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

ABSTRACT: Clinical 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.MEDLINE, 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).A 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.Most 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.

SUBMITTER: van den Boorn HG

PROVIDER: S-EPMC5805284 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

van den Boorn H G HG Engelhardt E G EG van Kleef J J Sprangers M A G MAG van Oijen M G H MGH Abu-Hanna A A Zwinderman A H AH Coupé V M H VMH van Laarhoven H W M HWM

PloS one 20180208 2

<h4>Background</h4>Clinical 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.<h4>Methods</h4>MEDLINE, EMBASE, PsycINFO, C ...[more]

PMID: 29420636

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Project description:AimIntestinal metaplasia (IM) and gastric atrophy (GA) are precancerous lesions in the stomach. There is a large debate on natural course of these lesions and surveillance strategy in these patients. This meta-analysis was aimed to find the most appropriate follow up and the rate of progression from IM and GA to GC.MethodsThis meta-analysis is followed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Electronic databases including EMBASE, PubMed, Web of Science databases, Scopus, and the Cochrane Library were searched until July 2018. Cochran's Q test and I-square (I2) test were used to examine heterogeneity across included studies. We pooled data using random-effect or fixed effect models indicated as incidence rate or proportion with 95% confidence intervals (CI). The variables of study included demographic data, endoscopy interval, follow up interval and time, GA and IM type and GC stage. Moreover, incidence rate of GC and progress rate, regress and persistence proportion in both GA and IM patients were assessed.ResultsOverall, 68 original articles out of 32981 citations were included in our meta-analysis. The pooled GC incidence rate in patients with GA was 1.24 (95% CI, 0.80, 1.76; I2: 83.6%) cases per 1,000 person-years. The rates of later diagnosis of IM and gastric dysplasia in patients with GA were estimated as 41.42 (95% CI, 3.11, 64.45; I2: 95.6%) and 6.23 (95% CI, 2.34, 11.46; I2: 83.0%) cases per 1,000 person-years, respectively. The pooled regressed proportion was 32.23 (95% CI, 18.07-48.02; I2: 94.0%) and the persistence proportion was 38.83 (95% CI, 20.20-59.13; I2: 97.0%) per 100 observations in GA patients. In IM studies, the pooled incidence rate of GC was 3.38 (95% CI, 2.13, 4.85; I2: 93.4%) cases per 1,000 person-years. The progressed rate to dysplasia in IM patient was estimated to be 12.51 (95% CI, 5.45, 22.03; I2: 95.1%) cases per 1,000 person-years. The pooled regressed proportion was 31.83 (95% CI, 25.48-38.51; I2: 91.0%) and the persistence proportion was 43.46 (95% CI, 32.52-54.71; I2: 96.0%) per 100 observations in IM patients.ConclusionOverall, the incidence of GC in patients with IM and GA are low but there is heterogeneity in data with the highest rate in Asian, males with those with incomplete IM. There is probability of regression or persistence without progression in patients with IM and GA who receive appropriate management.

Project description:ObjectiveTo describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.DesignSystematic review and meta-analysis.Data sourceMedline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studiesStudies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months.Primary and secondary outcome measuresModel discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.ResultsSixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled.ConclusionSome promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.Prospero registration numberCRD42020159839.

Project description:PurposePregnancy-related critical illness leads to death for 3-14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.MethodsSearch strategy: MEDLINE, EMBASE and Scopus, from inception to May 2017. Selection criteria: Trials or observational studies evaluating risk prediction models for maternal mortality. Data collection and analysis: Two reviewers independently assessed studies for eligibility and methodological quality, and extracted data on prediction performance.ResultsThirty-eight studies that evaluated 12 different mortality prediction models were included. Mortality varied across the studies, with an average rate 10.4%, ranging from 0 to 41.7%. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index had the best performance, were developed and validated from studies of obstetric population with a low risk of bias. The CIPHER applies to critically ill obstetric patients (discrimination: area under the receiver operating characteristic curve (AUC) 0.823 (0.811-0.835), calibration: graphic plot [intercept-0.09, slope 0.92]). The Maternal Severity Index applies to hospitalized obstetric patients (discrimination: AUC 0.826 [0.802-0.851], calibration: standardized mortality ratio 1.02 [0.86-1.20]).ConclusionsDespite the high heterogeneity of the study populations and the limited number of studies validating the finally eligible prediction models, the CIPHER and the Maternal Severity Index are recommended for use among critically ill and hospitalized pregnant and postpartum women for risk adjustment in clinical research and quality improvement studies. Neither index has sufficient discrimination to be applicable for clinical decision making at the individual patient level.

Dataset Information

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

Publications

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

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