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. 2019 Sep 18:2019:5634598.
doi: 10.1155/2019/5634598. eCollection 2019.

Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review

Affiliations

Prognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review

Qi Feng et al. Biomed Res Int. .

Abstract

Background: This study was designed to review the methodology and reporting of gastric cancer prognostic models and identify potential problems in model development.

Methods: This systematic review was conducted following the CHARMS checklist. MEDLINE and EMBASE were searched. Information on patient characteristics, methodological details, and models' performance was extracted. Descriptive statistics was used to summarize the methodological and reporting quality.

Results: In total, 101 model developments and 32 external validations were included. The median (range) of training sample size, number of death, and number of final predictors were 360 (29 to 15320), 193 (14 to 9560), and 5 (2 to 53), respectively. Ninety-one models were developed from routine clinical data. Statistical assumptions were reported to be checked in only nine models. Most model developments (94/101) used complete-case analysis. Discrimination and calibration were not reported in 33 and 55 models, respectively. The majority of models (81/101) have never been externally validated. None of the models have been evaluated regarding clinical impact.

Conclusions: Many prognostic models have been developed, but their usefulness in clinical practice remains uncertain due to methodological shortcomings, insufficient reporting, and lack of external validation and impact studies.

Impact: Future research should improve methodological and reporting quality and emphasize more on external validation and impact assessment.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The flowchart of study selection.
Figure 2
Figure 2
Number of published prognostic models by publication year. The estimated number of prognostic model in 2018 was calculated based on the assumption that the model number was proportionate to the number of months. We found 16 models through 30th May in 2018, and the estimated model number in 2018 would be 16 (12/5)=38.4.

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