Evidence map›Paper›PMID 36877734›Full record

ArticlePLoS neglected tropical diseases2023

A new model for predicting the outcome and effectiveness of drug therapy in patients with severe fever with thrombocytopenia syndrome: A multicenter Chinese study.

Guomei Xia, Shanshan Sun, Shijun Zhou, Lei Li, Xu Li, Guizhou Zou, Cheng Huang, Jun Li, Zhenhua Zhang

Open access · goldFull text readMulticenter Study
In one paragraph

Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

13 citing papers in PubMed, 18 citations in OpenAlex.

  1. Observational
  2. Review
  3. Review
  4. Virulence · 2025
    Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
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  11. Article
  12. Article
  13. Emerging Tick-BorneMicroorganisms · 2023
    Review
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

9 authors at 1 institution in 1 country.

Guomei XiaInstitute of Clinical Virology, Department of Infectious Diseases, The Second Hospital of Anhui Medical University, Hefei, China.
Shanshan SunInstitute of Clinical Virology, Department of Infectious Diseases, The Second Hospital of Anhui Medical University, Hefei, China.
Shijun ZhouInstitute of Clinical Virology, Department of Infectious Diseases, The Second Hospital of Anhui Medical University, Hefei, China.
Lei LiDepartment of Infectious Diseases, Anhui Provincial Hospital of Anhui Medical University, Hefei, China.
Xu LiDepartment of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Guizhou ZouInstitute of Clinical Virology, Department of Infectious Diseases, The Second Hospital of Anhui Medical University, Hefei, China.
Cheng HuangInflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China.
Jun LiInflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China.
Zhenhua ZhangInstitute of Clinical Virology, Department of Infectious Diseases, The Second Hospital of Anhui Medical University, Hefei, China.ORCID 0000-0002-8480-9004
Anhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are a few models for predicting the outcomes of patients with severe fever with thrombocytopenia syndrome (SFTS) based on single-center data, but clinicians need more reliable models based on multicenter data to predict the clinical outcomes and effectiveness of drug therapy. METHODOLOGY/PRINCIPAL

findingsThis retrospective multicenter study analyzed data from 377 patients with SFTS, including a modeling group and a validation group. In the modeling group, the presence of neurologic symptoms was a strong predictor of mortality (odds ratio: 168). Based on neurologic symptoms and the joint indices score, which included age, gastrointestinal bleeding, and the SFTS virus viral load, patients were divided into double-positive, single-positive, and double-negative groups, which had mortality rates of 79.3%, 6.8%, and 0%, respectively. Validation using data on 216 cases from two other hospitals yielded similar results. A subgroup analysis revealed that ribavirin had a significant effect on mortality in the single-positive group (P = 0.006), but not in the double-positive or double-negative group. In the single-positive group, prompt antibiotic use was associated with reduced mortality (7.2% vs 47.4%, P < 0.001), even in individuals without significant granulocytopenia and infection, and early prophylaxis was associated with reduced mortality (9.0% vs. 22.8%, P = 0.008). The infected group included SFTS patients with pneumonia or sepsis, while the noninfected group included patients with no signs of infection. The white blood cell count and levels of C-reactive protein and procalcitonin differed significantly between the infection and non-infection groups (P = 0.020, P = 0.011, and P = 0.003, respectively), although the absolute difference in the medians were small. CONCLUSIONS/SIGNIFICANCE: We developed a simple model to predict mortality in patients with SFTS. Our model may help to evaluate the effectiveness of drugs in these patients. In patients with severe SFTS, ribavirin and antibiotics may reduce mortality.

Indexed as

PhlebovirusSevere Fever with Thrombocytopenia SyndromeThrombocytopeniaC-Reactive ProteinHumansRetrospective StudiesRibavirinC-Reactive ProteinRibavirin

Identifiers

PMID36877734
PMCPMC10019728
OpenAlexW4323294543

What OpenQuestion holds

Textfull text, public
LicenceCC BY
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identifiers read12
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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.