Evidence map›Paper›PMID 31856910›Full record

ArticleBMC research notes2019

The effect of comorbidities for the prognosis of community-acquired pneumonia: an epidemiologic study using a hospital surveillance in Japan.

Mai Thi Ngoc Nguyen, Nobuyuki Saito, Yukiko Wagatsuma

Abstract read
In one paragraph

Article in BMC research notes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

3 authors.

Mai Thi Ngoc NguyenDepartment of Clinical Trials and Clinical Epidemiology, Graduate School of Comprehensive Human Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
Nobuyuki SaitoThe Shock and Trauma Center, Nippon Medical School Chiba Hokusoh Hospital, 1715 Kamagari, Inzai, Chiba, 270-1694, Japan.
Yukiko WagatsumaDepartment of Clinical Trials and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan. ywagats@md.tsukuba.ac.jp.ORCID http://orcid.org/0000-0002-8056-9998

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePneumonia is a common but serious illness that continues to present significant morbidity and mortality. Although the effect of severity at admission on outcome has been well reported, the role of comorbidity is still not widely understood. The Charlson Comorbidity Index measures comorbidity with a well-established history of predicting long-term outcome but its utility in pneumonia prognosis is still limited. Here, we use the Charlson Comorbidity Index and hospital surveillance data to investigate associations between comorbidities and in-hospital mortality due to community-acquired pneumonia.

resultsAmong the 535 eligible adult patients (69.0% male, median [IQR] age, 79 [70-84] years), 100 (18.7%) acquired severe to extremely severe pneumonia. The median [IQR] CCI was 1 [1-3]. Malignancy (129 of 535, 24.1%), chronic pulmonary diseases (113 of 535, 21.1%) and congestive heart failure (103 of 535, 19.3%) were frequent. Higher Charlson Comorbidity Index scores were associated with higher risk of in-hospital mortality (OR 1.28; 95% CI 1.07-1.53). These results support the inclusion of comorbid burden in predicting community-acquired pneumonia outcome.

Indexed as

AgedAged, 80 and overCohort StudiesCommunity-Acquired InfectionsComorbidityEpidemiological MonitoringFemaleHospital MortalityHumansJapanMalePneumoniaPrognosisRetrospective StudiesRisk FactorsSeverity of Illness IndexCharlson Comorbidity IndexCommunity-acquired pneumoniaComorbidityMortality

Identifiers

PMID31856910
PMCPMC6923893

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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.