Dataset Information

Device-based remote monitoring strategies for congestion-guided management of patients with heart failure: a systematic review and meta-analysis.

ABSTRACT:

Aims

Pre-clinical congestion markers of worsening heart failure (HF) can be monitored by devices and may support the management of patients with HF. We aimed to assess whether congestion-guided HF management according to device-based remote monitoring strategies is more effective than standard therapy.

Methods and results

A comprehensive literature research for randomized controlled trials (RCTs) comparing device-based remote monitoring strategies for congestion-guided HF management versus standard therapy was performed on PubMed, Embase, and CENTRAL databases. Incidence rate ratios (IRRs) and associated 95% confidence intervals (CIs) were calculated using the Poisson regression model with random study effects. The primary outcome was a composite of all-cause death and HF hospitalizations. Secondary endpoints included the individual components of the primary outcome. A total of 4347 patients from eight RCTs were included. Findings varied according to the type of parameters monitored. Compared with standard therapy, haemodynamic-guided strategy (4 trials, 2224 patients, 12-month follow-up) reduced the risk of the primary composite outcome (IRR 0.79, 95% CI 0.70-0.89) and HF hospitalizations (IRR 0.76, 95% CI 0.67-0.86), without a significant impact on all-cause death (IRR 0.93, 95% CI 0.72-1.21). In contrast, impedance-guided strategy (4 trials, 2123 patients, 19-month follow-up) did not provide significant benefits.

Conclusion

Haemodynamic-guided HF management is associated with better clinical outcomes as compared to standard clinical care.

SUBMITTER: Zito A

PROVIDER: S-EPMC10086988 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Device-based remote monitoring strategies for congestion-guided management of patients with heart failure: a systematic review and meta-analysis.

Zito Andrea A Princi Giuseppe G Romiti Giulio Francesco GF Galli Mattia M Basili Stefania S Liuzzo Giovanna G Sanna Tommaso T Restivo Attilio A Ciliberti Giuseppe G Trani Carlo C Burzotta Francesco F Cesario Alfredo A Savarese Gianluigi G Crea Filippo F D'Amario Domenico D

European journal of heart failure 20220911 12

<h4>Aims</h4>Pre-clinical congestion markers of worsening heart failure (HF) can be monitored by devices and may support the management of patients with HF. We aimed to assess whether congestion-guided HF management according to device-based remote monitoring strategies is more effective than standard therapy.<h4>Methods and results</h4>A comprehensive literature research for randomized controlled trials (RCTs) comparing device-based remote monitoring strategies for congestion-guided HF manageme ...[more]

PMID: 36054801

Similar Datasets

Project description:Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary Wedge Pressure (mPCWP), which is ideally measured invasively. Accurate non-invasive estimates of the mPCWP in patients with heart failure would help identify individuals at the greatest risk of a HF exacerbation. We developed a deep learning model, HFNet, that uses the 12-lead electrocardiogram (ECG) together with age and sex to identify when the mPCWP > 18 mmHg in patients who have a prior diagnosis of HF. The model was developed using retrospective data from the Massachusetts General Hospital and evaluated on both an internal test set and an independent external validation set, from another institution. We developed an uncertainty score that identifies when model performance is likely to be poor, thereby helping clinicians gauge when to trust a given model prediction. HFNet AUROC for the task of estimating mPCWP > 18 mmHg was 0.8 [Formula: see text] 0.01 and 0.[Formula: see text] 0.01 on the internal and external datasets, respectively. The AUROC on predictions with the highest uncertainty are 0.50 [Formula: see text] 0.02 (internal) and 0.[Formula: see text] 0.04 (external), while the AUROC on predictions with the lowest uncertainty were 0.86 ± 0.01 (internal) and 0.82 ± 0.01 (external). Using estimates of the prevalence of mPCWP > 18 mmHg in patients with reduced ventricular function, and a decision threshold corresponding to an 80% sensitivity, the calculated positive predictive value (PPV) is 0.[Formula: see text] 0.01when the corresponding chest x-ray (CXR) is consistent with interstitial edema HF. When the CXR is not consistent with interstitial edema, the estimated PPV is 0.[Formula: see text] 0.02, again at an 80% sensitivity threshold. HFNet can accurately predict elevated mPCWP in patients with HF using the 12-lead ECG and age/sex. The method also identifies cohorts in which the model is more/less likely to produce accurate outputs.

Dataset Information

Device-based remote monitoring strategies for congestion-guided management of patients with heart failure: a systematic review and meta-analysis.

Aims

Methods and results

Conclusion

Publications

Device-based remote monitoring strategies for congestion-guided management of patients with heart failure: a systematic review and meta-analysis.

Similar Datasets

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