ArticleIEEE transactions on bio-medical engineering2021
A Scalable Approach to Determine Intracardiac Pressure From Mechanical Circulatory Support Device Signals.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Dynamic load modulation predicts right heart tolerance of left ventricular cardiovascular assist in a porcine model of cardiogenic shock.Science translational medicine · 2024Article
- Hysteretic device characteristics indicate cardiac contractile state for guiding mechanical circulatory support device use.Intensive care medicine experimental · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveEffective mechanical circulatory support (MCS) relies on cardiac function measures to guide titration. Left ventricular end diastolic pressure (LVEDP) is a useful measure that is indirectly estimated using pulmonary artery catheters (PACs). PACs require additional intervention and provide intermittent and unreliable estimations. MCS device signals can estimate LVEDP but are prone to inter-device variability and require rigorous specialized characterization. We present a scalable and implementable approach to calculate LVEDP continuously using device signals.
methodsLVEDP was calculated from MCS device measured aortic pressure and motor current, which approximates the pressure head between the aorta and left ventricle. This motor current-pressure head relationship is device-specific but approximated using existing flow calibration and assumed physiologic relationships. Performance was evaluated with comparison from direct measurement of LVEDP in a series of acute animal models.
resultsLVEDP measures (n = 178,279) from 18 animals had good correlation (r = 0.84) and calibration (Bland-Altman limits of agreement -7.77 to 7.63 mmHg; mean bias -0.07 ± 0.02 mmHg). The total mean error prediction interval was -3.42 to 3.32 mmHg and RMS error was 3.85 mmHg.
conclusionLVEDP can be continuously calculated using device signals without specialized characterization. Calculated LVEDP values improved upon PAC estimations and were found using a scalable and manufacturer-accessible method. SIGNIFICANCE: This method improves upon existing LVEDP measures without the need for rigorous characterization, external calibration, or additional intervention; this allows widescale deployment of continuous LVEDP measurement for patients on MCS and demonstrates key considerations necessary to translate research-grade technologies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.