Evidence map›Paper›PMID 38984269›Full record

ArticleThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale2024

Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary Analysis.

Seyyed Amir Yasin Ahmadi, Yeganeh Karimi, Arash Abdollahi, Ali Kabir

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In one paragraph

Article in The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Seyyed Amir Yasin AhmadiPreventive Medicine and Public Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0001-9274-7408
Yeganeh KarimiTehran Heart Center Cardiovascular Diseases Research Institute Tehran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0001-7726-2752
Arash AbdollahiMinimally Invasive Surgery Research Center Iran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0002-9705-0332
Ali KabirMinimally Invasive Surgery Research Center Iran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0001-9496-4705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Although COVID-19 is not currently a public health emergency, it will affect susceptible individuals in the post-COVID-19 era. Hence, the present study aimed to develop a model for Iranian patients to identify at-risk groups based on past medical history (PMHx) and some other factors affecting the death of patients hospitalized with COVID-19. Methods: A secondary study was conducted with the existing data of hospitalized COVID-19 adult patients in the hospitals covered by Iran University of Medical Sciences. PMHx was extracted from the registered ICD-10 codes. Stepwise logistic regression was used to predict mortality by PMHx and background covariates such as intensive care unit (ICU) admission. Crude population attributable fraction (PAF) as well as crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were reported. Results: A total of 8879 patients were selected with 19.68% mortality. Infectious and parasitic diseases' history showed the greatest association (OR = 5.72, 95% CI: 4.20, 7.82), while the greatest PAF was for cardiovascular system diseases (20.46%). According to logistic regression modeling, the largest effect, other than ICU admission and age, was for history of infectious and parasitic diseases (OR = 3.089, 95% CI: 2.13, 4.47). A good performance was achieved (area under curve = 0.875). Conclusion: Considering the prevalence of underlying diseases, many mortality cases of COVID-19 are attributable to the history of cardiovascular disease. Future studies are needed for policy making regarding reduction of COVID-19 mortality in susceptible groups in the post-COVID-19 era.

Identifiers

PMID38984269
PMCPMC11233185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.