Browse by Medical Category
Dennis Ausiello, MD is the Director of the Center for Assessment Technology and Continuous Health (CATCH), a program he founded to discover earlier markers of disease, measured in daily life, for better patient control.
The Center for Assessment Technology and Continuous Health (CATCH) in the Department of Medicine is focusing on new approaches to medical research. CATCH is combining all sources of information on patient health – both traditional (e.g. medical records) and non-traditional (e.g. smartphone applications) sources to better define wellness and disease. Below are summaries of the various lines of research currently underway at CATCH.
Glucosuccess ResearchKit! This platform is allowing thousands of patients to enroll in a clinical trial via an app, and provide data for both individual and research use. Data collected on this scale is providing insights into diabetes progression and management never before achievable. We are currently obtaining new insights into the impact of exercise on patients, and also regarding regulation of glucose around meals. Data analysis is underway with unexpected findings of disease progression.
Predictive models for Patients at Risk for Medication Non-Adherence Combining large data set including the Partners electronic medical record (EMR) and publicly available data we are learning how patient behaviors, co-morbidities, and socio-geographic factors relate to the likelihood of medication adherence and build models predicting which patients are at risk.
Predictive models for Patients at Risk for Hypoglycemia We are combining our large EMR dataset with publicly available data to develop models predicting patient subsets at risk for hypoglycemia given behaviors, co-morbidities and environmental factors.
Olfactory (smell) Measurements of Disease: Individual differences in smell perception may associate with the presence of disease, such as with inflammatory and metabolic disease. One mechanism may be the function of microbes in the nose and/or gut, especially the microbial production of signaling molecules such as short chain fatty acids (SCFA). We will perform a first-in-man pilot using a new platform to analyze precise olfactory phenotypes in the context of the human microbiome and disease traits. If successful this platform will be a first in kind testing device.
Early detection of Congestive Heart Failure (CHF) In this pilot study we are determining if small changes in vocal patterns can provide early signals of fluid retention. The larynx is covered in a single layer of endothelial cells, and we hypothesize that small changes in fluid retention in these cells might cause detectable vocal changes, providing an early, simple and sensitive marker for CHF that can be measured with a home based device.
Hydration Sensor: No single measurement currently available determines hydration status. Currently an in-patient stay is required for evaluation. Our partners at MIT have developed a miniature NMR device and we are testing it to see if we can measure hydration status in minutes non-invasively. If successful this will lead to early, more accurate and less costly evaluation for patients with renal, pulmonary and cardiovascular diseases.
Gut Microbiome role in disease: Microbes found all over our bodies, especially in the gut (the microbiome), are recently recognized to modulate inflammation, immune function and metabolism in cancer, diabetes and auto-immune diseases. CATCH is characterizing the microbiome in the 3,800 participants of the Framingham Heart Study (Framingham, MA) along with data on diet, genetics, and medications
Behavior Assessment and Modification in T2 Diabetes CATCH is studying if a smartphone app can help patients improve symptoms and outcomes by reflecting accurate measures of tracked behavior. The app, designed at the MIT Media Lab, should be more effective than any other as it measures significant behavioral data points (i.e. sleep, exercise and social contact) through the phone sensors and not relying solely on biased patient reporting. These data, paired with patient reported metrics like diet and mood provide user friendly reports to support health behavior improvement.
Back to Top