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.
Clinical phenotype
Clustering
Machine learning
Prognosis
SARS-CoV-2
Journal
Respiratory research
ISSN: 1465-993X
Titre abrégé: Respir Res
Pays: England
ID NLM: 101090633
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
17
07
2024
accepted:
20
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
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. 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). 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. 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.
Identifiants
pubmed: 39478555
doi: 10.1186/s12931-024-03018-3
pii: 10.1186/s12931-024-03018-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
392Informations de copyright
© 2024. The Author(s).
Références
Ouldali N, Yang DD, Madhi F, et al. Factors Associated with severe SARS-CoV-2 infection. Pediatrics. 2021;147(3):e2020023432. https://doi.org/10.1542/peds.2020-023432 .
doi: 10.1542/peds.2020-023432
pubmed: 33323493
Ludvigsson JF. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 2020;109(6):1088–95. https://doi.org/10.1111/apa.15270 .
doi: 10.1111/apa.15270
pubmed: 32202343
pmcid: 7228328
Feldstein LR, Tenforde MW, Friedman KG, et al. Characteristics and outcomes of US children and adolescents with Multisystem Inflammatory Syndrome in Children (MIS-C) compared with severe Acute COVID-19. JAMA. 2021;325(11):1074–87. https://doi.org/10.1001/jama.2021.2091 .
doi: 10.1001/jama.2021.2091
pubmed: 33625505
McCormick DW, Richardson LC, Young PR, et al. Deaths in children and adolescents Associated with COVID-19 and MIS-C in the United States. Pediatrics. 2021;148(5):e2021052273. https://doi.org/10.1542/peds.2021-052273 .
doi: 10.1542/peds.2021-052273
pubmed: 34385349
Bailey LC, Razzaghi H, Burrows EK, et al. Assessment of 135 794 Pediatric patients tested for severe Acute Respiratory Syndrome Coronavirus 2 across the United States. JAMA Pediatr. 2021;175(2):176–84. https://doi.org/10.1001/jamapediatrics.2020.5052 .
doi: 10.1001/jamapediatrics.2020.5052
pubmed: 33226415
Zhu Y, Almeida FJ, Baillie JK, et al. International Pediatric COVID-19 Severity over the course of the pandemic. JAMA Pediatr. 2023;177(10):1073–84. https://doi.org/10.1001/jamapediatrics.2023.3117 .
doi: 10.1001/jamapediatrics.2023.3117
pubmed: 37603343
pmcid: 10442787
Sorg A-L, Hufnagel M, Doenhardt M, et al. Risk for severe outcomes of COVID-19 and PIMS-TS in children with SARS-CoV-2 infection in Germany. Eur J Pediatrics. 2022;181(10):3635–43. https://doi.org/10.1007/s00431-022-04587-5 .
doi: 10.1007/s00431-022-04587-5
Yamga E, Mullie L, Durand M, et al. Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis. Front Digit Health. 2023;5:1142822. https://doi.org/10.3389/fdgth.2023.1142822 .
doi: 10.3389/fdgth.2023.1142822
pubmed: 37114183
pmcid: 10128042
Loftus TJ, Shickel B, Balch JA, et al. Phenotype clustering in health care: a narrative review for clinicians. Front Artif Intell. 2022;5. https://doi.org/10.3389/frai.2022.842306 .
Hasan MM, Islam MU, Sadeq MJ, Fung W-K, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. Sensors. 2023;23(1):527. https://doi.org/10.3390/s23010527 .
doi: 10.3390/s23010527
pubmed: 36617124
pmcid: 9824505
Gutiérrez-Gutiérrez B, Del Toro MD, Borobia AM, et al. Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study. Lancet Infect Dis. 2021;21(6):783–92. https://doi.org/10.1016/S1473-3099(21)00019-0 .
doi: 10.1016/S1473-3099(21)00019-0
pubmed: 33636145
pmcid: 7906623
Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of Novel Clinical Phenotypes for Sepsis. JAMA. 2019;321(20):2003–17. https://doi.org/10.1001/jama.2019.5791 .
doi: 10.1001/jama.2019.5791
pubmed: 31104070
pmcid: 6537818
Ozonoff A, Schaenman J, Jayavelu ND, et al. Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: results from the IMPACC study. EBioMedicine. 2022;83:104208. https://doi.org/10.1016/j.ebiom.2022.104208 .
doi: 10.1016/j.ebiom.2022.104208
pubmed: 35952496
pmcid: 9359694
Rodríguez A, Ruiz-Botella M, Martín-Loeches I, et al. 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. Crit Care. 2021;25(1):63. https://doi.org/10.1186/s13054-021-03487-8 .
doi: 10.1186/s13054-021-03487-8
pubmed: 33588914
pmcid: 7883885
Geva A, Patel MM, Newhams MM et al. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. eClinicalMedicine. 2021; 40. https://doi.org/10.1016/j.eclinm.2021.101112
Armann J, Doenhardt M, Hufnagel M et al. Risk factors for hospitalization, disease severity and mortality in children and adolescents with COVID-19: Results from a nationwide German registry. medRxiv 2021: 2021.06.07.21258488. https://doi.org/10.1101/2021.06.07.21258488
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of Observational studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–9. https://doi.org/10.1016/j.jclinepi.2007.11.008 .
doi: 10.1016/j.jclinepi.2007.11.008
Williams N, Radia T, Harman K, Agrawal P, Cook J, Gupta A. COVID-19 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children and adolescents: a systematic review of critically unwell children and the association with underlying comorbidities. Eur J Pediatr. 2021;180(3):689–97. https://doi.org/10.1007/s00431-020-03801-6 .
doi: 10.1007/s00431-020-03801-6
pubmed: 32914200
Schober T, Caya C, Barton M, et al. Risk factors for severe PCR-positive SARS-CoV-2 infection in hospitalised children. BMJ Paediatr Open. 2022;6(1). https://doi.org/10.1136/bmjpo-2022-001440 .
Prévost B, Retbi A, Binder-Foucard F, et al. Risk factors for admission to the pediatric critical care unit among children hospitalized with COVID-19 in France. Front Pediatr. 2022;10:975826. https://doi.org/10.3389/fped.2022.975826 .
doi: 10.3389/fped.2022.975826
pubmed: 36160797
pmcid: 9489832
Graff K, Smith C, Silveira L, et al. Risk factors for severe COVID-19 in children. Pediatr Infect Dis J. 2021;40(4):e137–45. https://doi.org/10.1097/INF.0000000000003043 .
doi: 10.1097/INF.0000000000003043
pubmed: 33538539
Nguyen PNT, Thuc TT, Hung NT, et al. Risk factors for disease severity and mortality of children with Covid-19: a study at a Vietnamese children’s hospital. J Infect Chemother. 2022;28(10):1380–6. https://doi.org/10.1016/j.jiac.2022.06.010 .
doi: 10.1016/j.jiac.2022.06.010
pubmed: 35738340
pmcid: 9212605
Choudhary R, Webber BJ, Womack LS, et al. Factors Associated with severe illness in patients aged < 21 years hospitalized for COVID-19. Hosp Pediatr. 2022;12(9):760–83. https://doi.org/10.1542/hpeds.2022-006613 .
doi: 10.1542/hpeds.2022-006613
pubmed: 35670605
pmcid: 9773098
Ng DC-E, Liew C-H, Tan KK, et al. Risk factors for disease severity among children with Covid-19: a clinical prediction model. BMC Infect Dis. 2023;23(1):398. https://doi.org/10.1542/hpeds.2022-006613 .
doi: 10.1542/hpeds.2022-006613
pubmed: 37308825
pmcid: 10259359
Harwood R, Yan H, Talawila Da Camara N, et al. Which children and young people are at higher risk of severe disease and death after hospitalisation with SARS-CoV-2 infection in children and young people: a systematic review and individual patient meta-analysis. eClinicalMedicine. 2022;44. https://doi.org/10.1016/j.eclinm.2022.101287 .
Bertran M, Amin-Chowdhury Z, Davies HG, et al. COVID-19 deaths in children and young people in England, March 2020 to December 2021: an active prospective national surveillance study. PLoS Med. 2022;19(11):e1004118. https://doi.org/10.1371/journal.pmed.1004118 .
doi: 10.1371/journal.pmed.1004118
pubmed: 36346784
pmcid: 9642873
Oliveira EA, Oliveira MCL, Silva A, et al. Clinical outcomes of Omicron variant (B.1.1.529) infection in children and adolescents hospitalized with COVID-19 in Brazil with Observational Data on the efficacy of the vaccines in adolescents. Pediatr Infect Dis J. 2023;42(3):218–25. https://doi.org/10.1097/INF.0000000000003783 .
doi: 10.1097/INF.0000000000003783
pubmed: 36730085
Smith C, Odd D, Harwood R, et al. Deaths in children and young people in England after SARS-CoV-2 infection during the first pandemic year. Nat Med. 2022;28(1):185–92. https://doi.org/10.1038/s41591-021-01578-1 .
doi: 10.1038/s41591-021-01578-1
pubmed: 34764489
Little RJA. A test of missing completely at Random for Multivariate Data with missing values. J Am Stat Assoc. 1988;83(404):1198–202. https://doi.org/10.2307/2290157 .
doi: 10.2307/2290157
Murray CJ, Richards MA, Newton JN, et al. UK health performance: findings of the global burden of Disease Study 2010. Lancet. 2013;381(9871):997–1020. https://doi.org/10.1016/S0140-6736(13)60355-4 .
doi: 10.1016/S0140-6736(13)60355-4
pubmed: 23668584
Breiman L. Manual for Setting Up, Using, and Understanding Random Forest V4.0. 2003.
Gower JC. A General Coefficient of Similarity and some of its Properties. Biometrics. 1971;27:857–71. https://doi.org/10.2307/2528823 .
doi: 10.2307/2528823
Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236–44. https://doi.org/10.1080/01621459.1963.10500845 .
doi: 10.1080/01621459.1963.10500845
Charrad M, Ghazzali N, Boiteau V, Niknafs A, NbClust. An R Package for determining the relevant number of clusters in a Data Set. J Stat Softw. 2014;61(6):1–36. https://doi.org/10.18637/jss.v061.i06 .
doi: 10.18637/jss.v061.i06
Maechler M, Rousseeuw P, Struyf A, Mia. H, Hornik K. cluster: Cluster Analysis Basics and Extensions (R package version 2.1.6). 2023. https://doi.org/10.32614/CRAN.package.cluster
Farrar DS, Drouin O, Moore Hepburn C, et al. Risk factors for severe COVID-19 in hospitalized children in Canada: a national prospective study from March 2020-May 2021. Lancet Reg Health Am. 2022;15:100337. https://doi.org/10.1016/j.lana.2022.100337 .
doi: 10.1016/j.lana.2022.100337
pubmed: 35936225
pmcid: 9342862
Cheung KS, Hung IFN, Chan PPY, et al. Gastrointestinal manifestations of SARS-CoV-2 infection and virus load in fecal samples from a Hong Kong Cohort: systematic review and Meta-analysis. Gastroenterology. 2020;159(1):81–95. https://doi.org/10.1053/j.gastro.2020.03.065 .
doi: 10.1053/j.gastro.2020.03.065
pubmed: 32251668
Calitri C, Fumi I, Ignaccolo MG, et al. Gastrointestinal involvement in paediatric COVID-19 - from pathogenesis to clinical management: a comprehensive review. World J Gastroenterol. 2021;27(23):3303–16. https://doi.org/10.3748/wjg.v27.i23.3303 .
doi: 10.3748/wjg.v27.i23.3303
pubmed: 34163113
pmcid: 8218363
Chen Y-c, Wang X, Teng Y-s, Jia D-s, Li L, Pan H-g. Otolaryngology-related symptoms of COVID-19 in children in the post-epidemic era: a cross-sectional web-based survey study. Front Pead. 2023. https://doi.org/10.3389/fped.2023.1190734 .
doi: 10.3389/fped.2023.1190734
Hijazi LO, Alaraifi AK, Alsaab F. Otolaryngology manifestations of COVID-19 in pediatric patients. Int J Pediatr Otorhinolaryngol. 2021;144:110701. https://doi.org/10.1016/j.ijporl.2021.110701 .
doi: 10.1016/j.ijporl.2021.110701
pubmed: 33845420
pmcid: 8011029
Trapani G, Verlato G, Bertino E, et al. Long COVID-19 in children: an Italian cohort study. Ital J Pediatr. 2022;48(1):83. https://doi.org/10.1186/s13052-022-01282-x .
doi: 10.1186/s13052-022-01282-x
pubmed: 35659358
pmcid: 9163526
Laçinel Gürlevik S, Günbey C, Ozsurekci Y, et al. Neurologic manifestations in children with COVID-19 from a tertiary center in Turkey and literature review. Eur J Paediatr Neurol. 2022;37:139–54. https://doi.org/10.1016/j.ejpn.2022.02.003 .
doi: 10.1016/j.ejpn.2022.02.003
pubmed: 35287009
pmcid: 8853984
LaRovere KL, Riggs BJ, Poussaint TY, et al. Neurologic involvement in children and adolescents hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome. JAMA Neurol. 2021;78(5):536–47. https://doi.org/10.1001/jamaneurol.2021.0504 .
doi: 10.1001/jamaneurol.2021.0504
pubmed: 33666649
Francoeur C, Alcamo AM, Robertson CL, et al. Severe Pediatric neurological manifestations with SARS-CoV-2 or MIS-C hospitalization and New Morbidity. JAMA Netw Open. 2024;7(6):e2414122–e. https://doi.org/10.1001/jamanetworkopen.2024.14122 .
doi: 10.1001/jamanetworkopen.2024.14122
pubmed: 38857050
pmcid: 11165382
Woodruff RC, Campbell AP, Taylor CA, et al. Risk factors for severe COVID-19 in children. Pediatrics. 2022;149(1). https://doi.org/10.1542/peds.2021-053418 .
Misailovski M, Koller D, Blaschke S, et al. Refining the hospitalization rate: a mixed methods approach to differentiate primary COVID-19 from incidental cases. Infect Prev Pract. 2024;6(3):100371. https://doi.org/10.1016/j.infpip.2024.100371 .
doi: 10.1016/j.infpip.2024.100371
pubmed: 38855736
pmcid: 11153910
Strobl R, Misailovski M, Blaschke S, et al. Differentiating patients admitted primarily due to coronavirus disease 2019 (COVID-19) from those admitted with incidentally detected severe acute respiratory syndrome corona-virus type 2 (SARS-CoV-2) at hospital admission: a cohort analysis of German hospital records. Infect Control Hosp Epidemiol. 2024;45(6):746–53. https://doi.org/10.1017/ice.2024.3 .
doi: 10.1017/ice.2024.3
pubmed: 38351873
pmcid: 11102825