Prognostic value of stress hyperglycemia ratio, hemoglobin glycation index, and glycemic variability for postoperative atrial fibrillation: a machine learning-based prediction model

Comprehensive Summary

This article presents a good case for machine learning to help reduce postoperative atrial fibrillation (POAF) in cardiac surgery patients. After analyzing 2,177 patients, they found that variables such and stress hyperglycemia ratio (SHR), glycemic variability (GV), and hemoglobin glycation index (HGI) were linked to higher rates of POAF. They indicated that SHR and HGI were independent predictors of POAF and that SHR and GV had non-linear relationships with POAF, suggesting that once these values got to a certain level, risk significantly increased. Importantly higher values of HGI displayed a protective effect.

Outcomes and Implications

The study also used an AdaBoost-based web tool to check accuracy in determining POAF outcomes. They found that with SHR alone, the system yielded only a 0.556 AUC, while using a multimodal system yielded an AUC of 0.74. This data indicates that using a multivariable system in machine learning in this case may be superior than just looking at one glycemic variable alone. This study, however, only used one hospital's data, which brings up concerns of replicative potential, and draws questions to whether this data can be generalizable. It would be helpful to see a larger multi-hospital data-set to come to better conclusions whether machine learning model predictions for POAF could be universally used as an indicator for perioperative monitoring.