ArticleThe American journal of tropical medicine and hygiene2023
Risk Factors for Death in Patients with Severe Fever with Thrombocytopenia Syndrome.
Article in The American journal of tropical medicine and hygiene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.One health (Amsterdam, Netherlands) · 2026Article
- Construction of a novel prognostic model for severe fever with thrombocytopenia syndrome patients based on inflammatory indicators.BMC infectious diseases · 2026Article
- Divergent prognostic roles and risk determinants of myocardial injury in SFTS and HFRS.Tropical medicine and health · 2026Article
- Integration of SFTSV Viral Load, Age, and Double-Negative B-Cells as Prognostic Biomarkers for Severe Fever With Thrombocytopenia Syndrome Outcomes.Journal of immunology research · 2026Article
- Identification of early prognostic biomarkers in Severe Fever with Thrombocytopenia Syndrome using machine learning algorithms.Annals of medicine · 2025Article
- Review
- Prediction of Mortality by Clinical Laboratory Parameters in Severe Fever with Thrombocytopenia Syndrome: A Meta-Analysis.Tropical medicine and infectious disease · 2025Review
- Alarming implications: severe fever with thrombocytopenia syndrome and its biological vectors in the context of climate change.Frontiers in microbiology · 2025Review
- Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients.BMC infectious diseases · 2024Article
- Effect of fasting plasma glucose level in severe fever and thrombocytopenia syndrome patients without diabetes.PLoS neglected tropical diseases · 2024Article
- Observational
- Impact of Glucocorticoid Therapy on 28-Day Mortality in Patients Having Severe Fever with Thrombocytopenia Syndrome in an Intensive Care Unit: A Retrospective Analysis.Journal of inflammation research · 2024Article
- Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome.Frontiers in microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To establish a Cox regression model predicting risk factors for mortality in patients with severe fever with thrombocytopenia syndrome (SFTS), a total of 109 SFTS patients treated at The Second Hospital of Nanjing between June 2016 and October 2020 were included in this study. The patients were categorized into survival (n = 82) and death (n = 27) groups, and the clinical manifestations on admission and laboratory examination were collected. The factors associated with the mortality risk of SFTS patients were explored by univariate and binary logistic regression analyses. The receiver operating characteristic curve was used to evaluate the predictive value of independent influencing factors and the STFS scoring system. Univariate screening showed that the putative influencing factors were age, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, invasive mechanical ventilation, continuous renal replacement therapy, application of vasoactive medications, absolute count of lymphocytes, count of platelets, and levels of albumin and D-dimer (P < 0.05). Binary logistic regression showed that age (P = 0.042), APACHE II score (P = 0.030), and vasoactive medications (P = 0.035) were independent risk factors in SFTS patients. The combined prediction equation for the mortality risk of SFTS patients was "Combined predictor = age + 3.162 × APACHE II score + 22.306 × vasoactive medications," and the predictive value of combined predictor was greater than that of age (P = 0.004) or APACHE II score (P < 0.001). The combination of age, APACHE II score, and vasoactive medications had the highest ability to predict the risk of death. The STFS scoring system could make the clinical application of independent risk factors feasible.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.