Abstract
Background: Personalization of algorithms of alarms generated by telemonitoring systems may improve tailored management of patients with heart failure (HF). The purpose of our study is to test and develop clinical relevant alarm settings and responses based on trends of vital patient’s parameters.
Methods: We designed an observational study, to investigate the ability of a telemontoring system (iMediSense™) to detect clinical deterioration, in twenty HF patients admitted to the hospital. After informed consent, daily measurements of blood pressure (BP), body weight (BW) and heart rate (HR) will be performed. In addition, the patients are asked to fill out a questionnaire about their symptoms. Every morning after the first micturition but before breakfast, the measurements will be conducted. The daily measurements continue after hospital discharge till the first outpatient-clinic visit to the cardiologist. When measurements exceed predefined ranges, alarms will be generated. Parallel to patient’s measurement, the clinical team will assess parameters of vital status of patients during their in-hospital stay, as standard of care. These parallel data will be analyzed in a blinded fashion. Furthermore, we aim to study two algorithms for alarms, the HeartPhone algorithm, applying moving averages (MA) to daily weight data; and the signal filtering algorithm of blood pressure and heart rate. The clinical endpoints for a patient in the study are readmission to the hospital for HF or death. The endpoints of the study focus on the predictive value of the algorithms and clinical parameters.
Findings: We expect to provide tailored alarms of clinical significance based on trends of measured data, rather than having the caregivers preset uniform thresholds.
Discussion: Implementation of clinical relevant algorithms and professional responses accordingly, will improve the added value of telemonitoring in patients with heart failure and may improve quality of life and prevent adverse outcomes. Furthermore, self-management will be promoted.
Methods: We designed an observational study, to investigate the ability of a telemontoring system (iMediSense™) to detect clinical deterioration, in twenty HF patients admitted to the hospital. After informed consent, daily measurements of blood pressure (BP), body weight (BW) and heart rate (HR) will be performed. In addition, the patients are asked to fill out a questionnaire about their symptoms. Every morning after the first micturition but before breakfast, the measurements will be conducted. The daily measurements continue after hospital discharge till the first outpatient-clinic visit to the cardiologist. When measurements exceed predefined ranges, alarms will be generated. Parallel to patient’s measurement, the clinical team will assess parameters of vital status of patients during their in-hospital stay, as standard of care. These parallel data will be analyzed in a blinded fashion. Furthermore, we aim to study two algorithms for alarms, the HeartPhone algorithm, applying moving averages (MA) to daily weight data; and the signal filtering algorithm of blood pressure and heart rate. The clinical endpoints for a patient in the study are readmission to the hospital for HF or death. The endpoints of the study focus on the predictive value of the algorithms and clinical parameters.
Findings: We expect to provide tailored alarms of clinical significance based on trends of measured data, rather than having the caregivers preset uniform thresholds.
Discussion: Implementation of clinical relevant algorithms and professional responses accordingly, will improve the added value of telemonitoring in patients with heart failure and may improve quality of life and prevent adverse outcomes. Furthermore, self-management will be promoted.
| Original language | English |
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| Publication status | Published - 1 Jun 2018 |
| Event | 8th Conference Supporting Health By Technology 2018 - University of Twente, Enschede, Netherlands Duration: 1 Jun 2018 → 1 Jun 2018 Conference number: 8 http://healthbytech.com/ |
Conference
| Conference | 8th Conference Supporting Health By Technology 2018 |
|---|---|
| Abbreviated title | HealthbyTech 2018 |
| Country/Territory | Netherlands |
| City | Enschede |
| Period | 1/06/18 → 1/06/18 |
| Internet address |