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

Gyllensten, Illapha Cuba; Bonomi, A.; Goode, Kevin; Reiter, Harald; Habetha, Jörg; Amft, Oliver; Cleland, John G.F.

doi:10.2196/medinform.4842

Cited by 41 publications

(47 citation statements)

References 41 publications

(63 reference statements)

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“…Warning indices and notification systems that use physiological measurements to produce comprehensive metrics of patient health [3,4] and identify patterns predictive of patient instabilities [5,6] are a promising tool to provide lead time prior to a critical event when effective mitigations can be taken [7]. A sampling of such methods include approaches for identifying need for life-saving interventions in trauma patients [8,9], patients at risk for septic shock in the intensive care unit [10], need for care escalation from step-down units [11–13], and potential heart failure in at-home monitoring [14,15] environments. Patient monitoring environments are known for the number of false alarms [16,17], presenting a need to balance the performance of any new monitoring index and warning system to provide timely warnings prior to an event and low false-alarm rates.…”

Section: Introductionmentioning

confidence: 99%

Evaluating performance of early warning indices to predict physiological instabilities

Scully

Daluwatte

2017

Journal of Biomedical Informatics

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Patient monitoring algorithms that analyze multiple features from physiological signals can produce an index that serves as a predictive or prognostic measure for a specific critical health event or physiological instability. Classical detection metrics such as sensitivity and positive predictive value are often used to evaluate new patient monitoring indices for such purposes, but since these metrics do not take into account the continuous nature of monitoring, the assessment of a warning system to notify a user of a critical health event remains incomplete. In this article, we present challenges of assessing the performance of new warning indices and propose a framework that provides a more complete characterization of warning index performance predicting a critical event that includes the timeliness of the warning. The framework considers 1) an assessment of the sensitivity to provide a notification within a meaningful time window, 2) the cumulative sensitivity leading up to an event, 3) characteristics on if the warning stays on until the event occurs once a warning has been activated, and 4) the distribution of warning times and the burden of additional warnings (e.g., false-alarm rate) throughout monitoring that may or may not be associated with the event of interest. Using an example from an experimental study of hemorrhage, we examine how this characterization can differentiate two warning systems in terms of timeliness of warnings and warning burden.

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Section: Introductionmentioning

confidence: 99%

Evaluating performance of early warning indices to predict physiological instabilities

Scully

Daluwatte

2017

Journal of Biomedical Informatics

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“…However, uptake of this technology has been slow due to cumbersome prototype technologies and difficulties in interpreting the results since electrical resistance does not directly translate into a lung water assessment [25]. Recent analysis of the MyHeart trial data suggests that non-invasive TTI is more sensitive to impending deterioration than standard measures of fluid accumulation such as weight [16]. …”

Section: Methodsmentioning

confidence: 99%

“…These are described in more detail below. Whilst the weight-MACD and impedance-CUSUM have been shown to be more effective in the detection of impending deterioration [13, 16], they may not be as easy to interpret as simple differences, which could paradoxically result in longer review times and worse decisions.…”

Section: Methodsmentioning

confidence: 99%

“…However, recent evidence demonstrates that data-driven approaches looking at trends and patterns of weight change, improve the accuracy of detecting patient deterioration when compared to these simple expert rules [13–16]. …”

Section: Introductionmentioning

confidence: 99%

“…Weight itself may not be the best measure of fluid congestion and the detection of decompensated HF can be improved by using non-invasive trans-thoracic impedance [16] implanted intra-thoracic impedance [17] and haemodynamic pressures [18, 19]. However, whatever system is used to monitor the patients, ultimately it is the care decisions made by the patients and the healthcare professionals which improve clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions

Gyllensten

Crundall-Goode

Aarts

et al. 2017

BMC Med Inform Decis Mak

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BackgroundHome telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient’s condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied.MethodsUsing HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm).ResultsIn total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action.ConclusionAdvanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0398-9) contains supplementary material, which is available to authorized users.

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Measuring congestion with a non‐invasive monitoring device in heart failure and haemodialysis: CONGEST‐HF

Curtain,

Talebi,

McIntosh

et al. 2024

European J of Heart Fail

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AimsWe examined the effectiveness of a novel cardiopulmonary management wearable sensor (worn for less than 5 mins) at measuring congestion and correlated the device findings with established clinical measures of congestion.Methods and resultsWe enrolled three cohorts of patients: (1) patients with heart failure (HF) receiving intravenous diuretics in hospital; (2) patients established on haemodialysis, and (3) HF patients undergoing right heart catheterization (RHC). The primary outcomes in the respective cohorts were a Spearman correlation between (1) change in weight and change in thoracic impedance (TI) (from enrolment, 24 h after admission to discharge) in patients hospitalized for HF; (2) lung ultrasound B‐lines and volume removed during dialysis with device measured TI, and (3) pulmonary capillary wedge pressure (PCWP) and sub‐acoustic diastolic, third heart sound (S3) in the patients undergoing RHC. A total of 66 patients were enrolled. In HF patients (n = 25), change in weight was correlated with both change in device TI (Spearman correlation [rsp] = −0.64, p = 0.002) and change in device S3 (rsp = −0.53, p = 0.014). In the haemodialysis cohort (n = 21), B‐lines and TI were strongly correlated before (rsp = −0.71, p < 0.001) and after (rsp = −0.77, p < 0.001) dialysis. Volume of fluid removed by dialysis was correlated with change in device TI (rsp = 0.49, p = 0.024). In the RHC cohort (n = 20), PCWP measured at one time point and device S3 were not significantly correlated (rsp = 0.230, p = 0.204). There were no device‐related adverse events.ConclusionsA non‐invasive device was able to detect changes in congestion in patients with HF receiving decongestion therapy and patients having fluid removed at haemodialysis. The cardiopulmonary management device, which measures multiple parameters, is a potentially useful tool to monitor patients with HF to prevent hospitalizations.

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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

Cited by 41 publications

References 41 publications

Evaluating performance of early warning indices to predict physiological instabilities

Evaluating performance of early warning indices to predict physiological instabilities

Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions

Measuring congestion with a non‐invasive monitoring device in heart failure and haemodialysis: CONGEST‐HF

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