Evidence map›Paper›PMID 34497086›Full record

Trial reportBMJ open2021

External validation of the 4C Mortality Score for patients with COVID-19 and pre-existing cardiovascular diseases/risk factors.

Shunsuke Kuroda, Shingo Matsumoto, Takahide Sano, Takeshi Kitai, Taishi Yonetsu, Shun Kohsaka, Sho Torii, Takuya Kishi, Issei Komuro, Ken-Ichi Hirata and 2 more

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.8field-weighted citation impact, top 12% 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

15 citing papers in PubMed, 30 citations in OpenAlex.

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  5. Validation of a specialized evaluation system for COVID-19 in Japan: A retrospective, multicenter cohort study.Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy · 2023
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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

12 authors at 11 institutions in 2 countries.

Shunsuke KurodaDepartment of Cardiovascular Medicine, Cleveland Clinic, Cleveland, Ohio, USA.ORCID 0000-0002-7547-891X
Shingo MatsumotoDepartment of Cardiovascular Medicine, Toho University Faculty of Medicine, Ota-ku, Japan.
Takahide SanoDepartment of Cardiovascular Medicine, Toho University Graduate School of Medicine, Ota-ku, Japan.
Takeshi KitaiDepartment of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan.
Taishi YonetsuDepartment of Cardiovascular Medicine, Tokyo Medical and Dental University, Bunkyo-ku, Japan.ORCID 0000-0002-1798-5008
Shun KohsakaDepartment of Cardiology, Keio University School of Medicine, Shinjuku-ku, Japan.
Sho ToriiDepartment of Cardiology, Tokai University School of Medicine, Isehara, Japan.
Takuya KishiDepartment of Graduate School of Medicine (Cardiology), International University of Health and Welfare, Fukuoka, Japan.
Issei KomuroDepartment of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
Ken-Ichi HirataDivision of Cardiovascular Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
Koichi NodeDepartment of Cardiovascular Medicine, Saga University, Saga, Japan.
Yuya MatsueDepartment of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Japan yuya8950@gmail.com.
Toho University · JPCleveland Clinic · USInternational University of Health and Welfare · JPJuntendo University · JPKeio University · JPKobe City Medical Center General Hospital · JPKobe University · JPSaga University · JPThe University of Tokyo · JPTokai University · JPTokyo Medical and Dental University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPredictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate the performance of a COVID-19-specific prediction model, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, along with other conventional critical care risk models in Japanese nationwide registry data.

designRetrospective cohort study. SETTING AND

participantsHospitalised patients with COVID-19 and cardiovascular disease or coronary risk factors from January to May 2020 in 49 hospitals in Japan.

main outcome measuresTwo different types of outcomes, in-hospital mortality and a composite outcome, defined as the need for invasive mechanical ventilation and mortality.

resultsThe risk scores for 693 patients were tested by predicting in-hospital mortality for all patients and composite endpoint among those not intubated at baseline (n=659). The number of events was 108 (15.6%) for mortality and 178 (27.0%) for composite endpoints. After missing values were multiply imputed, the performance of the 4C Mortality Score was assessed and compared with three prediction models that have shown good discriminatory ability (RISE UP score, A-DROP score and the Rapid Emergency Medicine Score (REMS)). The area under the receiver operating characteristic curve (AUC) for the 4C Mortality Score was 0.84 (95% CI 0.80 to 0.88) for in-hospital mortality and 0.78 (95% CI 0.74 to 0.81) for the composite endpoint. It showed greater discriminatory ability compared with other scores, except for the RISE UP score, for predicting in-hospital mortality (AUC: 0.82, 95% CI 0.78 to 0.86). Similarly, the 4C Mortality Score showed a positive net reclassification improvement index over the A-DROP and REMS for mortality and over all three scores for the composite endpoint. The 4C Mortality Score model showed good calibration, regardless of outcome.

conclusionsThe 4C Mortality Score performed well in an independent external COVID-19 cohort and may enable appropriate disposition of patients and allocation of medical resources.

Indexed as

Cardiovascular DiseasesCOVID-19HumansRetrospective StudiesRisk FactorsSARS-CoV-2COVID-19epidemiologyrespiratory infections

Identifiers

PMID34497086
PMCPMC8438580
OpenAlexW3197145381

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.