Scholarlink

Background <p>This study investigated the prognostic significance of dynamic heart rate trajectories and heart rate coefficient of variation (CV) in critically ill patients with atrial fibrillation and heart failure (AF-HF).</p> Methods <p>This retrospective multicenter cohort study included 8,356 ICU patients with AF-HF from the MIMIC-IV and eICU databases. We applied a Joint Latent Class Model (JLCM), which simultaneously identifies distinct longitudinal heart rate trajectories and estimates their associations with survival, to classify patients into different heart rate trajectory groups over the first 30 days after ICU admission. Multivariable Cox models were used to evaluate the associations between heart rate patterns and 30-day all-cause mortality. A Bayesian joint model was further developed for dynamic risk prediction.</p> Results <p>Compared with a baseline heart rate &lt; 80&#xa0;bpm, the fully adjusted HRs were 1.13 (95% CI: 1.01–1.26) for 80–110&#xa0;bpm and 1.28 (95% CI: 1.10–1.49) for &gt; 110&#xa0;bpm. Three distinct heart rate trajectory groups were identified, with a progressively increasing trajectory associated with higher mortality (HR = 2.96, 95% CI: 2.24–3.92). Similarly, patients with increasing heart rate CV trajectories had higher mortality than those with stable CV patterns (HR = 3.11, 95% CI: 2.40–4.03). Subgroup analyses showed significant interactions by renal function and mechanical ventilation status (P for interaction &lt; 0.05). Dynamic prediction models showed improved discrimination with longer observation windows.</p> Conclusions <p>Heart rate trajectories were associated with short-term mortality in ICU patients with AF-HF, and similar associations were observed for heart rate variability trajectories in exploratory analyses. These findings suggest that longitudinal heart rate monitoring may have value for short-term risk stratification, although further validation is required.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Impact of heart rate trajectories on mortality in critically Ill patients with atrial fibrillation and heart failure: a longitudinal cohort study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

查看全文
  • Qian Ni,
  • Jialin Qi,
  • Qinghe Wang,
  • Chen Tang,
  • Zishun Liang,
  • Jing Cai,
  • Tong Qiao,
  • Baoyan Wang

摘要

Background

This study investigated the prognostic significance of dynamic heart rate trajectories and heart rate coefficient of variation (CV) in critically ill patients with atrial fibrillation and heart failure (AF-HF).

Methods

This retrospective multicenter cohort study included 8,356 ICU patients with AF-HF from the MIMIC-IV and eICU databases. We applied a Joint Latent Class Model (JLCM), which simultaneously identifies distinct longitudinal heart rate trajectories and estimates their associations with survival, to classify patients into different heart rate trajectory groups over the first 30 days after ICU admission. Multivariable Cox models were used to evaluate the associations between heart rate patterns and 30-day all-cause mortality. A Bayesian joint model was further developed for dynamic risk prediction.

Results

Compared with a baseline heart rate < 80 bpm, the fully adjusted HRs were 1.13 (95% CI: 1.01–1.26) for 80–110 bpm and 1.28 (95% CI: 1.10–1.49) for > 110 bpm. Three distinct heart rate trajectory groups were identified, with a progressively increasing trajectory associated with higher mortality (HR = 2.96, 95% CI: 2.24–3.92). Similarly, patients with increasing heart rate CV trajectories had higher mortality than those with stable CV patterns (HR = 3.11, 95% CI: 2.40–4.03). Subgroup analyses showed significant interactions by renal function and mechanical ventilation status (P for interaction < 0.05). Dynamic prediction models showed improved discrimination with longer observation windows.

Conclusions

Heart rate trajectories were associated with short-term mortality in ICU patients with AF-HF, and similar associations were observed for heart rate variability trajectories in exploratory analyses. These findings suggest that longitudinal heart rate monitoring may have value for short-term risk stratification, although further validation is required.