# Development and validation of simplified prognostic models for 14- and 30-day mortality in advanced cancer: beyond the glasgow prognostic score

## TL;DR

This study developed and validated new models to predict 14- and 30-day mortality in advanced cancer patients, outperforming existing tools like the Glasgow Prognostic Score.

## Contribution

The study introduces simplified, high-performing prognostic models for short-term mortality in advanced cancer.

## Key findings

- GPS and mGPS showed poor performance for short-term mortality prediction (AUC 0.52–0.55).
- The full model improved discrimination with AUCs of 0.663 and 0.654 for 14- and 30-day mortality.
- The simplified eight-variable model achieved comparable performance with AUCs of 0.652 and 0.636.

## Abstract

Reliable week-level survival prediction is needed for hospice referral and end-of-life decisions in advanced cancer. The Glasgow Prognostic Score (GPS) and modified GPS (mGPS) are widely used for long-term outcomes, but their short-term value is uncertain.

In a retrospective cohort of 6,063 patients with advanced cancer from a single center (5,315 development; 748 external validation), we evaluated GPS/mGPS for 14- and 30-day mortality and developed two logistic-regression models using routinely available variables: an L1-regularized “full” model and a simplified eight-variable model selected via least absolute shrinkage and selection operator. Performance was assessed by area under the receiver operating characteristic curve (AUC), Brier score, calibration, and decision-curve analysis.

GPS and mGPS demonstrated poor discrimination and suboptimal calibration for short-term mortality (AUC 0.52–0.55). In contrast, the full model improved discrimination for 14- and 30-day mortality (AUCs 0.663 and 0.654, respectively), with lower Brier scores and better calibration. The simplified model achieved comparable performance (AUCs 0.652 and 0.636). Both models provided modest net clinical benefit across clinically relevant threshold probabilities.

GPS and mGPS are inadequate for week-level prognostication in advanced cancer, whereas simplified models integrating objective clinical and laboratory variables improve discrimination, calibration, and potential clinical utility. The eight-variable model may facilitate real-world implementation in end-of-life care.