Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach.
Background: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that certain comorbidities and procedural needs may increase risks of poor outcomes. This study aims to identify prognostic factors for pediatric myocardit...
| Publicado en: | Pediatric Research Vol. 90; no. 2; pp. 427 - 431 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152603936&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152603936 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00313998 4FF jtl: Pediatric Research issn: 00313998 maglogo: N pubinfo: dt: Aug2021 vid: 90 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152603936 152603936 NLM33208880 152603936 10.1038/s41390-020-01268-7 NLM33208880 152603936 ppf: 427 ppct: 4 formats: tig: atl: Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach. aug: au: Chou, Fu-Sheng Ghimire, Laxmi V. affil: Department of Pediatrics, Loma Linda University School of Medicine, 92354, Loma Linda, CA, USA sug: subj: Myocardial Diseases Epidemiology Risk Factors Risk Assessment Comorbidity Time Factors Prognosis Myocardial Diseases Diagnosis Resource Databases Data Mining Prevalence Myocardial Diseases Therapy Age of Onset United States Human Length of Stay Myocardial Diseases Mortality Comparative Studies Multicenter Studies Evaluation Research ab: Background: Pediatric myocarditis is a rare disease with substantial mortality. Little is known regarding its prognostic factors. We hypothesize that certain comorbidities and procedural needs may increase risks of poor outcomes. This study aims to identify prognostic factors for pediatric myocarditis.Methods: The national Kids' Inpatient Database was used in the study. A random forests algorithm was implemented for mortality prediction based on comorbidities and procedures. Linear regression analysis was then performed to quantify their associations with mortality and length of stay.Results: The prevalence of pediatric myocarditis among all pediatric hospitalizations doubled from 2003 to 2016. The mortality rate peaked in 2006 (6.7%) and declined steadily thereafter, with a rate of 3.2% in 2016. Brain injury (including encephalopathy, cerebral edema, and intracranial hemorrhage), acute kidney injury, dysrhythmias, coagulopathy, sepsis, and ECMO use were all independent prognostic factors associated with increased mortality and prolonged hospital stay.Conclusion: Prognostic factor identification may not be straightforward in rare diseases such as pediatric myocarditis due to small cohort size in each treating facility. Findings from this report provide insights into the prognostic factors for pediatric myocarditis, and may allow clinicians to be better prepared when informing patients and their families regarding disease outcomes.Impact: The rate of hospitalization due to pediatric myocarditis was increasing but the mortality rate was declining over the past decade. End organ damage, including the brain and the kidney, was associated with mortality and prolonged hospital stay in pediatric myocarditis. Tachyarrhythmias and cardiac function compromise requiring ECMO were also associated with mortality and prolonged hospital stay. A data science approach combining machine learning algorithms and conventional regression modeling using a large dataset may facilitate risk factor identification and outcome correlation in rare diseases, as illustrated in this study. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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