SBIR-STTR Award

Heart failure (HF) is the most common cause for both hospitalizations and readmissions in the Medicare program. HF's high mortality rate and high hospitalization utilization rate via readmissions results in a large economic burden currently estimated at over $30B, and prevalence of HF is expected to continue rising by 46% by 2030. There is an opportunity to deploy remote patient monitoring (RPM) tools for measuring prognostic biomarkers of worsening HF and acute decompensation and hospital readmission. Bender Tech (BT) has developed a urine testing platform capable of easily attaching to a home-toilet for accurate and easy collection of longitudinal health and behavior data without the requirement of manual sample collection and/or testing. We propose to adapt our system for use in elderly HF patient populations by rendering data collection and transmission completely passive. We propose to integrate sensors and firmware capable of identifying when a user has used their home toilet(Specific Aim 1), develop the machine learning (ML) classification algorithms necessary for determining when to perform a testing sequence (Specific Aim 2), and use ML methods to demonstrate feasibility of user biometric identification via urinary testing data (Specific Aim 3). A successful outcome of this proposal will be a set of classification models built using ML techniques designed to enable passive collection of longitudinal urine profile data from a home-toilet for use in remotely managing elderly HF patients. This will ready the product for prospective clinical trials that would be the subject of a future phase II submission for use in remotely monitoring diuretic effectiveness and preventing hospital readmissions.

Public Health Relevance Statement:
Project Narrative Remotely stratifying risk of worsening heart failure and predicting imminent decompensation and hospitalizations using remote patient monitoring devices can help lower healthcare costs and improve lives for the elderly living with heart failure at home. Longitudinal urinary trend data of physiologically important markers, such as spot sodium tests, have been shown to help classify potentially stable heart failure patients, predict acute decompensation, and measure diuretic effectiveness via relative changes from baseline measurements. Bender Tech proposes to integrate the hardware and machine learning methods necessary to render its internet-connected home urine testing platform completely passive to enable use by the elderly HF population and obviate any need for device interaction for data collection and transmission.

Project Terms:
Elderly ; advanced age ; elders ; geriatric ; late life ; later life ; older adult ; older person ; senior citizen ; Biometry ; Biometrics ; Biostatistics ; Cardiology ; Classification ; Systematics ; Clinical Trials ; Data Collection ; Defecation ; bowel movement ; Diuretics ; Engineering ; Fingerprint ; Future ; Goals ; Health behavior ; health related behavior ; Medicare ; Health Insurance for Aged and Disabled, Title 18 ; Health Insurance for Disabled Title 18 ; Title 18 ; health insurance for disabled ; Heart failure ; cardiac failure ; Hospitalization ; Hospital Admission ; Manuals ; Maps ; mortality ; Patients ; Potassium ; K element ; Publishing ; Research ; Risk ; Running ; Sleep ; Societies ; Sodium ; Na element ; Specificity ; Technology ; Testing ; Urination ; micturition ; Urine ; Urine Urinary System ; Use Effectiveness ; Measures ; Health Care Costs ; Health Costs ; Healthcare Costs ; Treatment Failure ; therapy failure ; sensor ; improved ; sample collection ; specimen collection ; Acute ; Phase ; Physiological ; Physiologic ; Individual ; European ; Measurement ; Internet ; WWW ; web ; world wide web ; Spottings ; tool ; machine learned ; Machine Learning ; Consensus ; programs ; Frequencies ; Techniques ; System ; Test Result ; interest ; meetings ; experience ; patient monitoring device ; Performance ; hospital re-admission ; re-admission ; re-hospitalization ; readmission ; rehospitalization ; hospital readmission ; Devices ; Modeling ; Effectiveness ; preventing ; prevent ; Dose ; Data ; Economic Burden ; Molecular Marker of Prognosis ; Prognosis Marker ; prognostic biomarker ; prognostic indicator ; Prognostic Marker ; Collection ; trend ; Characteristics ; urinary ; Output ; cost ; design ; designing ; Outcome ; Population ; Prevalence ; prospective ; patient population ; data exchange ; data transfer ; data transmission ; health data ; risk stratification ; stratify risk ; clinical decision support ; unsupervised learning ; unsupervised machine learning ; classification algorithm ; machine learning method ; machine learning methodologies ; remote monitoring ; remote patient monitoring ; Home ;