Cardiovascular Division, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Physics Department, University of California, Santa Barbara, CA, USA.
Eur J Heart Fail. 2022 Aug;24(8):1418-1426. doi: 10.1002/ejhf.2528. Epub 2022 May 22.
Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency.
Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44-1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30-1.54), and HFpEF (HR 1.74, 95% CI 1.53-1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit.
Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.
预后富集策略可以提高试验效率，但可能会降低外部有效性。使用风险评分来确定死亡率较高的人群是否可以提高试验效率尚不确定。我们旨在评估先前验证过的风险评分——Machine learning Assessment of RisK and EaRly mortality in Heart Failure（MARKER-HF）是否可以提高临床试验的效率。
在四个社区心力衰竭（HF）和五个 HF 临床试验队列中，评估了 MARKER-HF 的死亡率和与 1 年全因死亡的相关性。假设研究性治疗对死亡率的影响，计算了评估治疗效果所需的样本量，假设 MARKER-HF 风险和拟议治疗效果分布不同。来自社区 HF 队列的患者（n=11297）的观察死亡率和 MARKER-HF 评分均高于 HF 临床试验队列（n=13165），HF 患者的射血分数降低（HFrEF）或射血分数保留（HFpEF）。MARKER-HF 评分与社区（风险比 [HR] 1.48，95%置信区间 [CI] 1.44-1.52）和 HFrEF（HR 1.41，95% CI 1.30-1.54）临床试验队列中 1 年死亡率风险密切相关，HFpEF（HR 1.74，95% CI 1.53-1.98），每增加 0.1 分。使用 MARKER-HF 识别接受评估死亡率降低的干预措施的临床试验患者，可以减少证明获益所需的样本量。
使用可靠的死亡率预测指标（如 MARKER-HF）来丰富临床试验人群，是通过减少需要证明临床获益的样本量来提高效率的潜在策略。
