External validation of a model to identify cardiometabolic predictors of mortality in cancer survivors

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Cancer survivors are at risk of cardiovascular disease because of shared risk factors and effects of treatment. There are few tools to assist in estimating the risk of poor outcomes relating to cardiovascular disease in cancer survivors and identifying those at risk. The purpose of this study was to externally validate a model for predicting the risk of increased mortality in female cancer survivors. Methods: A risk prediction model originally developed using data from the general population of older adults from the Australian Longitudinal Study of Ageing was externally validated using data from two Australian Longitudinal Study on Women’s Health (ALSWH) cohorts. Three measures of discrimination were calculated. Calibration was assessed by visualising a graph of the model predictions and observed events. Results: The ALSWH cohorts consisted of 1764 women (aged 73–78 years) and 1833 women (aged 47–52 years). Discrimination was acceptable with the Harrell C-index and the Gonen and Heller K statistic both greater than 0.5. The model explained up to 30% of the variation in mortality. Calibration showed that the recalibrated model performed best in years 8–10 suggesting that the model is better at predicting survival for those with a higher probability of surviving. Overall, model performance was better in the 47–52 years cohort than in the older cohort. Conclusion: We have externally validated a model of cardiometabolic predictors of mortality in female cancer survivors. The model can serve as a basis of clinical tool to assist with decision-making regarding potential risk reduction strategies in this population.

Original languageEnglish
Number of pages9
JournalSupportive Care in Cancer
Early online date5 Mar 2021
DOIs
Publication statusE-pub ahead of print - 5 Mar 2021

Keywords

  • Cancer
  • Cardiovascular disease
  • Model
  • Oncology
  • Screening
  • Survivor

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