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Risk for Severe Illness and Death among Pediatric Patients with Down Syndrome Hospitalized for COVID-19, Brazil.

ABSTRACT: Down syndrome is the most common human chromosomal disorder. Whether Down syndrome is a risk factor for severe COVID-19 outcomes in pediatric patients remains unclear, especially in low-to-middle income countries. We gathered data on patients <18 years of age with SARS-CoV-2 infection from a national registry in Brazil to assess the risk for severe outcomes among patients with Down syndrome. We included data from 14,684 hospitalized patients, 261 of whom had Down syndrome. After adjustments for sociodemographic and medical factors, patients with Down syndrome had 1.8 times higher odds of dying from COVID-19 (odds ratio 1.82, 95% CI 1.22-2.68) and 27% longer recovery times (hazard ratio 0.73, 95% CI 0.61-0.86) than patients without Down syndrome. We found Down syndrome was associated with increased risk for severe illness and death among COVID-19 patients. Guidelines for managing COVID-19 among pediatric patients with Down syndrome could improve outcomes for this population.

SUBMITTER: Leung C

PROVIDER: S-EPMC9796221 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Risk for Severe Illness and Death among Pediatric Patients with Down Syndrome Hospitalized for COVID-19, Brazil.

Leung Char C Su Li L Simões-E-Silva Ana Cristina AC Arocha Luisamanda Selle LS de Paiva Karina Mary KM Haas Patricia P

Emerging infectious diseases 20230101 1

Down syndrome is the most common human chromosomal disorder. Whether Down syndrome is a risk factor for severe COVID-19 outcomes in pediatric patients remains unclear, especially in low-to-middle income countries. We gathered data on patients <18 years of age with SARS-CoV-2 infection from a national registry in Brazil to assess the risk for severe outcomes among patients with Down syndrome. We included data from 14,684 hospitalized patients, 261 of whom had Down syndrome. After adjustments for ...[more]

PMID: 36573520

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Project description:Due to the high rates of transmission and deaths due to COVID-19, understanding the factors associated with its occurrence, as well as monitoring and implementing control measures should be priority actions in health surveillance, highlighting the use of epidemiological surveillance information systems as an important ally. Thus, the objectives of this study were to calculate the mortality rate of hospitalized patients with severe acute respiratory syndrome due to COVID-19 and to identify factors associated with death, in the period corresponding to epidemiological weeks 01 to 53 of the year 2020. This was a longitudinal study, using the national influenza epidemiological surveillance information system database, routinely collected by healthcare services. The sociodemographic and clinical characteristics of 563,051 hospitalized patients with severe acute respiratory syndrome due to COVID-19 in the five regions of Brazil were analyzed. Cox regression was performed to assess factors associated with patient death during hospitalization. The national lethality rate was 35.7%, and the highest rates of lethality occurred in the Northeast (44.3%) and North (41.2%) regions. During the hospital stay, death was associated with older age (Hazard Ratio-HR = 1.026; p<0.001); male sex (HR = 1.052; p<0.001); living in the North (HR = 1.429; p<0.001), Northeast (HR = 1.271; p<0.001) or Southeast regions of Brazil (HR = 1.040; p<0.001), presenting any risk factor (HR = 1.129; p< 0.001), the use of invasive (HR = 2.865; p<0.001) or noninvasive (HR = 1.401; p<0.001) mechanical ventilation devices. A high case lethality rate was evidenced in patients with severe acute respiratory syndrome due to COVID-19, however, deaths were not evenly distributed across the country's regions, being heavily concentrated in the Northeast and North regions. Older male patients living in the North, Northeast, or Southeast regions of Brazil, who presented any risk factor and were submitted to the use of invasive or noninvasive mechanical ventilation devices, presented a higher risk of evolving to death.

Project description:BackgroundThe Covid-19 pandemic threatens to overwhelm scarce clinical resources. Risk factors for severe illness must be identified to make efficient resource allocations.ObjectiveTo evaluate risk factors for severe illness.DesignRetrospective, observational case series.SettingSingle-institution.ParticipantsFirst 117 consecutive patients hospitalized for Covid-19 from March 1 to April 12, 2020.ExposureNone.Main outcomes and measuresIntensive care unit admission or death.ResultsIn-hospital mortality was 24.8% and average total length of stay was 11.82 days (95% CI: 10.01 to 13.63 days). 30.8% of patients required intensive care unit admission and 29.1% required mechanical ventilation. Multivariate regression identified the amount of supplemental oxygen required at admission (OR: 1.208, 95% CI: 1.011-1.443, p = .037), sputum production (OR: 6.734, 95% CI: 1.630-27.812, p = .008), insulin dependent diabetes mellitus (OR: 11.873, 95% CI: 2.218-63.555, p = .004) and chronic kidney disease (OR: 4.793, 95% CI: 1.528-15.037, p = .007) as significant risk factors for intensive care unit admission or death. Of the 48 patients who were admitted to the intensive care unit or died, this occurred within 3 days of arrival in 42%, within 6 days in 71%, and within 9 days in 88% of patients.ConclusionsAt our regional medical center, patients with Covid-19 had an average length of stay just under 12 days, required ICU care in 31% of cases, and had a 25% mortality rate. Patients with increased sputum production and higher supplemental oxygen requirements at admission, and insulin dependent diabetes or chronic kidney disease may be at increased risk for severe illness. A model for predicting intensive care unit admission or death with excellent discrimination was created that may aid in treatment decisions and resource allocation. Early identification of patients at increased risk for severe illness may lead to improved outcomes in patients hospitalized with Covid-19.

Project description:BackgroundPredicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission.ObjectiveTo develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization.DesignRetrospective observational cohort study.SettingsFive hospitals in Maryland and Washington, D.C.PatientsPatients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease.MeasurementsA clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization.ResultsAmong 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively.LimitationThe SCARP tool was developed by using data from a single health system.ConclusionUsing the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information.Primary funding sourceHopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

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Risk for Severe Illness and Death among Pediatric Patients with Down Syndrome Hospitalized for COVID-19, Brazil.

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Risk for Severe Illness and Death among Pediatric Patients with Down Syndrome Hospitalized for COVID-19, Brazil.

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