiles: Does the use of value sets bias inference? Medical Decision Making, 30, 556–565.CrossRef
23.
Peters, E., Dieckmann, N., Dixon, A., Hibbard, J. H., & Mertz, C. K. (2007). Less is more in presenting quality information to consumers. Medical Care Research and Review, 64(2), 169–190.CrossRef
24.
Price, M., Cameron, R., & Butow, P. (2007). Communicating risk information: the in uence of graphical display format on quantitative information perception - accuracy, comprehension and preferences. Patient Education and Counseling, 69, 121–128.CrossRef
25.
Rogowski, W., Payne, K., Schnell-Inderst, P., Manca, A., Rochau, U., Jahn, B., et al. (2015). Concepts of ‘personalization’ in personalized medicine: implications for economic evaluation. Pharmacoeconomics, 33(1), 49–59.CrossRef
26.
Schleidgen, S., Klinger, C., Bertram, T., Rogowski, W., & Marckmann, G. (2013). What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Medical Ethics, 14, 55.CrossRef
27.
Stacey, D., Léegarée, F., Col, N., Bennett, C., Barry, M., & Eden, K. (2014). Decision aids for people facing health treatment or screening decisions. Cochrane Database of Systematic Reviews, 1, CD001431.
28.
Steyerberg, E. W. (2009). Clinical prediction models: A practical approach to development, validation, and updating., Statistics for biology and health New York: Springer.CrossRef
29.
Timmermans, D., Molewijk, B., Stiggelbout, A., & Kievit, J. (2004). Different formats for communicating surgical risks to patients and the effect on choice of treatment. Patient Education and Counseling, 54(3), 255–263.CrossRef
30.
Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A., et al. (1984). Classification and Regression Trees. Abingdon: Taylor & Francis.
Metadata
Title
Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery
Authors
Nils Gutacker
Andrew Street
Publication date
01-09-2017
Publisher
Springer International Publishing
Published in
Quality of Life Research / Issue 9/2017
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-017-1599-0

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