• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis.

出版信息

Intensive Care Med. 2021 Jan;47(1):113-115. doi: 10.1007/s00134-020-06236-7. Epub 2020 Oct 19.

DOI:10.1007/s00134-020-06236-7
PMID:33074342
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7569095/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dd3/7569095/93d0c2d07792/134_2020_6236_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dd3/7569095/93d0c2d07792/134_2020_6236_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dd3/7569095/93d0c2d07792/134_2020_6236_Fig1_HTML.jpg

相似文献

1
Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis.首次检测呈阳性时，新冠病毒病患者三种亚表型的临床恶化差异：聚类分析结果
Intensive Care Med. 2021 Jan;47(1):113-115. doi: 10.1007/s00134-020-06236-7. Epub 2020 Oct 19.
2
Association of clinical sub-phenotypes and clinical deterioration in COVID-19: further cluster analyses.新冠病毒病临床亚表型与临床病情恶化的关联：进一步的聚类分析
Intensive Care Med. 2021 Apr;47(4):482-484. doi: 10.1007/s00134-021-06363-9. Epub 2021 Feb 18.
3
Clinical stability and in-hospital mortality prediction in COVID-19 patients presenting to the Emergency Department.急诊科就诊的 COVID-19 患者的临床稳定性和院内死亡率预测。
Minerva Med. 2021 Feb;112(1):118-123. doi: 10.23736/S0026-4806.20.07074-3. Epub 2020 Oct 26.
4
Predictors of clinical deterioration in non-severe patients with COVID-19: a retrospective cohort study.新型冠状病毒肺炎非重症患者临床恶化的预测因素：一项回顾性队列研究。
Curr Med Res Opin. 2021 Mar;37(3):385-391. doi: 10.1080/03007995.2021.1876005. Epub 2021 Feb 4.
5
Risk factors for deterioration in mild COVID-19 remain undefined.轻度新冠肺炎病情恶化的风险因素仍不明确。
Emerg Med J. 2021 May;38(5):406. doi: 10.1136/emermed-2021-211233. Epub 2021 Mar 16.
6
Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.运用无监督聚类分析方法，得出西班牙 2022 年 COVID-19 危重症患者死亡率相关的临床表型和风险因素。
Crit Care. 2021 Feb 15;25(1):63. doi: 10.1186/s13054-021-03487-8.
7
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.从一个包含 5594 名患者的欧洲多中心队列中开发和验证 COVID-19 不良结局风险预测模型。
Sci Rep. 2021 Feb 5;11(1):3246. doi: 10.1038/s41598-021-81844-x.
8
Hypertension in Patients Hospitalized with COVID-19 in Wuhan, China.中国武汉 COVID-19 住院患者的高血压。
Int Heart J. 2021 Mar 30;62(2):337-343. doi: 10.1536/ihj.20-323. Epub 2021 Mar 6.
9
Risk of acute deterioration and care complexity individual factors associated with health outcomes in hospitalised patients with COVID-19: a multicentre cohort study.与 COVID-19 住院患者健康结局相关的急性恶化和护理复杂性个体因素的风险：一项多中心队列研究。
BMJ Open. 2021 Feb 17;11(2):e041726. doi: 10.1136/bmjopen-2020-041726.
10
[Impact of the COVID-19 epidemic in Friuli Venezia Giulia Region (Northern Italy): assessment of factors associated with the risk of death by competing risks analysis].[新冠疫情对弗留利-威尼斯朱利亚大区（意大利北部）的影响：通过竞争风险分析评估与死亡风险相关的因素]
Epidemiol Prev. 2020 Sep-Dec;44(5-6 Suppl 2):128-135. doi: 10.19191/EP20.5-6.S2.111.

引用本文的文献

1
Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients.临床表型分析揭示了危重症 COVID-19 患者中皮质类固醇治疗与生存之间的异质关联。
Intensive Care Med. 2024 Nov;50(11):1884-1896. doi: 10.1007/s00134-024-07593-3. Epub 2024 Aug 26.
2
Identification of distinct phenotypes and improving prognosis using metabolic biomarkers in COVID-19 patients.利用 COVID-19 患者的代谢生物标志物识别不同表型并改善预后。
Crit Care Sci. 2024 Aug 5;36:e20240028en. doi: 10.62675/2965-2774.20240028-en. eCollection 2024.
3
Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.

本文引用的文献

1
The de Winter Electrocardiogram Pattern Evolving From Hyperacute T Waves.从超急性T波演变而来的de Winter心电图模式。
JAMA Intern Med. 2021 Mar 1;181(3):372-373. doi: 10.1001/jamainternmed.2020.7084.
2
Facing COVID-19 in the ICU: vascular dysfunction, thrombosis, and dysregulated inflammation.重症监护病房中应对新型冠状病毒肺炎：血管功能障碍、血栓形成与炎症失调
Intensive Care Med. 2020 Jun;46(6):1105-1108. doi: 10.1007/s00134-020-06059-6. Epub 2020 Apr 28.
3
ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.
使用数字表型和可扩展机器学习方法进行新型术前风险分层
Anesth Analg. 2024 Jul 1;139(1):174-185. doi: 10.1213/ANE.0000000000006753. Epub 2023 Dec 5.
4
Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.纽约三种主要病毒变异株住院 COVID-19 患者的纵向动态临床表型。
Int J Med Inform. 2024 Jan;181:105286. doi: 10.1016/j.ijmedinf.2023.105286. Epub 2023 Nov 8.
5
Using Trajectories of Bedside Vital Signs to Identify COVID-19 Subphenotypes.利用床边生命体征轨迹识别新冠病毒疾病亚表型
Chest. 2024 Mar;165(3):529-539. doi: 10.1016/j.chest.2023.09.020. Epub 2023 Sep 23.
6
Coagulation biomarkers and coronavirus disease 2019 phenotyping: a prospective cohort study.凝血生物标志物与2019冠状病毒病表型分析：一项前瞻性队列研究。
Thromb J. 2023 Jul 28;21(1):80. doi: 10.1186/s12959-023-00524-0.
7
COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study.COVID-19 住院时的亚型与死亡率相关：一项横断面研究。
Ann Med. 2023 Dec;55(1):12-23. doi: 10.1080/07853890.2022.2148733.
8
Modern Learning from Big Data in Critical Care: Primum Non Nocere.重症大数据时代的现代学习：首要原则是不伤害。
Neurocrit Care. 2022 Aug;37(Suppl 2):174-184. doi: 10.1007/s12028-022-01510-6. Epub 2022 May 5.
9
Long COVID 12 months after discharge: persistent symptoms in patients hospitalised due to COVID-19 and patients hospitalised due to other causes-a multicentre cohort study.COVID-19 后 12 个月的长期 COVID：因 COVID-19 住院和因其他原因住院的患者的持续症状——一项多中心队列研究。
BMC Med. 2022 Feb 23;20(1):92. doi: 10.1186/s12916-022-02292-6.
10
Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.用于抗击 COVID-19 的人工智能：用计算智能对抗疫情
Intell Med. 2022 Feb;2(1):13-29. doi: 10.1016/j.imed.2021.09.001. Epub 2021 Oct 21.
ConsensusClusterPlus：一种具有置信度评估和项目跟踪功能的分类发现工具。
Bioinformatics. 2010 Jun 15;26(12):1572-3. doi: 10.1093/bioinformatics/btq170. Epub 2010 Apr 28.