Preprint / Version 1

Status and trends in research on clinical prediction models for severity risk stratification in confirmed Covid-19 patients

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DOI:

https://doi.org/10.1590/SciELOPreprints.5795

Keywords:

prognosis, triage, theoretical models, covid-19, sars cov-2, pandemic

Abstract

Introduction: Previous knowledge in the scientific literature on clinical prediction models in patients with Covid-19 may be useful for the development of new research. 
Objective: We describe the sources, authors, documents and key issues that are part of the research front. Identify which models, outcome variables, predictors and algorithms have been relevant. We Identify to what extent the available models could meet the quality attributes and what characteristics they must have to be applicable
in the Cuban context. 
Methods: A review and scientometric analysis was carried out on the research in development and validation of clinical predictive models for Covid-19. The scientometric indicators were used and a thematic map was made for the analysis
of the conceptual structure of the subject.
Results: The subject was of great interest with papers published in the highest level journals. It is possible to distinguish a context of low and high risk application according to the primary and secondary health levels. The systematic review
published by Wynants et al. was the publication with the greatest impact and an important source for the identification of models, main components, as well as possible causes of bias.
Conclusions: The literature recognizes that most of the published models are not recommended for general use in clinical practice, so it is an open research front. However, the data obtained could be useful for the development and validation of
Cuban models.

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Author Biography

Maicel Monzón-Peréz, CENCEC

EXPERIENCE
Professor at the Center for Cybernetics Applied to Medicine (2003-2005, 2012-2014)

Head of research National Center of Medical Genetics of the Bolivarian Republic of Venezuela (2010-2012)

Head of statistical analysis Clinical Trials Coordination Center (2014-2019).

Researcher, statistician, data scientist

Expert-level experience with R statistical processing software, including: access to databases (SQL, MYSQL, POSTGRET); reading text / excel files; manipulating data sets; performing frequent and Bayesian statistical analyses; summary of results in presentations and reports; and write script to automate reports and streamline data management.

Design and execution of clinical investigations.

5 years of experience in the design and execution of statistical processing of clinical trials: Calculation of the sample size, random assignment to treatment groups, design of processing plans, execution of statistical processing, preparation of the final statistical processing report and other analyses.

Design and execution of epidemiological research.

Experience of more than 3 years carrying out epidemiological research designs. Observational studies of cases and controls, cohorts, ecological studies, among others. Most of the studies carried out in the Bolivarian Republic of Venezuela.

data scientist
Experience in data mining and pattern recognition, procedures for the analysis of unstructured data, text, image, electronic capture of forms by optical brand recognition, among other procedures.

Consultant

Experience as an expert in the editorial committee of the Havana Journal of Medical Sciences. Referee of the Cuban magazine of medical informatics.

Teacher

Assistant Professor of the Higher Institute of Medical Sciences of Havana. He regularly teaches Residents of Clinical Areas of the CIMEQ and Albarrán hospitals in disciplines such as computing and research methodology. He taught the Mathematical Analysis module to Residents of the biostatistics specialty.

Posted

03/27/2023

How to Cite

Monzón-Peréz, M., Sanchez-Valdés, L., & Lage-Dávila , A. (2023). Status and trends in research on clinical prediction models for severity risk stratification in confirmed Covid-19 patients. In SciELO Preprints. https://doi.org/10.1590/SciELOPreprints.5795

Section

Health Sciences

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