Scholarlink

Background <p>Prognostication for people with Alzheimer’s disease (AD) at the point of care could improve clinical management.</p> Methods <p>In this retrospective cohort study using the electronic health record (EHR) data from a large healthcare system (2011-2022), we applied an unsupervised latent factor clustering approach guided by knowledge graph embeddings to stratify AD patients into two groups at diagnosis (baseline) using clinical features in the two years preceding diagnosis. We prognosticated the risk of AD-related outcomes (nursing home admission and mortality) for the clusters in survival analyses adjusted for baseline confounders (age, gender, race, ethnicity, healthcare utilization, and comorbidities). To reflect real-world evolution in clinical trajectories, we updated patient stratification for patients remaining at risk one year post-diagnosis and repeated prognostication.</p> Results <p>We stratify 16,411 AD patients into two groups at baseline (41% Group 1, 59% Group 2). Baseline Group 2 has a significantly lower risk of nursing home admission (HR [95% CI] = 0.804 [0.765, 0.844], <i>p</i> &lt; .001) but comparable mortality risk to baseline Group 1 (HR [95% CI] = 1.008 [0.963, 1.056], <i>p</i> = 0.733). We re-stratify the 12,606 patients remaining at risk one year post-diagnosis (46% Group 1, 54% Group 2). Consistent with baseline, the updated Group 2 has a lower risk of nursing home admission (HR [95% CI] = 0.815 [0.766, 0.868], <i>p</i> &lt; .001) but comparable mortality risk (HR [95% CI] = 0.977 [0.922, 1.035], <i>p</i> = .430) to Group 1.</p> Conclusions <p>Patient stratification enables outcome prognosis for AD patients. While baseline prognostication can guide early treatment and tailored management, dynamic prognostication may inform more timely interventions to improve long-term outcomes.</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

Stratification of Alzheimer’s disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月20日

查看全文
  • Linshanshan Wang,
  • Shruthi Venkatesh,
  • Michele Morris,
  • Mengyan Li,
  • Ratnam Srivastava,
  • Shyam Visweswaran,
  • Oscar L. Lopez,
  • Zongqi Xia,
  • Tianxi Cai

摘要

Background

Prognostication for people with Alzheimer’s disease (AD) at the point of care could improve clinical management.

Methods

In this retrospective cohort study using the electronic health record (EHR) data from a large healthcare system (2011-2022), we applied an unsupervised latent factor clustering approach guided by knowledge graph embeddings to stratify AD patients into two groups at diagnosis (baseline) using clinical features in the two years preceding diagnosis. We prognosticated the risk of AD-related outcomes (nursing home admission and mortality) for the clusters in survival analyses adjusted for baseline confounders (age, gender, race, ethnicity, healthcare utilization, and comorbidities). To reflect real-world evolution in clinical trajectories, we updated patient stratification for patients remaining at risk one year post-diagnosis and repeated prognostication.

Results

We stratify 16,411 AD patients into two groups at baseline (41% Group 1, 59% Group 2). Baseline Group 2 has a significantly lower risk of nursing home admission (HR [95% CI] = 0.804 [0.765, 0.844], p < .001) but comparable mortality risk to baseline Group 1 (HR [95% CI] = 1.008 [0.963, 1.056], p = 0.733). We re-stratify the 12,606 patients remaining at risk one year post-diagnosis (46% Group 1, 54% Group 2). Consistent with baseline, the updated Group 2 has a lower risk of nursing home admission (HR [95% CI] = 0.815 [0.766, 0.868], p < .001) but comparable mortality risk (HR [95% CI] = 0.977 [0.922, 1.035], p = .430) to Group 1.

Conclusions

Patient stratification enables outcome prognosis for AD patients. While baseline prognostication can guide early treatment and tailored management, dynamic prognostication may inform more timely interventions to improve long-term outcomes.