Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

Avelino‐Silva, Vivian Iida; Avelino‐Silva, Thiago Junqueira; Aliberti, Márlon Juliano Romero; Ferreira, Juliana Carvalho; Cobello, Vilson; Silva, Katia Regina da; Pompeu, José Eduardo; Antonângelo, Leila; Magri, Marcello Mihailenko Chaves; Filho, Tarcísio Eloy Pessoa Barros; Souza, Heraldo Possolo de; Kallás, Esper G.

doi:10.1016/j.clinsp.2023.100183

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Upon completion of the database and case validation, the possibility of accessing the institutional database above was widely advertised in successive calls open to HC-FMUSP-based research groups. Thus far, the database has been used in 17 published studies, attracting several research groups (36,(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53) (see Supplementary Table S3). The hospitalization database is currently being used for a few additional analyses, and it will continue to be accessible for new studies proposed in the near future.…”

Section: Institutional Database Of Hospitalizations Due To Covid-19mentioning

confidence: 99%

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Ritto,

de Araujo,

de Carvalho

et al. 2024

Front. Public Health

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IntroductionThe COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.MethodsAt the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.ResultsOver the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020–2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.DiscussionResearch is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

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Section: Institutional Database Of Hospitalizations Due To Covid-19mentioning

confidence: 99%

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Ritto,

de Araujo,

de Carvalho

et al. 2024

Front. Public Health

Self Cite

View full text Add to dashboard Cite

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Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

Hussain,

Songhua,

Aslam

et al. 2024

New Gener. Comput.

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Answer to Comments on “ Comparison between various scoring systems in predicting the need for intensive care unit admission of acute pesticide-poisoned patients” by El-Sarnagawy et al. (https://doi.org /10.1007/s11356-021–17790-y)

El-Sarnagawy,

Abdelnoor,

Abuelfadl

et al. 2023

Environ Sci Pollut Res

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Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

Cited by 4 publications

References 24 publications

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information

Answer to Comments on “ Comparison between various scoring systems in predicting the need for intensive care unit admission of acute pesticide-poisoned patients” by El-Sarnagawy et al. (https://doi.org /10.1007/s11356-021–17790-y)

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