Research

Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery

BMJ 2010; 340 doi: 10.1136/bmj.b5606 (Published 19 January 2010)
Cite this as: BMJ 2010;340:b5606
  1. Martin Henriksson, research fellow in health economics12,
  2. Stephen Palmer, senior research fellow in health economics2,
  3. Ruoling Chen, honorary senior lecturer in epidemiology3,
  4. Jacqueline Damant, systematic reviewer3,
  5. Natalie K Fitzpatrick, project coordinator3,
  6. Keith Abrams, professor of medical statistics4,
  7. Aroon D Hingorani, professor of genetic epidemiology3,
  8. Ulf Stenestrand, associate professor of cardiology5,
  9. Magnus Janzon, cardiologist5,
  10. Gene Feder, professor of primary health care6,
  11. Bruce Keogh, professor of cardiac surgery3,
  12. Martin J Shipley, senior lecturer in medical statistics3,
  13. Juan-Carlos Kaski, professor of cardiovascular science7,
  14. Adam Timmis, professor of clinical cardiology8,
  15. Mark Sculpher, professor of health economics2,
  16. Harry Hemingway, professor of clinical epidemiology3
  1. 1Centre for Medical Technology Assessment, Linkoping University, Sweden
  2. 2Centre for Health Economics, University of York
  3. 3Department of Epidemiology and Public Health, University College London, London WC1E 6BT
  4. 4Department of Health Sciences, University of Leicester
  5. 5Department of Cardiology, HeartCentre, Linkoping University Hospital
  6. 6Academic Unit of Primary Health Care, University of Bristol
  7. 7Division of Cardiac and Vascular Sciences, St George’s, University of London
  8. 8Barts and the London Medical School
  1. Correspondence to: H Hemingway h.hemingway{at}ucl.ac.uk
  • Accepted 27 October 2009

Abstract

Objective To determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery.

Design Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared.

Data sources Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers.

Results The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10 000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of £20 000-£30 000 (€22 000-€33 000; $32 000-$48 000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was <£410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100 000 patients at an additional cost of £245 000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate.

Conclusion Evaluating the cost effectiveness of prognostic biomarkers is important even when effects at an individual level are small. Formal prioritisation of patients awaiting coronary artery bypass grafting using a routinely assessed biomarker (estimated glomerular filtration rate) along with simple, routinely collected clinical information was cost effective. Prioritisation strategies based on the prognostic information conferred by C reactive protein, which is not currently measured in this context, or a combination of C reactive protein and estimated glomerular filtration rate, is unlikely to be cost effective. The widespread practice of using only implicit or informal means of clinically ordering the waiting list may be harmful and should be replaced with formal prioritisation approaches.

Footnotes

  • Contributors: MS, MH, and SP contributed to the design and implementation of the decision analytical and cost effectiveness models. MH analysed the decision model. HH led and JD and NF carried out the systematic review. MJS generated the scaling factors for the meta-analysis and RC carried out the meta-analyses of the biomarkers. KA supervised the statistical aspects of the meta-analyses and decision analytical modelling. MH and HH wrote the manuscript; SP wrote much of the discussion. AT contributed clinical insights into the design and analysis of the meta-analyses and decision analytical modelling. AH advised on the scope of the biomarkers to include and the interpretation of the biomarker results in biological and clinical context. BK contributed to the design of the project at its inception. MJ obtained clearance for the use of the data from the Swedish Coronary Angiography and Angioplasty Registry and its linkage to the Swedish death and hospital admission registries, and US obtained permission to use the Swedish Coronary Angiography and Angioplasty Registry data for this project and helped interpret the data. J-CK made available for sharing the St George’s angina dataset, which allowed imputation of C reactive protein in the Swedish dataset and the calculation of adjustment factors. HH, MS, SP, AH, J-CK, BK, GF, and AT obtained grant funding. All authors approved the final version submitted for publication. HH is guarantor.

  • Funding: This study was funded by a grant from the Health Technology Assessment programme, HTA 05-40 and a National Institute for Health Research programme grant (RP-PG-0407-10314). The views expressed here are those of the authors and do not necessarily reflect those of the Department of Health. MJS is supported by the British Heart Foundation (RG/07/007). ADH is supported by a British Heart Foundation senior research fellowship (FS/05/125).

  • Competing interests: All authors have completed the unified competing interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare (1) no financial support for the submitted work from anyone other than their employer; (2) no financial relationships with commercial entities that might have an interest in the submitted work; (3) no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and (4) no non-financial interests that may be relevant to the submitted work.

  • Ethical approval: The analysis of the SCA Swedish Coronary Angiography and Angioplasty Registry AR database was approved by the Regionala etikprövningsnämnden, Linköping (M108-07).

  • Data sharing: Requests for access to the data are welcome.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

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