Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery
- Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery
- Henriksson M, Palmer S, Chen R, Damant J, Fitzpatrick NK, Abrams K, Hingorani AD, Stenestrand U, Janzon M, Feder G, Keogh B, Shipley MJ, Kaski JC, Timmis A, Sculpher M, Hemingway H
- Publication date:
- First page:
- Link to pubmed:
- Publication type:
- Free text
Objective: To determine the effectiveness and cost effectivenessof using information from circulating biomarkers to inform theprioritisation process of patients with stable angina awaitingcoronary artery bypass graft surgery. Design: Decision analytical model comparing four prioritisationstrategies without biomarkers (no formal prioritisation, twourgency scores, and a risk score) and three strategies basedon a risk score using biomarkers: a routinely assessed biomarker(estimated glomerular filtration rate), a novel biomarker (Creactive protein), or both. The order in which to perform coronaryartery bypass grafting in a cohort of patients was determinedby each prioritisation strategy, and mean lifetime costs andquality adjusted life years (QALYs) were compared. Data sources: Swedish Coronary Angiography and Angioplasty Registry(9935 patients with stable angina awaiting coronary artery bypassgrafting and then followed up for cardiovascular events afterthe procedure for 3.8 years), and meta-analyses of prognosticeffects (relative risks) of biomarkers. Results: The observed risk of cardiovascular events while onthe waiting list for coronary artery bypass grafting was 3 per10 000 patients per day within the first 90 days (184 eventsin 9935 patients). Using a cost effectiveness threshold of £20000-£30 000 (22 000-33 000; $32 000-$48 000) per additionalQALY, a prioritisation strategy using a risk score with estimatedglomerular filtration rate was the most cost effective strategy(cost per additional QALY was <£410 compared with theOntario urgency score). The impact on population health of implementingthis strategy was 800 QALYs per 100 000 patients at an additionalcost of £245 000 to the National Health Service. The prioritisationstrategy using a risk score with C reactive protein was associatedwith lower QALYs and higher costs compared with a risk scoreusing estimated glomerular filtration rate. Conclusion: Evaluating the cost effectiveness of prognostic biomarkersis important even when effects at an individual level are small.Formal prioritisation of patients awaiting coronary artery bypassgrafting using a routinely assessed biomarker (estimated glomerularfiltration rate) along with simple, routinely collected clinicalinformation was cost effective. Prioritisation strategies basedon the prognostic information conferred by C reactive protein,which is not currently measured in this context, or a combinationof C reactive protein and estimated glomerular filtration rate,is unlikely to be cost effective. The widespread practice ofusing only implicit or informal means of clinically orderingthe waiting list may be harmful and should be replaced withformal prioritisation approaches.
Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery Henriksson M, Palmer S, Chen R, Damant J, Fitzpatrick NK, Abrams K et al. BMJ 2010; 340:b5606.