ArticleJMIR medical informatics2016
Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Wearable Devices Based on Bioimpedance Test in Heart Failure: Clinical Relevance: Systematic Review.Reviews in cardiovascular medicine · 2024Pooled it
- Mouse model for sensitivity of fluid measurement with textile electrodes.Biomedical engineering online · 2026Article
- Non-Invasive Wearable Technology to Predict Heart Failure Decompensation.Journal of clinical medicine · 2025Review
- In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients.Clinical research in cardiology : official journal of the German Cardiac Society · 2025Observational
- Advances in Machine Learning Models for Healthcare Applications: A Precise and Patient-Centric Approach.Current pharmaceutical design · 2025Review
- Bioimpedance Analysis in CKD and HF Patients: A Critical Review of Benefits, Limitations, and Future Directions.Journal of clinical medicine · 2024Review
- Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure.Reviews in cardiovascular medicine · 2024Review
- Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure.European heart journal. Digital health · 2023Article
- Wearables in Cardiovascular Disease.Journal of cardiovascular translational research · 2023Review
- Development of a wearable belt with integrated sensors for measuring multiple physiological parameters related to heart failure.Scientific reports · 2022Article
- Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022Review
- Home Telemonitoring and a Diagnostic Algorithm in the Management of Heart Failure in the Netherlands: Cost-effectiveness Analysis.JMIR cardio · 2022Article
- The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Protocol for a Single-Center Prospective Trial.JMIR research protocols · 2022Article
- Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research.Sensors (Basel, Switzerland) · 2021Review
- Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions.JMIR biomedical engineering · 2021Article
- Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy.International journal of heart failure · 2021Review
- The Role of Wearables in Heart Failure.Current heart failure reports · 2020Review
- A framework to characterize the performance of early warning index alarm systems for patient monitoring.MethodsX · 2019Article
- Wearable technology for cardiology: An update and framework for the future.Trends in cardiovascular medicine · 2018Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHeart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation.
objectiveIn this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations.
methodsWe monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM).
resultsAlgorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines.
conclusionsNITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation.
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