Evidence map›Paper›PMID 33737679›Full record

ArticleScientific reports2021

Impact of comorbidity burden on mortality in patients with COVID-19 using the Korean health insurance database.

Soo Ick Cho, Susie Yoon, Ho-Jin Lee

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 4 pooled it
–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

64 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  8. A Pilot Study of COVID-19 Outcomes in People Living with HIV in Tatarstan.International journal of molecular sciences · 2025
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4 more citing papers are in PubMed but not listed here.

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

3 authors.

Soo Ick Cho *Department of Dermatology, Seoul National University Hospital, Seoul, Republic of Korea.
Susie Yoon *Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Ho-Jin LeeDepartment of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea. zenerdiode03@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to investigate the impact of comorbidity burden on mortality in patients with coronavirus disease (COVID-19). We analyzed the COVID-19 data from the nationwide health insurance claims of South Korea. Data on demographic characteristics, comorbidities, and mortality records of patients with COVID-19 were extracted from the database. The odds ratios of mortality according to comorbidities in these patients with and without adjustment for age and sex were calculated. The predictive value of the original Charlson comorbidity index (CCI) and the age-adjusted CCI (ACCI) for mortality in these patients were investigated using the receiver operating characteristic (ROC) curve analysis. Among 7590 patients, 227 (3.0%) had died. After age and sex adjustment, hypertension, diabetes mellitus, congestive heart failure, dementia, chronic pulmonary disease, liver disease, renal disease, and cancer were significant risk factors for mortality. The ROC curve analysis showed that an ACCI threshold > 3.5 yielded the best cut-off point for predicting mortality (area under the ROC 0.92; 95% confidence interval 0.91-0.94). Our study revealed multiple risk factors for mortality in patients with COVID-19. The high predictive power of the ACCI for mortality in our results can support the importance of old age and comorbidities in the severity of COVID-19.

Indexed as

AdolescentAdultAgedAged, 80 and overChildCohort StudiesComorbidityCOVID-19Databases as TopicFemaleHumansMaleMiddle AgedRepublic of KoreaYoung Adult

Identifiers

PMID33737679
PMCPMC7973767

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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.