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Use of special software program to analyze echocardiograms to help predict clinical complications.

Use of artificial intelligence in heart failure with preserved ejection fraction to predict outcomes. - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/05/087364
Enrollment
560
Registered
2025-05-22
Start date
Unknown
Completion date
Unknown
Last updated
2025-05-26

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: I429- Cardiomyopathy, unspecified

Interventions

Intervention1: Nil: Nil Intervention2: Nil: Nil Intervention3: Nil: Nil

Sponsors

NIL
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Confirmed HFpEF patients n is equal to 150 Diagnosed according to ESC guidelines with LVEF greaterthan or equal to 50 percent Asymptomatic individuals with risk factors n is equal to 500 Age and gender matched participants but no symptoms and normal NT proBNP n is equal to 125 Patients with established systemic auto-immune conditions and evidence of a pro-inflammatory state patients with rheumatoid arthritis systemic lupus erythematosus mixed connective tissue disorder scleroderma systemic sclerosis or systemic vasculitis with high-sensitivity C-reactive protein hs CRP greater than 3 mg or dL n is equal to 150 Healthy controls n is equal to 750 No cardiovascular risk factors or abnormalities

Exclusion criteria

Exclusion criteria:

Design outcomes

Primary

MeasureTime frame
To determine the outcomes of patients with HFpEFTimepoint: At 1 year 2 Years

Secondary

MeasureTime frame
Compare the patients echocardiograms with normal controls and analyse the echocardiograms using AI.Timepoint: Yearly upto 2 Years

Countries

India

Contacts

Public ContactDr Srilakshmi M A

St. Johns Medical college Hospital

srilakshmi.ma@stjohns.in9845953267

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026