Preliminary study of surgical audit for overall survival following gastric cancer resection

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

Background

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.

Methods

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).

Results

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.

Conclusion

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.

Validation of an assessment tool: Estimation of Postoperative Overall Survival for Gastric Cancer

BACKGROUND

Benchmarking of long-term surgical outcomes has rarely been attempted. We previously devised a prediction model for assessing the outcome of late survival after surgery, termed the Estimation of Postoperative Overall Survival for Gastric Cancer (EPOS-GC). This study was undertaken to validate EPOS-GC in an external data set.

METHODS

A retrospective cohort study was conducted in 11 cancer care hospitals in Japan, analyzing a consecutive series of patients who underwent elective gastric cancer resection between April 2007 and March 2009. EPOS-GC consists of three tumor-related variables and three physiological variables. The primary endpoint was postoperative overall survival. The observed-to-expected (O/E) ratio of 5-year survival rates was defined as a metric of quality of care. The sample size for O/E was determined as 42.

RESULTS

We included 2045 patients for analysis. The median (95% confidence interval) follow-up time was 5.1 (1.2-6.8) years for censored patients. Although EPOS-GC demonstrated a good discriminative power (Harrell's C-index, 95% confidence interval: 0.80, 0.79-0.83), the calibration plot revealed that EPOS-GC underestimated 5-year survival rates in the high-risk group. Therefore, we recalibrated the model with Cox's regression analysis. The recalibrated EPOS-GC showed a good calibration, preserving the high discriminative power (C-index, 95% confidence interval: 0.80, 0.78-0.82). The O/E among hospitals according to the recalibrated EPOS-GC ranged between 0.87 and 1.27. The O/E correlated with hospital volumes (Spearman's correlation = 0.76, n = 11, p = .006).

CONCLUSION

EPOS-GC with recalibration can convey risk-adjusted quality assurance regarding late survival following gastric cancer resection.