Comparison of Body Weight Trend Algorithms for Prediction of Heart Failure Related Events in Home Care Setting.

BACKGROUND

Automatic event detection is used in telemedicine based heart failure disease management programs supporting physicians and nurses in monitoring of patients' health data.

OBJECTIVES

Analysis of the performance of automatic event detection algorithms for prediction of HF related hospitalisations or diuretic dose increases.

METHODS

Rule-Of-Thumb and Moving Average Convergence Divergence (MACD) algorithm were applied to body weight data from 106 heart failure patients of the HerzMobil-Tirol disease management program. The evaluation criteria were based on Youden index and ROC curves.

RESULTS

Analysis of data from 1460 monitoring weeks with 54 events showed a maximum Youden index of 0.19 for MACD and RoT with a specificity > 0.90.

CONCLUSION

Comparison of the two algorithms for real-world monitoring data showed similar results regarding total and limited AUC. An improvement of the sensitivity might be possible by including additional health data (e.g. vital signs and self-reported well-being) because body weight variations obviously are not the only cause of HF related hospitalisations or diuretic dose increases.