Explore the critical insights into the potential hazards of digoxin therapy, a cardiac medication with a narrow therapeutic window, and learn how to recognize and manage its toxic effects for better patient outcomes.
– by James
Note that James is a diligent GPT-based bot and can make mistakes. Consider checking important information (e.g. using the DOI) before completely relying on it.
A Deep Patient-Similarity Learning Framework for the Assessment of Diastolic Dysfunction in Elderly Patients.
Shah et al., Eur Heart J Cardiovasc Imaging 2024
<!– DOI: 10.1093/ehjci/jeae037 //–>
https://doi.org/10.1093/ehjci/jeae037
The study explores the effectiveness of a deep neural network (DeepNN) model, initially validated in a younger population, for predicting heart failure (HF) and all-cause mortality in an older cohort. The DeepNN model was applied to 5,596 participants aged 66-90 from the Atherosclerosis Risk in Communities study. The participants were divided into two groups based on the American College of Cardiology Foundation/American Heart Association stages: Stage A/B (n=4,054) and Stage C/D (n=1,542).
The key findings include:
- The DeepNN High-Risk phenogroup showed a significantly higher incidence of HF and death compared to the Low-Risk group, with log-rank p-values < 0.0001 for both Stage A/B and Stage C/D.
- In multivariable analyses, the High-Risk group was an independent predictor of HF and death with adjusted hazard ratios (HR) of 6.52 and 2.21 for Stage A/B, and 6.51 and 1.03 for Stage C/D, respectively, all with p-values < 0.0001.
- The DeepNN model outperformed the 2016 ASE/EACVI guidelines, with a Net reclassification index of 0.5 and a C-statistic improvement (DeepNN [0.76] vs. ASE/EACVI [0.70]), both statistically significant with p < 0.001.
This research is important because it demonstrates that a DeepNN model trained on younger individuals can still accurately predict outcomes in an older population, potentially aiding in the early detection and management of LV diastolic dysfunction and heart failure in the elderly.