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通过对 SARS-CoV-2 感染儿童和青少年的无监督机器学习,确定了具有预后意义的六种临床表型:来自德国全国登记处的结果。

Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry.

机构信息

Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-Universität München (LMU Munich), Marchioninistr. 15, 81377, Munich, Germany.

Pettenkofer School of Public Health, Munich, Germany.

出版信息

Respir Res. 2024 Oct 30;25(1):392. doi: 10.1186/s12931-024-03018-3.

DOI:10.1186/s12931-024-03018-3
PMID:39478555
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11526611/
Abstract

OBJECTIVE

Phenotypes are important for patient classification, disease prognostication, and treatment customization. We aimed to identify distinct clinical phenotypes of children and adolescents hospitalized with SARS-CoV-2 infection, and to evaluate their prognostic differences.

METHODS

The German Society of Pediatric Infectious Diseases (DGPI) registry is a nationwide, prospective registry for children and adolescents hospitalized with a SARS-CoV-2 infection in Germany. We applied hierarchical clustering for phenotype identification with variables including sex, SARS-CoV-2-related symptoms on admission, pre-existing comorbidities, clinically relevant coinfection, and SARS-CoV-2 risk factors. Outcomes of this study were: discharge status and ICU admission. Discharge status was categorized as: full recovery, residual symptoms, and unfavorable prognosis (including consequential damage that has already been identified as potentially irreversible at the time of discharge and SARS-CoV-2-related death). After acquiring the phenotypes, we evaluated their correlation with discharge status by multinomial logistic regression model, and correlation with ICU admission by binary logistic regression model. We conducted an analogous subgroup analysis for those aged < 1 year (infants) and those aged ⩾ 1 year (non-infants).

RESULTS

The DGPI registry enrolled 6983 patients, through which we identified six distinct phenotypes for children and adolescents with SARS-CoV-2 which can be characterized by their symptom pattern: phenotype A had a range of symptoms, while predominant symptoms of patients with other phenotypes were gastrointestinal (95.9%, B), asymptomatic (95.9%, C), lower respiratory tract (49.8%, D), lower respiratory tract and ear, nose and throat (86.2% and 41.7%, E), and neurological (99.2%, F). Regarding discharge status, patients with D and E phenotype had the highest odds of having residual symptoms (OR: 1.33 [1.11, 1.59] and 1.91 [1.65, 2.21], respectively) and patients with phenotype D were significantly more likely (OR: 4.00 [1.95, 8.19]) to have an unfavorable prognosis. Regarding ICU, patients with phenotype D had higher possibility of ICU admission than staying in normal ward (OR: 4.26 [3.06, 5.98]), compared to patients with phenotype A. The outcomes observed in the infants and non-infants closely resembled those of the entire registered population, except infants did not exhibit typical neurological/neuromuscular phenotypes.

CONCLUSIONS

Phenotypes enable pediatric patient stratification by risk and thus assist in personalized patient care. Our findings in SARS-CoV-2-infected population might also be transferable to other infectious diseases.

摘要

目的

表型对于患者分类、疾病预后预测和治疗方案定制非常重要。本研究旨在鉴定因 SARS-CoV-2 感染住院的儿童和青少年的不同临床表型,并评估其预后差异。

方法

德国儿科传染病学会(DGPI)登记处是一项全国性、前瞻性登记研究,登记了德国因 SARS-CoV-2 感染住院的儿童和青少年。我们应用层次聚类方法,根据入院时的性别、SARS-CoV-2 相关症状、既往合并症、临床相关合并感染以及 SARS-CoV-2 危险因素等变量识别表型。本研究的结局为出院状态和入住 ICU。出院状态分为:完全康复、残留症状和不良预后(包括出院时已确定的潜在不可逆后果和 SARS-CoV-2 相关死亡)。获得表型后,我们通过多项逻辑回归模型评估其与出院状态的相关性,通过二项逻辑回归模型评估其与入住 ICU 的相关性。我们对年龄 ⩽ 1 岁(婴儿)和年龄 ⩾ 1 岁(非婴儿)的患者进行了类似的亚组分析。

结果

DGPI 登记处共纳入 6983 例患者,通过该登记处我们鉴定了 6 种不同的 SARS-CoV-2 感染儿童和青少年表型,可根据其症状模式进行特征描述:表型 A 症状多样,而其他表型患者的主要症状为胃肠道(95.9%,B)、无症状(95.9%,C)、下呼吸道(49.8%,D)、下呼吸道和耳、鼻、喉(86.2%和 41.7%,E)和神经系统(99.2%,F)。关于出院状态,表型 D 和 E 的患者更有可能出现残留症状(OR:1.33 [1.11, 1.59] 和 1.91 [1.65, 2.21]),表型 D 的患者更有可能出现不良预后(OR:4.00 [1.95, 8.19])。关于入住 ICU,表型 D 的患者入住 ICU 的可能性高于普通病房(OR:4.26 [3.06, 5.98]),与表型 A 的患者相比。在婴儿和非婴儿中观察到的结局与整个登记人群的结局非常相似,只是婴儿没有表现出典型的神经/神经肌肉表型。

结论

表型可根据风险对儿科患者进行分层,从而有助于实现个体化患者护理。我们在 SARS-CoV-2 感染人群中观察到的结果可能也适用于其他传染病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7430/11526611/a6046081c747/12931_2024_3018_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7430/11526611/a6046081c747/12931_2024_3018_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7430/11526611/a6046081c747/12931_2024_3018_Fig1_HTML.jpg

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