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基于 ICU 心力衰竭患者实验室数据的聚类分析。

Clustering Based on Laboratory Data in Patients With Heart Failure Admitted to the Intensive Care Unit.

机构信息

Department of Cardiology, BooAli Hospital, Azad University of Medical Sciences, Tehran, Iran.

Independent Researcher, Tehran, Iran.

出版信息

J Clin Lab Anal. 2024 Nov;38(21):e25109. doi: 10.1002/jcla.25109. Epub 2024 Oct 4.

DOI:10.1002/jcla.25109
PMID:39367634
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11555613/
Abstract

BACKGROUND

Heart failure (HF) is a common condition that imposes a significant burden on healthcare systems. We aimed to identify subgroups of patients with heart failure admitted to the ICU using routinely measured laboratory biomarkers.

METHODS

A large dataset (N = 1176) of patients with heart failure admitted to the ICU at the Beth Israel Deaconess Medical Center in Boston, USA, between June 1, 2001, and October 31, 2012, was analyzed. We clustered patients to identify laboratory phenotypes. Cluster profiling was then performed to characterize each cluster, using a binary logistic model.

RESULTS

Two distinct clusters of patients were identified (N = 679 and 497). There was a significant difference in the mortality rate between Clusters 1 and 2 (50 [7.4%] vs. 109 [21.9%], respectively, p < 0.001). Patients in the Cluster 2 were significantly older (mean [SD] age = 72.35 [14.40] and 76.37 [11.61] years, p < 0.001) with a higher percentage of chronic kidney disease (167 [24.6%] vs. 262 [52.7%], respectively, p < 0.001). The logistic model was significant (Log-likelihood ratio p < 0.001, pseudo R = 0.746) with an area under the curve of 0.905. The odds ratio for leucocyte count, mean corpuscular volume (MCV), red blood cell (RBC) distribution width, hematocrit (HcT), lactic acid, blood urea nitrogen (BUN), serum potassium, magnesium, and sodium were significant (all p < 0.05).

CONCLUSION

Laboratory data revealed two phenotypes of ICU-admitted patients with heart failure. The two phenotypes are of prognostic importance in terms of mortality rate. They can be differentiated using blood cell count, kidney function status, and serum electrolyte concentrations.

摘要

背景

心力衰竭(HF)是一种常见病症,会给医疗保健系统带来重大负担。我们旨在使用常规测量的实验室生物标志物来确定入住 ICU 的心力衰竭患者的亚组。

方法

分析了美国波士顿 Beth Israel Deaconess 医疗中心 2001 年 6 月 1 日至 2012 年 10 月 31 日期间收治的 ICU 心力衰竭患者的大型数据集(N=1176)。我们对患者进行聚类以确定实验室表型。然后使用二元逻辑模型对每个聚类进行聚类分析,以对每个聚类进行特征描述。

结果

确定了两个不同的患者群(N=679 和 497)。群 1 和群 2 的死亡率有显著差异(分别为 50 [7.4%]和 109 [21.9%],p<0.001)。群 2 中的患者年龄明显较大(平均[标准差]年龄分别为 72.35[14.40]和 76.37[11.61]岁,p<0.001),慢性肾脏病的百分比也较高(分别为 167 [24.6%]和 262 [52.7%],p<0.001)。逻辑模型具有统计学意义(对数似然比 p<0.001,伪 R=0.746),曲线下面积为 0.905。白细胞计数、平均红细胞体积(MCV)、红细胞分布宽度、血细胞比容(HcT)、乳酸、血尿素氮(BUN)、血清钾、镁和钠的比值比均有显著意义(均 p<0.05)。

结论

实验室数据揭示了 ICU 收治的心力衰竭患者的两种表型。这两种表型在死亡率方面具有预后意义。它们可以通过血细胞计数、肾功能状态和血清电解质浓度来区分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/0de085b52977/JCLA-38-e25109-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/47e0a78d8e5b/JCLA-38-e25109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/d42a14a16bc6/JCLA-38-e25109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/55d2743db845/JCLA-38-e25109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/0de085b52977/JCLA-38-e25109-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/47e0a78d8e5b/JCLA-38-e25109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/d42a14a16bc6/JCLA-38-e25109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/55d2743db845/JCLA-38-e25109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58fe/11555613/0de085b52977/JCLA-38-e25109-g005.jpg

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本文引用的文献

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2
The roles of C-reactive protein-albumin ratio as a novel prognostic biomarker in heart failure patients: A systematic review.C 反应蛋白-白蛋白比值作为心力衰竭患者新型预后生物标志物的作用:系统评价。
Curr Probl Cardiol. 2024 May;49(5):102475. doi: 10.1016/j.cpcardiol.2024.102475. Epub 2024 Feb 22.
3
Comprehensive diagnostic workup in patients with suspected heart failure and preserved ejection fraction.
疑似心力衰竭且射血分数保留患者的全面诊断性检查。
Hellenic J Cardiol. 2024 Jan-Feb;75:60-73. doi: 10.1016/j.hjc.2023.09.013. Epub 2023 Sep 22.
4
Predictive value of blood urea nitrogen in heart failure: a systematic review and meta-analysis.血尿素氮在心力衰竭中的预测价值:一项系统评价与荟萃分析
Front Cardiovasc Med. 2023 Jul 31;10:1189884. doi: 10.3389/fcvm.2023.1189884. eCollection 2023.
5
Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database.基于机器学习的透明特征重要性树状图对心力衰竭关联进行分类:对重症监护信息集市 III (MIMIC-III)数据库回顾性队列研究的重新分析。
PLoS One. 2023 Jul 20;18(7):e0288819. doi: 10.1371/journal.pone.0288819. eCollection 2023.
6
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Health Sci Rep. 2023 Apr 20;6(4):e1214. doi: 10.1002/hsr2.1214. eCollection 2023 Apr.
7
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8
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PLoS One. 2023 Feb 23;18(2):e0281922. doi: 10.1371/journal.pone.0281922. eCollection 2023.
9
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JACC Heart Fail. 2023 Feb;11(2):252-254. doi: 10.1016/j.jchf.2022.11.022.
10
Prognostic value of Intermountain Risk Score for short- and long-term mortality in patients with cardiogenic shock.Intermountain 风险评分对心源性休克患者短期和长期死亡率的预后价值。
Coron Artery Dis. 2023 Mar 1;34(2):154-159. doi: 10.1097/MCA.0000000000001219. Epub 2023 Jan 4.