Evidence map›Paper›PMID 33414260›Full record

ArticleBMJ open respiratory research2021

Predicting survival in malignant pleural mesothelioma using routine clinical and laboratory characteristics.

Samal Gunatilake, David Lodge, Daniel Neville, Thomas Jones, Carole Fogg, Paul Bassett, Selina Begum, Sumita Kerley, Laura Marshall, Sharon Glaysher and 4 more

Abstract read
In one paragraph

Article in BMJ open respiratory research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. [Recent Advances in Medical Treatment of Malignant Pleural Mesothelioma].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. 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

14 authors.

Samal GunatilakeDepartment of Respiratory Medicine, Hampshire Hospitals NHS Foundation Trust, Winchester, Hampshire, UK samal.gunatilake@hhft.nhs.uk.
David LodgeDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.ORCID 0000-0002-3762-0284
Daniel NevilleDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Thomas JonesDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Carole FoggDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Paul BassettStatsconsultancy Ltd, Amersham, Buckinghamshire, UK.
Selina BegumDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Sumita KerleyDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Laura MarshallDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Sharon GlaysherDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Scott ElliottDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.
Rebecca StoresInstitute of Biological and Biomedical Sciences, University of Portsmouth, Portsmouth, Hampshire, UK.
Lesley BishopDepartment of Respiratory Medicine, Queen Alexandra Hospital, Portsmouth, Portsmouth, UK.
Anoop ChauhanDepartment of Respiratory Research & Innovation, Portsmouth Hospitals NHS Trust, Portsmouth, Portsmouth, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe prognosis of malignant pleural mesothelioma (MPM) is poor, with a median survival of 8-12 months. The ability to predict prognosis in MPM would help clinicians to make informed decisions regarding treatment and identify appropriate research opportunities for patients. The aims of this study were to examine associations between clinical and pathological information gathered during routine care, and prognosis of patients with MPM, and to develop a 6-month mortality risk prediction model.

methodsA retrospective cohort study of patients diagnosed with MPM at Queen Alexandra Hospital, Portsmouth, UK between December 2009 and September 2013. Multivariate analysis was performed on routinely available histological, clinical and laboratory data to assess the association between different factors and 6-month survival, with significant associations used to create a model to predict the risk of death within 6 months of diagnosis with MPM.

results100 patients were included in the analysis. Variables significantly associated with patient survival in multivariate analysis were age (HR 1.31, 95% CI 1.09 to 1.56), smoking status (current smoker HR 3.42, 95% CI 1.11 to 4.20), chest pain (HR 2.14, 95% CI 1.23 to 3.72), weight loss (HR 2.13, 95% CI 1.18 to 3.72), platelet count (HR 1.05, 95% CI 1.00 to 1.10), urea (HR 2.73, 95% CI 1.31 to 5.69) and adjusted calcium (HR 1.47, 95% CI 1.10 to 1.94). The resulting risk model had a c-statistic value of 0.76. A Hosmer-Lemeshow test confirmed good calibration of the model against the original dataset.

conclusionRisk of death at 6 months in patients with a confirmed diagnosis of MPM can be predicted using variables readily available in clinical practice. The risk prediction model we have developed may be used to influence treatment decisions in patients with MPM. Further validation of the model requires evaluation of its performance on a separate dataset.

Indexed as

Lung NeoplasmsMesotheliomaMesothelioma, MalignantPleural NeoplasmsHumansLaboratoriesRetrospective Studiesasbestos induced lung diseaselung cancermesothelioma

Identifiers

PMID33414260
PMCPMC7797245

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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.