Evidence map›Paper›PMID 36750802›Full record

ArticleBMC pulmonary medicine2023

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

Sahar Kandil, Ayman I Tharwat, Sherief M Mohsen, Mai Eldeeb, Waleed Abdallah, Amr Hilal, Hala Sweed, Mohamed Mortada, Elham Arif, Tarek Ahmed and 16 more

Abstract read
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In one paragraph

Article in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. The Impact of Vaccination on COVID-19 Outcomes in Vietnam.Diagnostics (Basel, Switzerland) · 2024
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Sahar KandilDepartment of Community, Environmental, and Occupational Medicine, Faculty of Medicine, Ain Shams University, 38 Ramses St., Abbassia Square, Cairo, 1156, Egypt. saharkandil@med.asu.edu.eg.ORCID http://orcid.org/0000-0001-9433-659X
Ayman I TharwatDepartment of Anaesthesia, Intensive Care and Pain Management, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Sherief M MohsenDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Mai EldeebDepartment of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Waleed AbdallahDepartment of Anaesthesia, Intensive Care and Pain Management, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Amr HilalDepartment of Anaesthesia, Intensive Care and Pain Management, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Hala SweedDepartment of Geriatric Medicine and Gerontology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Mohamed MortadaDepartment of Geriatric Medicine and Gerontology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Elham ArifDepartment of Geriatric Medicine and Gerontology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Tarek AhmedDepartment of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Ahmed ElshafieDepartment of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Tarek YoussefDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Mohamed ZakiDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Yasmin El-GendyDepartment of Paediatrics, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Essam EbiedDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Safwat HamadDepartment of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
Ihab HabilDepartment of Community, Environmental, and Occupational Medicine, Faculty of Medicine, Ain Shams University, 38 Ramses St., Abbassia Square, Cairo, 1156, Egypt.
Hany DabbousDepartment of Hepatology and Infectious Diseases, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Amr El-SaidDepartment of Anaesthesia, Intensive Care and Pain Management, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Yasser MostafaDepartment of Chest Diseases, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Samia GirgisDepartment of Clinical Pathology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Ossama MansourDepartment of Otolaryngology and Head and Neck Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Ali El-AnwarDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Ashraf OmarDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Ayman SalehDepartment of Cardiology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
Mahmoud El-MeteiniDepartment of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSince the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction.

methodsCOVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models.

resultsPatients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873).

conclusionPatients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

Indexed as

COVID-19AgedEgyptFemaleHospital MortalityHospitals, UniversityHumansMaleMiddle AgedRetrospective StudiesSARS-CoV-2ComorbiditiesCOVID-19In-hospital mortalityMortality predictorsObesityPrognostic modelSmokingSurvival

Identifiers

PMID36750802
PMCPMC9903412

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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.