Lin Lijing, Poppe Katrina, Wood Angela, Martin Glen P, Peek Niels, Sperrin Matthew
Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.
Schools of Population Health & Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Front Epidemiol. 2024 Apr 3;4:1326306. doi: 10.3389/fepid.2024.1326306. eCollection 2024.
Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol.
We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining existing prediction models with causal effects reported in published literature.
The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between a non-causal estimate of 2.5% and a causal estimate of 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate).
Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.
大多数现有的临床预测模型不允许在干预措施下进行预测。此类预测能够比较不同拟议策略下的预测风险，因此有助于支持临床决策。我们旨在比较三种干预措施（戒烟、降低血压和降低胆固醇）下预测个体心血管风险的方法。
我们使用了来自新西兰PREDICT前瞻性队列研究的数据，以计算初级保健环境中的心血管风险。我们比较了三种估计干预措施下绝对风险的策略：(a) 在非因果模型中基于假设干预进行条件设定；(b) 将现有的预测模型与使用观察性因果推断方法估计的因果效应相结合；(c) 将现有的预测模型与已发表文献中报告的因果效应相结合。
吸烟者的心血管绝对风险中位数为3.9%；根据估计方法，我们的方法预测戒烟可将其降至非因果估计的2.5%至因果估计的2.8%之间的中位数。对于降低血压，所提出的方法估计绝对风险从中位数4.9%降至3.2%至4.5%之间的中位数（均来自因果估计）。降低胆固醇估计可将绝对风险中位数从3.1%降至2.2%（非因果估计）至2.8%（因果估计）之间。
基于非因果方法估计的绝对风险降低与基于因果方法的不同，并且因果方法内的估计存在很大差异。希望估计干预措施下风险的研究人员应明确其因果建模假设，并通过考虑一系列可能的方法进行敏感性分析。
