Evidence map›Paper›PMID 35053493›Full record

ReviewCancers2022

Predictors of Mortality in Patients with Advanced Cancer-A Systematic Review and Meta-Analysis.

Catherine Owusuaa, Simone A Dijkland, Daan Nieboer, Agnes van der Heide, Carin C D van der Rijt

Abstract readReview
In one paragraph

Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Article
  5. Review
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Review
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  13. Is the ECOG-PS similar to the sarcopenia status for predicting mortality in older adults with cancer? A prospective cohort study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023
    Article
  14. Article
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  16. Review
  17. Review
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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

5 authors.

Catherine OwusuaaDepartment of Medical Oncology, Erasmus MC Cancer Institute, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Simone A DijklandDepartment of Public Health, Erasmus MC, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Daan NieboerDepartment of Public Health, Erasmus MC, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Agnes van der HeideDepartment of Public Health, Erasmus MC, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
Carin C D van der RijtDepartment of Medical Oncology, Erasmus MC Cancer Institute, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.ORCID 0000-0001-5996-1242

Funding

Netherlands Organisation for Health Research and Development 844001209
6 · The paper itself

Abstract

To timely initiate advance care planning in patients with advanced cancer, physicians should identify patients with limited life expectancy. We aimed to identify predictors of mortality. To identify the relevant literature, we searched Embase, MEDLINE, Cochrane Central, Web of Science, and PubMed databases between January 2000-April 2020. Identified studies were assessed on risk-of-bias with a modified QUIPS tool. The main outcomes were predictors and prediction models of mortality within a period of 3-24 months. We included predictors that were studied in ≥2 cancer types in a meta-analysis using a fixed or random-effects model and summarized the discriminative ability of models. We included 68 studies (ranging from 42 to 66,112 patients), of which 24 were low risk-of-bias, and 39 were included in the meta-analysis. Using a fixed-effects model, the predictors of mortality were: the surprise question, performance status, cognitive impairment, (sub)cutaneous metastases, body mass index, comorbidity, serum albumin, and hemoglobin. Using a random-effects model, predictors were: disease stage IV (hazard ratio [HR] 7.58; 95% confidence interval [CI] 4.00-14.36), lung cancer (HR 2.51; 95% CI 1.24-5.06), ECOG performance status 1+ (HR 2.03; 95% CI 1.44-2.86) and 2+ (HR 4.06; 95% CI 2.36-6.98), age (HR 1.20; 95% CI 1.05-1.38), male sex (HR 1.24; 95% CI 1.14-1.36), and Charlson comorbidity score 3+ (HR 1.60; 95% CI 1.11-2.32). Thirteen studies reported on prediction models consisting of different sets of predictors with mostly moderate discriminative ability. To conclude, we identified reasonably accurate non-tumor specific predictors of mortality. Those predictors could guide in developing a more accurate prediction model and in selecting patients for advance care planning.

Indexed as

advance care planningadvanced cancermortalitypredictors

Identifiers

PMID35053493
PMCPMC8774229

What OpenQuestion holds

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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.