Evidence map›Paper›PMID 29420636›Full record

SynthesisPloS one2018

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis.

H G van den Boorn, E G Engelhardt, J van Kleef, M A G Sprangers, M G H van Oijen, A Abu-Hanna, A H Zwinderman, V M H Coupé, H W M van Laarhoven

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 5 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

37 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Development of a prognostic prediction model incorporatingJournal of gastrointestinal oncology · 2026
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  16. Risk prediction model for gastric cancer within 5 years in healthy Korean adults.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024
    Article
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  18. Predicting mortality in patients with anastomotic leak after esophagectomy: development of a prediction model using data from the TENTACLE-Esophagus study.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023
    Article
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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

9 authors.

H G van den BoornCancer Center Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0003-4269-834X
E G EngelhardtAmsterdam Public Health Research Institute, Amsterdam, The Netherlands.
J van KleefCancer Center Amsterdam, Amsterdam, The Netherlands.
M A G SprangersAmsterdam Public Health Research Institute, Amsterdam, The Netherlands.
M G H van OijenCancer Center Amsterdam, Amsterdam, The Netherlands.
A Abu-HannaDepartment of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
A H ZwindermanDepartment of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
V M H CoupéDepartment of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.
H W M van LaarhovenCancer Center Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis.

methodsMEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices).

resultsA total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85.

conclusionMost available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient's HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients' information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.

Indexed as

Models, TheoreticalEsophageal NeoplasmsHumansQuality of LifeStomach NeoplasmsSurvival Analysis

Identifiers

PMID29420636
PMCPMC5805284

What OpenQuestion holds

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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.