LSHF Index™ – Low-output Subclinical Heart Failure Index

Project LSHF Index™Low-output Subclinical Heart Failure IndexNon-invasive diagnosis of latent subclinical heart failure using bioimpedance analysis 1. Project TitleLSHF Index™ — Low-output Subclinical Heart Failure IndexA non-invasive method for early-stage detection of subclinical heart failure (HF) based on combined bioimpedance measurement (BIM) and echocardiography (EchoCG) analysis. 2. Project Mission"Early detection of latent heart failure before the onset of manifest symptoms to preserve quality and length of life."Up to 40% of HF cases remain undiagnosed during the latent stage. Patients often seek medical help too late — after developing severe congestive complications.LSHF Index™ offers a solution based on BIM and EchoCG data analysis for timely identification of latent HF. 3. Medical ProblemCurrent HF diagnostic methods — NT-proBNP and EchoCG — often fail to detect disease early due to: High costs Limited sensitivity Dependence on patient’s workload and body massBioimpedance measurement can detect early signs of disease: Extracellular fluid overload Decreased cellular tone Latent reduction in cardiac index 4. Solution: LSHF Index™A mathematical model combining key parameters: Cardiac Index (CI) Total Peripheral Resistance Index (TPRI) ECW/TBW ratio — extracellular water to total body water Stroke Index (STR) EchoCG parameters: ejection fraction (EF), end-diastolic volume (EDV), left ventricle (LV) dimensions The algorithm calculates: Risk of latent heart failure Probability of progression to manifest HF Therapeutic response 5. Scientific BasisStudy conducted on a sample of 90 patients with pre- and post-treatment data: ECW/TBW >48% before treatment is a reliable predictor of latent fluid retention Improvement in CI and STR reflects early positive therapy effect EchoCG parameters may remain within normal range while BIM reveals hidden abnormalities 6. Advantages of the Approach Non-invasive and safe Rapid diagnosis — 5–10 minutes High reproducibility and objective results Scalable for use in clinics and telemedicine Ability to predict clinical outcomes before symptom onset 7. Prototype and Technology Use of data from bioimpedance measurement device Algorithm implemented in Python Calculation of LSHF Index™ — a risk coefficient for latent HF Integration with electronic medical records (EMR) and mobile apps API availability and IoT device integration 8. Clinical and Market Potential Screening in high-risk groups: diabetes, obesity, elderly, hypertension Patient monitoring after COVID-19, surgeries, chemotherapy Potential HF diagnostic and monitoring market — $5–8 billion per year Possibility of mobile screening in shortage-of-specialists settings 9. Achievements and Plans Ethical approval obtained Over 90 clinical cases collected Preliminary machine learning model developed Planned cohort study with model training on 1000+ patients over 12 months Creation of LSHF Index™ clinical scale Publication of scientific results and intellectual property registration 10. Funding Request and OpportunitiesSeeking partnership and funding of $150,000–300,000 divided into stages: Stage 1 ($50K): MVP development, algorithm refinement, open-access publication Stage 2 ($100–250K): Large-scale cohort study, patent registration, multicenter launch