Life sciences · Observational Study
ClinicalTrials.gov · October 13, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Observational Study.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Registry record from ClinicalTrials.gov (NCT07010562). This is a study registration, not published results. Lead sponsor: Acorai AB. Recruitment status: RECRUITING. Study type: OBSERVATIONAL. Enrollment: 392 participants (ESTIMATED). Conditions: Heart Failure. Interventions: DEVICE: Data collection. Primary outcome measures: Evaluation of the ML's performance to estimate pressure , Day 0 to Day 90. Brief summary: Acorai is developing a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters in patients with suspected or confirmed heart failure, and/or pulmonary hypertension, who require hemodynamic assessment. The device will be intended as a companion test or clinical decision support tool to be used and interpreted by qualified healthcare professionals to aid standard-of-care clinical assessment in identifying hemodynamic congestion and supporting personalized treatment of heart failure and pulmonary congestion. This study is part of the development of a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters. It will be conducted to collect the data needed to train the machine learning models retrospectively.