Advanced Care Research Centre, Centre for Population Health Sciences, Usher Institute, The University of Edinburgh, Edinburgh, UK.
Manchester Centre for Health Economics, The University of Manchester, Manchester, UK.
Health Soc Care Deliv Res. 2024 Feb;12(4):1-275. doi: 10.3310/KLTR7714.
Clinical guidelines commonly recommend preventative 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 (i.e. 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.
Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments.
DESIGN, PARTICIPANTS, MAIN OUTCOME MEASURES, DATA SOURCES: Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25-84 years for cardiovascular disease and aged 30-99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making).
CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell's = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85-99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise ( = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best-worst scaling ( = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk.
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 previous 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. Not all the inputs to the cost-effectiveness models could be updated.
Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making.
This study is registered as PROSPERO CRD42021249959.
This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in ; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.
临床指南通常建议对风险阈值以上的人群进行预防治疗。因此，决策者必须对风险预测工具和基于模型的成本效益分析有信心，这些工具和分析适用于不同风险水平的人群。存在两个问题：对死亡的竞争风险处理不当，以及未能考虑直接治疗的不便（即治疗的麻烦）。我们使用两个案例研究来探讨这些问题：使用他汀类药物进行心血管疾病的一级预防和使用双膦酸盐进行骨质疏松性骨折的预防。
验证三种风险预测工具[QRISK®3、QRISK®-Lifetime、QFracture-2012（Leeds，ClinRisk Ltd，英国）]；为心血管疾病（考虑到合并症的死亡的竞争风险模型与 Charlson 合并症指数（CRISK-CCI））和骨折（CFracture）推导并内部验证新的风险预测工具，以量化直接治疗的不便（即治疗的麻烦）；并研究竞争风险和直接治疗不便对预防治疗的成本效益的影响。
设计、参与者、主要结局指标、数据来源：心血管疾病（年龄 25-84 岁）和骨折（年龄 30-99 岁）的风险预测模型的区分和校准（来自临床实践研究数据链接参与者）；直接治疗的不便在在线陈述偏好调查中进行了调查（有/没有他汀类药物/双膦酸盐治疗经验的人）；成本和质量调整生命年从决策分析模型中确定（更新的模型用于国家卫生与保健卓越研究所的决策制定）。
CRISK-CCI 具有出色的区分能力，与 QRISK3 相似（女性的 Harrell's = 0.864 与 0.865，男性的 Harrell's = 0.819 与 0.834）。CRISK-CCI 的校准效果更好，尽管两个模型在高风险亚组中都存在过度预测。使用 QRISK-Lifetime 时，被建议接受治疗（10 年风险≥10%）的人群比使用 QRISK3 时更年轻，且在 10 年随访期间观察到的事件更少（女性分别为 4.0%和 11.9%，男性分别为 4.3%和 10.8%）。QFracture-2012 低估了骨折，这是由于其推导过程中事件的确定不足。然而，在 85-99 岁年龄组和/或患有多种长期疾病的人群中，存在严重的过度预测。CFracture 的校准效果更好，尽管在老年人中也存在过度预测。在时间权衡练习（ = 879）中，他汀类药物表现出 0.034 的直接治疗不便，而双膦酸盐的不便更大，为 0.067。在最佳最差分级法（ = 631）中，不便也会影响偏好。更新后的成本效益分析在心血管风险低于平均水平的人群中产生了更多的质量调整生命年，而在心血管风险高于平均水平的人群中则产生了更少的质量调整生命年。如果人们在服用他汀类药物时感到不便，那么受益超过危害的心血管风险阈值会随着年龄的增长而上升（40 岁时≥8%的 10 年风险；80 岁时≥38%的 10 年风险）。假设每个人在口服双膦酸盐时都经历了人群平均的直接治疗不便，那么在所有风险水平下，治疗都是有害的。
在推导风险预测模型时，将数据视为随机缺失是一个很强的假设。在过去二十年中，将心血管疾病的他汀类药物的趋势与心血管疾病的竞争风险分开是具有挑战性的。如果不使用非常历史的数据，验证终生风险预测是不可能的。我们的陈述偏好调查的受访者可能无法代表人群。对于成本效益分析，没有共识应该使用哪种直接治疗不便。并非所有成本效益模型的投入都可以进行更新。
在风险预测中忽略竞争死亡率会高估心血管事件和骨折的风险，尤其是在老年人和患有多种合并症的人群中。调整竞争风险并不会显著改变这些预防干预措施的成本效益，但直接治疗不便可衡量，并且有可能改变收益和危害之间的平衡。我们认为，在个体层面的共同决策中，最好解决这个问题。
本研究由英国国家卫生与保健卓越研究所（NIHR）健康和社会保健交付研究计划（NIHR 奖 REF：15/12/22）资助，并在 ; 第 12 卷，第 4 期。有关该奖项的更多信息，请访问 NIHR 资助和奖项网站。
