Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery.
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...
| Publicado en: | Quality of Life Research Vol. 26; no. 9; pp. 2497 - 2506 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124517128&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124517128 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09629343 GPQ jtl: Quality of Life Research issn: 09629343 maglogo: N pubinfo: dt: Sep2017 vid: 26 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124517128 124517128 144190013 NLM28567601 124517128 10.1007/s11136-017-1599-0 NLM28567601 124517128 ppf: 2497 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery. aug: au: Gutacker, Nils Street, Andrew affil: Centre for Health Economics , University of York , Heslington YO10 5DD UK sug: subj: National Health Programs Standards Surgery, Operative Methods Quality of Life Psychosocial Factors Middle Age Aged Aged, 80 and Over Risk Factors Male Female Adolescence Adult Young Adult Human Funding Source Questionnaires Clinical Assessment Tools Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Adolescent: 13-18 years Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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