Explainable AI for Predicting Hospital Admissions in Heart Failure: ExplAIn-HF

Condition(s) studied

Study summary

The goal of this observational study is to develop and validate an XAI based model that predicts HF events and identifies modifiable con-tributing factors, and to evaluate the added value of integrating high frequency smartwatch data compared to usual care in patients with heart failure. The main objectives of the study are:

1. To develop and validate an XAI based model that predicts HF events and identifies modifiable contributing factors, and to evaluate the added value of integrating high frequency smartwatch data compared to usual care. In the Netherlands usual care includes remote monitoring of heartrate, blood pressure, weight and symptoms.
2. To include insights of the smartwatch into activity patterns, impact on quality of life (KCCQ-12), and patient satisfaction with net promoter score (NPS).

Participants will wear a smartwatch for six months and perform an I-lead ecg with the smartwatch weekly.

Eligibility

Primary outcome measure(s)

• Heart failure (HF) event — From enrollment to the end of follow-up at 6 months
  HF event is defined as: * Increase in diuretic treatment * Increase in NYHA class * Hospitalization caused by HF * Death caused by HF

Trial sites (1)

More Medisch Spectrum Twente trials in the Netherlands

Other trials for the same condition