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1000 Titel
  • 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
1000 Autor/in
  1. Shi, Yanyan |
  2. Strobl, Ralf |
  3. Berner, Reinhard |
  4. Armann, Jakob |
  5. Scheithauer, Simone |
  6. Grill, Eva |
1000 Verlag BioMed Central
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-10-30
1000 Erschienen in
1000 Quellenangabe
  • 25(1):392
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12931-024-03018-3 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526611/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Objective</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>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 &lt; 1 year (infants) and those aged ⩾ 1 year (non-infants).</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>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.</jats:p> </jats:sec>
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Hospitalization/statistics
lokal Prognosis
lokal Germany/epidemiology [MeSH]
lokal COVID-19/mortality [MeSH]
lokal Infant [MeSH]
lokal Male [MeSH]
lokal Phenotype [MeSH]
lokal COVID-19/epidemiology [MeSH]
lokal Child [MeSH]
lokal SARS-CoV-2 [MeSH]
lokal SARS-CoV-2
lokal COVID-19/diagnosis [MeSH]
lokal Adolescent [MeSH]
lokal Female [MeSH]
lokal Machine learning
lokal Humans [MeSH]
lokal Prospective Studies [MeSH]
lokal Unsupervised Machine Learning [MeSH]
lokal Clustering
lokal Research
lokal Prognosis [MeSH]
lokal Clinical phenotype
lokal Registries [MeSH]
lokal Child, Preschool [MeSH]
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  1. Ludwig-Maximilians-Universität München |
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    1000 Förderer Ludwig-Maximilians-Universität München |
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