Nationwide observational study of paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) in the Czech Republic.
Jan DavidVeronika StaraOndrej HradskyJana TuckovaKaterina SlabaPetr JabandzievLumir SasekMichal HumlIveta ZidkovaJan PavlicekAlzbeta PalatovaEva KlaskovaKarina BanszkaEva TerifajovaRadim VyhnanekMarketa BloomfieldSarka FingerhutovaPavla DolezalovaLucie ProchazkovaGabriela ChramostovaFilip FenclJan LeblPublished in: European journal of pediatrics (2022)
The worldwide outbreak of the novel 2019 coronavirus disease (COVID-19) has led to recognition of a new immunopathological condition: paediatric inflammatory multisystem syndrome (PIMS-TS). The Czech Republic (CZ) suffered from one of the highest incidences of individuals who tested positive during pandemic waves. The aim of this study was to analyse epidemiological, clinical, and laboratory characteristics of all cases of paediatric inflammatory multisystem syndrome (PIMS-TS) in the Czech Republic (CZ) and their predictors of severe course. We performed a retrospective-prospective nationwide observational study based on patients hospitalised with PIMS-TS in CZ between 1 November 2020 and 31 May 2021. The anonymised data of patients were abstracted from medical record review. Using the inclusion criteria according to World Health Organization definition, 207 patients with PIMS-TS were enrolled in this study. The incidence of PIMS-TS out of all SARS-CoV-2-positive children was 0.9:1,000. The estimated delay between the occurrence of PIMS-TS and the COVID-19 pandemic wave was 3 weeks. The significant initial predictors of myocardial dysfunction included mainly cardiovascular signs (hypotension, oedema, oliguria/anuria, and prolonged capillary refill). During follow-up, most patients (98.8%) had normal cardiac function, with no residual findings. No fatal cases were reported.Conclusions: A 3-week interval in combination with incidence of COVID-19 could help increase pre-test probability of PIMS-TS during pandemic waves in the suspected cases. Although the parameters of the models do not allow one to completely divide patients into high and low risk groups, knowing the most important predictors surely could help clinical management.
Keyphrases
- sars cov
- coronavirus disease
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- emergency department
- peritoneal dialysis
- prognostic factors
- intensive care unit
- young adults
- risk assessment
- machine learning
- clinical trial
- case report
- patient reported
- cross sectional
- pulmonary embolism
- electronic health record