## 4 Clinical Applications of AI to CHF Management
### Monitoring Devices and AI-Powered Platforms
Along the same line, a retrospective analysis by [PERSON] showed that the HeartLogic algorithm might be helpful to detect the gradual worsening of HF and to stratify the risk of HF decompensation [42]. On the other hand, [PERSON] et al. evaluated the clinical impact of the HeartLogic algorithm observing a relevant reduction in the total number of HF hospitalisations, which declined from 27 in the pre-activation period to 7 in the post-activation period (\(p\) = 0.003) [43]. Similar results were reported for the RE-HEART registry: the HeartLogic algorithm was shown to predict HF decompensation or clinically relevant events in more than half of the alerts, with an average of 20 days in advance [44]. The favourable impact of these technologies was also explored in the SELENE HF study, which validated the algorithm HeartInsight for the prediction of heart failure (HF) hospitalisations using remote monitoring data transmitted by implanted defibrillators. The study included patients with an ICD capable of atrial sensing or a CRT-D, left ventricular ejection fraction (LVEF) less than 35 per cent, and a New York Heart Association (NYHA) class II or III before the implantation. All devices used the Home Monitoring technology characterised by daily automatic data transmissions over the Global System for Mobile Communication network. With the developed algorithm, two-thirds of first post-implant HF hospitalisations could be predicted timely with only 0.7 false alerts per patient year [45]. Another platform for HF remote monitoring of implantable devices was tested in the TriageHF, originally developed and validated by [PERSON] et al. [46] The primary aim of the TRIAGE-HF trial [47] was to correlate cardiac implantable electronic device-generated heart failure risk status (HFRS) with signs and symptoms associated with worsening heart failure (HF). The algorithm could predict the worsening of HF with a sensitivity of 90 per cent. Table 2 summarises these approaches. In the RESPOND-CRT trial, 998 patients were randomised to receive weekly automatic CRT optimisation with SonR vs. an echo-guided optimisation of AV and VV timings. Responder rates were 75.0 per cent in the SonR arm and 70.4 per cent in the Echo arm. At an overall mean follow-up of about 700 days, SonR was associated with a 35 per cent risk reduction in HF hospitalisation [48]. Recent evidence from OptiVol algorithm, available on CRT-D and CRT-P devices was in the PARTNERS HF study that assessed the relationship between OptiVol-powered fluid monitoring data and clinically relevant pulmonary congestion events [49]. Insertable cardiac monitors (ICMs) have become widely adopted in clinical electrophysiology practice. Their utility in the diagnosis of worsening conditions in patients with heart failure was analysed by the LUX-Dx PERFORM, a multicenter, prospective, single-arm, post-market, observational study with a planned enrollment of up to 827 patients, demonstrating the safety of insertion, high data transmission rates, the ability to detect atrial flutter, and the feasibility of remote programming to optimise arrhythmia detection and improve clinical workflow. At the same time, LINQ II ICM enables remote programming capability for all device parameters post-insertion from the clinic, which may reduce patient office visits and scheduling hassles [50]. These algorithm developed to monitor HF might be further improved through AI and/or the integration with other tools, such as CardioMEMS or ReDS, described in Table 1.