Evidence map›Paper›PMID 38200490›Full record

ArticleBMC pulmonary medicine2024

Predicting mortality risk in hospitalized COVID-19 patients: an early model utilizing clinical symptoms.

Cong Nguyen Hai, Thanh Bui Duc, The Nguyen Minh, Lich Ngo Quang, Son Luong Cao Tung, Loi Trinh Duc, Sy Duong-Quy

Open access · goldAbstract read
In one paragraph

Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-weighted citation impact, top 13% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

7 authors at 2 institutions in 2 countries.

Cong Nguyen HaiDepartment of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam. nguyen_med@ymail.com.
Thanh Bui DucMilitary Hospital 175, Ho Chi Minh City, Vietnam.
The Nguyen MinhDepartment of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
Lich Ngo QuangDepartment of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
Son Luong Cao TungDepartment of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
Loi Trinh DucDepartment of Tuberculosis and Respiratory Pathology, Military Hospital 175, Ho Chi Minh City, Vietnam.
Sy Duong-QuyClinical Research Unit, Lam Dong Medical College and Bio-Medical Research Centre, Dalat City, Vietnam.
Dalat University · VNPham Ngoc Thach Hospital · VN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite global efforts to control the COVID-19 pandemic, the emergence of new viral strains continues to pose a significant threat. Accurate patient stratification, optimized resource allocation, and appropriate treatment are crucial in managing COVID-19 cases. To address this, a simple and accurate prognostic tool capable of rapidly identifying individuals at high risk of mortality is urgently needed. Early prognosis facilitates predicting treatment outcomes and enables effective patient management. The aim of this study was to develop an early predictive model for assessing mortality risk in hospitalized COVID-19 patients, utilizing baseline clinical factors.

methodsWe conducted a descriptive cross-sectional study involving a cohort of 375 COVID-19 patients admitted and treated at the COVID-19 Patient Treatment Center in Military Hospital 175 from October 2021 to December 2022.

resultsAmong the 375 patients, 246 and 129 patients were categorized into the survival and mortality groups, respectively. Our findings revealed six clinical factors that demonstrated independent predictive value for mortality in COVID-19 patients. These factors included age greater than 50 years, presence of multiple underlying diseases, dyspnea, acute confusion, saturation of peripheral oxygen below 94%, and oxygen demand exceeding 5 L per minute. We integrated these factors to develop the Military Hospital 175 scale (MH175), a prognostic scale demonstrating significant discriminatory ability with an area under the curve (AUC) of 0.87. The optimal cutoff value for predicting mortality risk using the MH175 score was determined to be ≥ 3 points, resulting in a sensitivity of 96.1%, specificity of 63.4%, positive predictive value of 58%, and negative predictive value of 96.9%.

conclusionsThe MH175 scale demonstrated a robust predictive capacity for assessing mortality risk in patients with COVID-19. Implementation of the MH175 scale in clinical settings can aid in patient stratification and facilitate the application of appropriate treatment strategies, ultimately reducing the risk of death. Therefore, the utilization of the MH175 scale holds significant potential to improve clinical outcomes in COVID-19 patients.

trial registrationAn independent ethics committee approved the study (Research Ethics Committee of Military Hospital 175 (No. 3598GCN-HDDD; date: October 8, 2021), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.

Indexed as

COVID-19Area Under CurveCross-Sectional StudiesHumansMiddle AgedPandemicsPatientsClinical symptomsCOVID-19MH175 scaleMortality prediction

Identifiers

PMID38200490
PMCPMC10777603
OpenAlexW4390699176

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.