Open AccessPrognostic Models for Predicting Overall Survival in Patients with Primary Gastric Cancer: A Systematic Review
Author(s) -
Qi Feng,
Margaret May,
Suzanne M Ingle,
Ming Lu,
ZuYao Yang,
JinLing Tang
Publication year - 2019
Publication title -
biomed research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 126
eISSN - 2314-6141
pISSN - 2314-6133
DOI - 10.1155/2019/5634598
Subject(s) - checklist , medicine , medline , sample size determination , systematic review , medical physics , descriptive statistics , intensive care medicine , statistics , psychology , mathematics , political science , law , cognitive psychology
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.