The predictors of mortality in patients with acromegaly: an old debate revisited by machine learning

Abstract

Mortality studies on acromegaly face two fundamental problems: large registries suffer from heterogeneous approaches among centers, while homogeneous single-center studies are limited by sample size. Furthermore, conventional statistical methods did not produce consistent results in mortality predictors. These challenges fueled the debate about which factors most significantly influence mortality in this orphan disease. Systematic analysis of complex interactions between multiple clinical variables in a large homogenous dataset may overcome these concerns. In this setting, machine learning (ML) might be useful.