Projects per year
Design/setting A narrative systematic review of international peer reviewed journals
Data source Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.
Primary and secondary outcome measures Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.
Results 263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).
Conclusions Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.
- prognostic model development
- pancreatic ductal adenocarcinoma
- post resection
A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinomaBradley, A., Van der Meer, R. & McKay, C. J., 9 Sep 2019, In : PLoS ONE. 14, 9, 14 p., e0222270.
Research output: Contribution to journal › ArticleOpen AccessFile1 Citation (Scopus)1 Downloads (Pure)
Making personalized predictions of poor outcome post resection of pancreatic ductal adenocarcinoma (PDAC): a prognostic bayesian network with pre- and post-operative applicationBradley, A., Van der Meer, R., McKay, C. & Jamieson, N., 5 Jun 2019, In : Pancreatology. 19, S1, p. S122 1 p., P6-13.
Research output: Contribution to journal › Meeting abstractOpen AccessFile2 Citations (Scopus)2 Downloads (Pure)
Personalized prognostic bayesian network for pancreatic cancer: delivering personalized pancreatic cancer management throughout the patient journeyBradley, A., Van der Meer, R., McKay, C. & Jamieson, N., 5 Jun 2019, In : Pancreatology. 19, S1, p. S31-S32 2 p., P1-40.
Research output: Contribution to journal › Meeting abstractOpen AccessFile4 Citations (Scopus)3 Downloads (Pure)