ABSTRACT
Clinical prediction models promise a future of precision drug therapy. However, very few models are used in practice. Although models can eliminate unwanted clinical guesswork, several barriers hinder model implementation in practice. Here we discuss the less well-recognized barriers hindering model implementation for precision prescribing, highlighting areas that warrant attention, primarily relating to ethical and regulatory considerations that are requisite in paving the way towards a future of precision pharmacotherapy.
Subject(s)
Data Mining/methods , Drug Development/methods , Drug Discovery/methods , Evidence-Based Medicine/methods , Models, Theoretical , Precision Medicine/methods , Translational Research, Biomedical/methods , Animals , Databases, Factual , Humans , Learning , Models, Animal , Patient Safety , Risk AssessmentABSTRACT
BACKGROUND: Stage IV colorectal cancer patients with unresectable metastasis who undergo elective primary tumour resection experience heterogeneous post-operative survival. We aimed to develop a scoring model for predicting post-operative survival using pre-operative variables to identify patients who are least likely to experience extended survival following the procedure. METHODS: Survival data were collected from stage IV colorectal cancer patients who had undergone elective primary tumour resection between January 1999 and December 2007. Coefficients of significant covariates from the multivariate Cox regression model were used to compute individual survival scores to classify patients into three prognostic groups. A survival function was derived for each group via Kaplan-Meier estimation. Internal validation was performed. RESULTS: Advanced age (hazard ratio, HR 1.43 (1.16-1.78)); poorly differentiated tumour (HR 2.72 (1.49-5.04)); metastasis to liver (HR 1.76 (1.33-2.33)), lung (HR 1.37 (1.10-1.71)) and bone (HR 2.08 ((1.16-3.71)); carcinomatosis (HR 1.68 (1.30-2.16)); hypoalbuminaemia (HR 1.30 (1.04-1.61) and elevated carcinoembryonic antigen levels (HR 1.89 (1.49-2.39)) significantly shorten post-operative survival. The scoring model separated patients into three prognostic groups with distinct median survival lengths of 4.8, 12.4 and 18.6 months (p < 0.0001). Internal validation revealed a concordance probability estimate of 0.65 and a time-dependent area under receiver operating curve of 0.75 at 6 months. Temporal split-sample validation implied good local generalizability to future patient populations (p < 0.0001). CONCLUSION: Predicting survival following elective primary tumour resection using pre-operative variables has been demonstrated with the scoring model developed. Model-based survival prognostication can support clinical decisions on elective primary tumour resection eligibility.
