Evidence map›Paper›PMID 35943063›Full record

Observational studyJournal of the American Heart Association2022

Remotely Monitored Cardiac Implantable Electronic Device Data Predict All-Cause and Cardiovascular Unplanned Hospitalization.

Camilla Sammut-Powell, Joanne K Taylor, Manish Motwani, Catherine M Leonard, Glen P Martin, Fozia Zahir Ahmed

Abstract readObservational Study
In one paragraph

Observational study in Journal of the American Heart Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Observational
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  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Camilla Sammut-PowellDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester United Kingdom.
Joanne K TaylorDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester United Kingdom.
Manish MotwaniDivision of Cardiovascular Sciences, Faculty of Biology, Medicine and Health University of Manchester United Kingdom.
Catherine M LeonardDivision of Health Economics Medtronic Ltd Watford United Kingdom.
Glen P MartinDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester United Kingdom.ORCID 0000-0002-3410-9472
Fozia Zahir AhmedDivision of Cardiovascular Sciences, Faculty of Biology, Medicine and Health University of Manchester United Kingdom.ORCID 0000-0001-9769-0180

Funding

British Heart Foundation FS/19/34163
6 · The paper itself

Abstract

Background Unplanned hospitalizations are common in patients with cardiovascular disease. The "Triage Heart Failure Risk Status" (Triage-HFRS) algorithm in patients with cardiac implantable electronic devices uses data from up to 9 device-derived physiological parameters to stratify patients as low/medium/high risk of 30-day heart failure (HF) hospitalization, but its use to predict all-cause hospitalization has not been explored. We examined the association between Triage-HFRS and risk of all-cause, cardiovascular, or HF hospitalization. Methods and Results A prospective observational study of 435 adults (including patients with and without HF) with a Medtronic Triage-HFRS-enabled cardiac implantable electronic device (cardiac resynchronization therapy device, implantable cardioverter-defibrillator, or pacemaker). Cox proportional hazards models explored association between Triage-HFRS and time to hospitalization; a frailty term at the patient level accounted for repeated measures. A total of 274 of 435 patients (63.0%) transmitted ≥1 high HFRS transmission before or during the study period. The remaining 161 patients never transmitted a high HFRS. A total of 153 (32.9%) patients had ≥1 unplanned hospitalization during the study period, totaling 356 nonelective hospitalizations. A high HFRS conferred a 37.3% sensitivity and an 86.2% specificity for 30-day all-cause hospitalization; and for HF hospitalizations, these numbers were 62.5% and 85.6%, respectively. Compared with a low Triage-HFRS, a high HFRS conferred a 4.2 relative risk of 30-day all-cause hospitalization (8.5% versus 2.0%), a 5.0 relative risk of 30-day cardiovascular hospitalization (3.6% versus 0.7%), and a 7.7 relative risk of 30-day HF hospitalization (2.0% versus 0.3%). Conclusions In patients with cardiac implantable electronic devices, remotely monitored Triage-HFRS data discriminated between patients at high and low risk of all-cause hospitalization (cardiovascular or noncardiovascular) in real time.

Indexed as

Cardiac Resynchronization TherapyDefibrillators, ImplantableHeart FailureElectronicsHospitalizationHumansall‐cause hospitalizationcardiac‐resynchronization therapycardiovascular hospitalizationheart failureimplantable cardioverter defibrillatorsremote monitoringrisk prediction

Identifiers

PMID35943063
PMCPMC9496305

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.