Evidence map›Paper›PMID 40474225›Full record

ArticleVirology journal2025

Analysis of the clinical efficacy of CCBs in patients with severe fever with thrombocytopenia syndrome combined with hypertension.

Ziruo Ge, Chenxi Zhao, Yanli Xu, Xiaoyu Xue, Wei Pan, Wei Zhang, Zhihai Chen, Ling Lin, Di Tian

Abstract read
In one paragraph

Article in Virology journal, 2025. 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

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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Ziruo Ge *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China.
Chenxi Zhao *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China.
Yanli Xu *Department of Infectious Diseases, Yantai Qishan Hospital, 62 Huanshan Road, Zhifu District, Yantai, China.
Xiaoyu XueNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China.
Wei PanDepartment of Infectious Diseases, Yantai Qishan Hospital, 62 Huanshan Road, Zhifu District, Yantai, China.
Wei ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China.
Zhihai ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China.
Ling LinDepartment of Infectious Diseases, Yantai Qishan Hospital, 62 Huanshan Road, Zhifu District, Yantai, China. linling4012@163.com.
Di TianNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, 8 Jingshun East Street, Chaoyang District, Beijing, China. tiandi412@hotmail.com.

Funding

The National Key R&D Program of China 2022YFF0705200the National Natural Science Foundation of China 82072295the National Science and Technology Major Project of China 2018ZX09711003
6 · The paper itself

Abstract

backgroundCalcium channel blockers (CCBs), commonly used in the treatment of various cardiovascular diseases, have shown promising potential in inhibiting the formation of viral inclusion bodies by lowering the level of intracellular Ca2+, thereby reducing viral replication. We conducted a retrospective single-center cohort study of SFTS patients with hypertension to assess the impact of CCB therapy on SFTS.

methodsSFTS patients with hypertension treated in Qishan Hospital of Yantai from May 1, 2018, to December 31, 2022, were included. According to the admission criteria, 94 patients with SFTS were divided into the CCBs group (N = 30) and the control group (patients who took other antihypertensive drugs) (N = 64). We observed the severity of the disease, fatality rate, nucleic acid conversion time, nucleic acid's positive rate, and the clinical improvement of the two groups. The effects of different CCBs on nucleic acid conversion were evaluated.

resultsThere was a significant difference in the severity rate between the CCBs and control groups (63.3% vs. 82.8%, P = 0.038). The time of nucleic acid conversion in the CCBs group was significantly shorter than in the control group (P = 0.001). The nucleic acid negative conversion curve differed between the two groups (Log Rank P = 0.010), and the CCBs group was better. The frequency of vomiting and nervous system symptoms in the CCBs group was significantly lower than in the control group (P = 0.049,0.038), and the platelet level was significantly higher in the former than in the latter (P = 0.015). The conversion rate of nucleic acid in patients taking nifedipine was significantly higher than that in patients taking other CCBs (p = 0.036).

conclusionsOur research indicates that CCBs may aid in decreasing disease severity, easing clinical symptoms, and potentially slowing the advancement to severe illness in patients with SFTS.

Indexed as

Calcium Channel BlockersHypertensionSevere Fever with Thrombocytopenia SyndromeAdultAgedAntihypertensive AgentsFemaleHumansMaleMiddle AgedPhlebovirusRetrospective StudiesSeverity of Illness IndexTreatment OutcomeAntihypertensive AgentsCalcium Channel BlockersCCBsClinical efficacyHypertensionSFTS

Identifiers

PMID40474225
PMCPMC12142868

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Registered trials

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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.