Pre-Symptomatic Detection of Impending Decompensation in Heart Failure Through
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 123
- 试验地点
- 3
- 主要终点
- Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration
研究概览
简要总结
PRE-DETECT-HF is a prospective, single-arm observational study evaluating a voice-based machine learning algorithm for early detection of heart failure decompensation. 123 patients hospitalized for acute decompensated or de-novo heart failure will be enrolled across three sites in the Netherlands and Spain.
Patients make daily voice recordings via a smartphone app and answer symptom questions for 6 months. The algorithm analyzes voice patterns compared to a baseline recording at discharge. Treatment decisions are based on symptom data only; voice-based predictions are analyzed retrospectively after study completion.
The primary endpoint is sensitivity of the voice-based software in detecting heart failure deterioration, defined as heart failure hospitalization, or intensification of heart failure therapy. Secondary endpoints include app adherence, usability, and associations between voice data and blood biomarkers.
详细描述
Heart failure decompensation is often detected too late by conventional symptom and weight monitoring, leaving insufficient time to intervene. Invasive alternatives such as implantable pulmonary artery pressure monitors are effective but require surgical implantation. Voice-based digital biomarkers offer a promising non-invasive approach, as fluid overload may produce detectable changes in vocal features.
Patients begin voice recordings during hospitalization while still volume overloaded. At home, patients record daily using standardized and variable text content. The voice-based algorithm extracts biomechanical vocal features and calculates a risk score.
Healthcare providers access a dashboard showing symptom-based notifications and may adjust therapy at their discretion. Voice-derived risk scores are withheld during the study and analyzed retrospectively.
Study visits occur at months 3 and 6 (in-clinic) and month 1 (telephone). Blood samples are collected at baseline, month 3, and month 6 for analysis of traditional (NT-proBNP, creatinine) and novel biomarkers. Usability and quality of life are assessed via questionnaires distributed throughout the study period.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Informed consent provided
- •Currently hospitalized for acutely decompensated HF or de-novo HF
- •Age: 18 years and above
排除标准
- •Inability to provide consent
- •Pregnancy
- •Life-expectancy lower than 1 year due to a condition other than HF
- •Planned cardiac intervention within the next 6 months (e.g. valve replacement, bypass surgery)
- •Disabling mental diseases (e.g., Alzheimer's disease)
- •Symptoms mainly caused by chronic disease other than HF such as chronic obstructive pulmonary disease
- •Inability to use a smartphone or a tablet computer despite support by informal caregiver if required
- •Insufficient knowledge of the local language
- •Previous operations on organs involved in generation of voice (vocal tract, vocal folds, etc.)
- •Participation in another interventional study within 30 days of inclusion
研究组 & 干预措施
Voice-Based Monitoring
All participants receive standard heart failure care as per local standard of care plus daily voice monitoring via a mobile application. Patients record voice samples daily and answer symptom questions. Healthcare providers receive symptom-based notifications and may adjust therapy at their discretion. Voice-based risk scores are not used for clinical decisions during the study and are analyzed retrospectively.
干预措施: Daily Voice Recording and Symptom Monitoring (Other)
结局指标
主要结局
Sensitivity of Voice-Based Software in Detecting Heart Failure Deterioration
时间窗: 6 month
Sensitivity of the voice-based prediction in detecting heart failure deterioration, defined as heart failure-related hospitalization, or intensification of heart failure therapy due to worsening heart failure.
次要结局
- Alert Lead Time in Days(6 month)
- Unexplained Alert Rate per Patient-Year(6 month)
- Adherence to voice-based monitoring(6 month)
- App Usability via In-App Questionnaires(6 month)
- Quality of Life using the Kansas City Cardiomyopathy Questionnaire(6 months)