The implications of competing risks and direct treatment disutility in cardiovascular disease and osteoporotic fracture: risk prediction and cost effectiveness analysis

Bruce Guthrie, Gabriel Rogers, Shona Livingstone, Dan Morales, Peter Donnan, Sarah Davis, Ji Hee Youn, Rob Hainsworth, Alexander Thompson, Katherine Payne

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background
Clinical guidelines commonly recommend preventive treatments for people above a risk-threshold. Therefore, decision-makers must have faith in risk-prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (the hassle of taking treatments). We explored these issues using two case-studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates.

Objectives
Externally validate three risk-prediction tools (QRISK3, QRiskLifetime, QFracture); derive and internally validate new risk-prediction tools for cardiovascular disease (CCRISK-CCI) and fracture (CFracture) accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; examine the effect of competing risks and direct treatment disutility on cost effectiveness of preventive treatments.

Design, participants, main outcome measures, datasources
Discrimination and calibration of risk-prediction models (Clinical Practice Research Datalink participants aged 25-84 [cardiovascular] and 30-99 [fractures]); direct treatment disutility elicited in online stated-preference surveys (people with/without experience of statins/bisphosphonates); costs and quality-adjusted life-years from decision-analytic modelling (updated models used in NICE decision-making).

Results
CCRISK-CCI has similar excellent discrimination to QRISK3 (Harrell’s C=0.864 vs 0.865 [women]; 0.819 vs 0.834 [men]). CCRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk ≥10%) are younger using QRiskLifetime than QRISK3, and have fewer observed events in 10-year follow-up (4.0% vs 11.9% [women]; 4.3% vs 10.8% [men]).

QFracture underpredicts fractures, owing to underascertainment of events in its derivation. However, there is major overprediction in people aged 85-99 and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts in older people.

In a time-trade-off exercise (n=879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, 0.067. Inconvenience also influenced preferences in best-worst scaling (n=631).

Updated cost-effectiveness analysis generates more quality-adjusted life-years in people with below-average cardiovascular risk and fewer in people with above-average risk. If people experience disutility whenever taking statins, the cardiovascular risk-threshold at which benefits outweigh harms rises with age (≥8% 10-year risk at 40; ≥38% at 80). Assuming everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net-harmful at all levels of risk.

Limitations
Treating data as missing-at-random is a strong assumption in risk-prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the past two decades is challenging. Validating lifetime risk-prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. We could not update all inputs to cost-effectiveness models.

Conclusions
Ignoring competing mortality in risk-prediction overestimates the risk of cardiovascular events and fracture, especially in older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost effectiveness of these preventive interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue this is best addressed in individual-level shared decision-making.
This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme (15/12/22) and will be published in full in XXX Journal; Vol. XX, No. XX. See the NIHR Journals Library website for further project information.
Original languageEnglish
Number of pages308
JournalHealth Services and Delivery Research
Volume12
Issue number4
DOIs
Publication statusPublished - 27 Feb 2024

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