Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 420

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score

Open access
Guo, Lingxi, Wei, Dong, Zhang, Xinxin, Wu, Yurong, Li, Qingyun, Zhou, Min, Qu, Jieming·2019·Frontiers in Microbiology

Abstract

Objective The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. Methods A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. Results 76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR=5.20, 95%CI 1.41-12.52, p=0.010; 5 points), lymphocyte≤0.8*109/L (OR=4.53, 95%CI 2.55-8.05, p<0.001; 4 points), bacterial coinfection (OR=3.71, 95%CI 2.11-6.51, p<0.001; 4 points), acute-smoker (OR=3.19, 95% CI 1.34-6.26, p=0.001; 3 points), quit-smoker (OR=2.18, 95% CI 0.99-4.82, p=0.054; 2 points), hypertension (OR=2.39, 95% CI 1.55-4.26, p=0.003; 2 points) and age≥60 years (OR=2.14, 95% CI 1.04-4.39, p=0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs 0.717, p<0.001). Conclusions Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fmicb.2019.02752

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 27

  1. Barlow, 2007, The CURB65 pneumonia severity score outperforms generic sepsis and early warning scores in predicting mortality in community-acquired pneumonia., Thorax, 62, 253, 10.1136/thx.2006.067371 · DOI
  2. Bender, 1992, Transgenic mice lacking class I major histocompatibility complex-restricted T cells have delayed viral clearance and increased mortality after influenza virus challenge., J. Exp. Med., 175, 1143, 10.1084/jem.175.4.1143 · DOI · Paper page
  3. Charles, 2009, Rocket science and the Infectious Diseases Society of America/American Thoracic Society (IDSA/ATS) guidelines for severe community-acquired pneumonia., Clin. Infect. Dis., 48, 10.1086/599227 · doi:10.1086/599227
  4. Chemaly, 2012, The characteristics and outcomes of parainfluenza virus infections in 200 patients with leukemia or recipients of hematopoietic stem cell transplantation., Blood, 119, 2738, 10.1182/blood-2011-08-371112 · doi:10.1182/blood-2011-08-371112
  5. Das, 2015, Viruses detected by systematic multiplex polymerase chain reaction in adults with suspected community-acquired pneumonia attending emergency departments in France., Clin. Microbiol. Infect., 21, e1, 10.1016/j.cmi.2015.02.014 · DOI
  6. Elizaga, 2001, Parainfluenza virus 3 infection after stem cell transplant: relevance to outcome of rapid diagnosis and ribavirin treatment., Clin. Infect. Dis., 32, 413, 10.1086/318498 · doi:10.1086/318498
  7. Gao, 2013, Clinical findings in 111 cases of influenza a (H7N9) virus infection., N. Engl. J. Med., 368, 2277, 10.1056/NEJMoa1305584 · DOI · Paper page
  8. Heylen, 2017, Drug candidates and model systems in respiratory syncytial virus antiviral drug discovery., Biochem. Pharmacol., 127, 1, 10.1016/j.bcp.2016.09.014 · DOI
  9. Jefferson, 2014, Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments., BMJ Apr., 9, 10.1136/bmj.g2545 · DOI · Paper page
  10. Jennings, 2008, Incidence and characteristics of viral community-acquired pneumonia in adults., Thorax, 63, 42, 10.1136/thx.2006.075077 · DOI · Paper page
  11. La Gruta, 2007, A question of self-preservation: immunopathology in influenza virus infection., Immunol. Cell Biol., 85, 85, 10.1038/sj.icb.7100026 · DOI · Paper page
  12. Lee, 2017, Comparison of Efficacy of Intravenous Peramivir and Oral Oseltamivir for the Treatment of Influenza: Systematic Review and Meta-Analysis., Yonsei Med. J., 58, 778, 10.3349/ymj.2017.58.4.778 · doi:10.3349/ymj.2017.58.4.778
  13. Li, 2017, Pandemic and Avian Influenza A Viruses in Humans: Epidemiology, Virology, Clinical Characteristics, and Treatment Strategy., Clin. Chest Med., 38, 59, 10.1016/j.ccm.2016.11.005 · DOI
  14. Li, 2012, Type I interferon induction during influenza virus infection increases susceptibility to secondary Streptococcus pneumoniae infection by negative regulation of gammadelta T cells., J. Virol., 86, 12304, 10.1128/JVI.01269-12 · DOI · Paper page
  15. Mandell, 2007, Infectious Diseases Society of America/American Thoracic Society consensus Guidelines on the Management of Community–Acquired Pneumonia in Adults., Clin. Infect. Dis., 44, S27, 10.1086/511159 · DOI · Paper page
  16. Mortensen, 2003, Assessment of mortality after long-term follow-up of patients with community-acquired pneumonia., Clin. Infect. Dis., 37, 1617, 10.1086/379712 · DOI
  17. Nussing, 2018, Innate and adaptive T cells in influenza disease., Front. Med., 12, 34, 10.1007/s11684-017-0606-8 · DOI
  18. Ogata, 2001, Association between natural killer cell activity and infection in immunologically normal elderly people., Clin. Exp. Immunol., 124, 392, 10.1046/j.1365-2249.2001.01571.x · DOI · Paper page
  19. Pappalardo, 2013, Predicting mortality risk in patients undergoing venovenous ECMO for ARDS due to influenza A (H1N1) pneumonia: the ECMOnet score., Intensive Care Med., 39, 275, 10.1007/s00134-012-2747-1 · DOI · Paper page
  20. Pawelec, 2002, T cells and aging, January 2002 update., Front. Biosci., 7, d1056, 10.2741/a831 · DOI · Paper page
  21. Riquelme, 2011, Predicting mortality in hospitalized patients with 2009 H1N1 influenza pneumonia., Int. J. Tuberc. Lung. Dis., 15, 542, 10.5588/ijtld.10.0539 · DOI
  22. Shi, 2017, Mortality prediction to hospitalized patients with influenza pneumonia: PO2/FiO2 combined lymphocyte count is the answer., Clin. Respir. J., 11, 352, 10.1111/crj.12346 · DOI
  23. Shorr, 2017, Viruses are prevalent in non-ventilated hospital-acquired pneumonia., Respir. Med., 122, 76, 10.1016/j.rmed.2016.11.023 · DOI
  24. Uranga, 2018, Predicting 1-year mortality after hospitalization for community-acquired pneumonia., PloS One, 13, 10.1371/journal.pone.0192750 · doi:10.1371/journal.pone.0192750
  25. Viasus, 2016, Biomarkers for predicting short-term mortality in community-acquired pneumonia: a systematic review and meta-analysis., J. Infect., 72, 273, 10.1016/j.jinf.2016.01.002 · DOI
  26. Viasus, 2011, Factors associated with severe disease in hospitalized adults with pandemic (H1N1) 2009 in Spain., Clin. Microbiol. Infect., 17, 738, 10.1111/j.1469-0691.2010.03362.x · DOI
  27. Zhou, 2019, Disease severity and clinical outcomes of community-acquired pneumonia caused by non-influenza respiratory viruses in adults: a multicentre prospective registry study from the CAP-China Network., Eur. Respir. J., 54, 10.1183/13993003.02406-2018 · DOI

Cited by

See all 420

Metadata

Authors
Guo, L., Wei, D., Zhang, X., Wu, Y., Li, Q., Zhou, M., & Qu, J.
Venue
Frontiers in Microbiology
Published
2019-12-03
Volume
10
Pages
2752-2752
ISSN
1664-302X (ISSN-L)
OpenAlex
W2991899552
Cited by
420
References
4
FWCI
57.13
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score. https://app.cochat.ai/papers/10.3389/fmicb.2019.02752
The paper
Guo, L., Wei, D., Zhang, X., Wu, Y., Li, Q., Zhou, M., & Qu, J. (2019). Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score. Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2019.02752

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.