Evidence map›Paper›PMID 38053350›Full record

ArticleEpidemiology and infection2023

ACCI could be a poor prognostic indicator for the in-hospital mortality of patients with SFTS.

Chen Gong, Xinjian Xiang, Baoyu Hong, Tingting Shen, Meng Zhang, Shichun Shen, Shenggang Ding

Abstract read
In one paragraph

Article in Epidemiology and infection, 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

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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. Observational
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chen GongDepartment of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID 0000-0002-5835-3605
Xinjian XiangDepartment of Plastic and Reconstructive Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Baoyu HongDepartment of Pediatrics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Tingting ShenDepartment of Pathology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
Meng ZhangDepartment of Cardiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Shichun ShenDepartment of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Shenggang DingDepartment of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Funding

National Natural Science Foundation of China Grant No.52273113
6 · The paper itself

Abstract

This study aims to evaluate the predictive role of age-adjusted Charlson comorbidity index (ACCI) scores for in-hospital prognosis of severe fever in thrombocytopenia syndrome (SFTS) patients. A total of 192 patients diagnosed with SFTS were selected as the study subjects. Clinical data were retrospectively collected. Receiver operating characteristic curves were used to evaluate the diagnostic value of ACCI for the mortality of SFTS patients, and Cox regression models were used to assess the association between predictive factors and prognosis. The 192 SFTS patients were divided into two groups according to the clinical endpoints (survivors/non-survivors). The results showed that the mortality of the 192 hospitalized SFTS patients was 26.6%. The ACCI score of the survivor group was significantly lower than that of the non-survivor group. Multivariate Cox regression analysis showed that the increased ACCI score was a significant predictor of poor prognosis in SFTS. Kaplan-Meier survival analysis showed that SFTS patients with an ACCI >2.5 had shorter mean survival times, indicating a poor prognosis. Our findings suggest that ACCI, as an easy-to-use clinical indicator, may offer a simple and feasible approach for clinicians to determine the severity of SFTS.

Indexed as

Severe Fever with Thrombocytopenia SyndromeAge FactorsComorbidityHospital MortalityHumansPrognosisRetrospective StudiesRisk FactorsACCIBunyaviridaeinfectious diseasein-hospital mortalitySFTS

Identifiers

PMID38053350
PMCPMC10753457

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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.