Remote Monitoring of High-Risk Patients With Chronic Cardiopulmonary Diseases
Remote Monitoring of High-Risk Patients With Chronic Cardiopulmonary Diseases
In this project, Singapore Bioimaging Consortium would like to collaborate with Massachusetts
 General Hospital to aggregate patient data and to further develop its software algorithm
 using machine learning and statistical models for predicting exacerbations and deterioration
 on 60 patients with cardiopulmonary diseases.
Exacerbations of chronic cardiopulmonary diseases are a major cause of morbidity and
 mortality worldwide. There are an estimated 23 million patients with heart failure worldwide,
 and the prevalence of heart failure in the United Sates is projected to rise over the next
 four decades with an estimated 772,000 new heart failure cases projected in the year 2040.
 Exacerbations of chronic respiratory disease can accelerate lung function decline and reduce
 survival. They may also lead to significant rise to the cost of healthcare. Chronic
 Obstructive Pulmonary Disease (COPD) exacerbations are an important cause of readmissions
 with a 30-day readmission rate of approximately 20% and subsequent expenditure of US $15
 Billion in annual health care spending. Cystic fibrosis (CF), a genetic disorder that affects
 airways clearance and secretions, has a 30-day readmission rate of approximately 11%.

 Due to the high cost of hospital stays and emergency department visits, and especially in the
 setting of the COVID-19 pandemic, more cost-effective "out-of-hospital" management models
 have become increasingly appealing. Such models not only provide cost benefits to patients,
 payers, and hospitals, but also increase the ability to provide care to people at home.

 Respiratory variables have shown to be one of the most sensitive indicators for COPD
 exacerbation, and a significant correlation between respiratory rate and COPD symptoms has
 been observed. When combined with pulse rate and oxygen saturation, these variables provide a
 useful method of identifying exacerbations. Current analytical models are designed to trigger
 alarms, which are generally based on traditional threshold-type driven analytics. Such
 methods are not able to identify and recognize trends due to limited access to advanced
 analytics (e.g., machine learning methods). The device proposed for use in this study will
 measure respiratory rate, Inspiratory: Expiratory (I:E) ratio, respiratory depth, heart rate,
 SpO2, SpO2 variability, patient movement, and the investigators will use machine learning and
 data modeling to analyze their trends over time.

 The wearable biometric platform (termed 'Respiratory Sensor') developed by Singapore
 Bioimaging Consortium is to be worn on the chest area via a medical grade adhesive patch. The
 Respiratory Sensor includes a non-invasive sensor combining an accelerometer and light-based
 methods to sense chest wall expansion or breathing.

 The Respiratory Sensor allows the possibility of collecting additional respiratory
 information (respiratory rate, relative tidal depth and duty cycle) as predictors for
 exacerbation of chronic cardiopulmonary diseases and perhaps improving advanced analytical
 models that can provide better sensitivity and specificity compared to traditional models
 (e.g. clinical diaries). Ultimately, this may allow early prediction of outpatient
 exacerbations to allow early intervention and reduced re-admissions (via remote
 interventions).

 In this project, Singapore Bioimaging Consortium would like to collaborate with Massachusetts
 General Hospital to aggregate patient data and to further develop its software algorithm
 using machine learning (e.g. random forest models and long-short term memory models etc.) and
 statistical models (e.g. regression models and survival analysis with univariate and
 multivariate analysis) based on respiratory features and hemodynamics for predicting
 exacerbations and deterioration on 60 patients with cardiopulmonary diseases. The end-points
 of this collaboration includes the following:

 1. To validate hypothesis of using respiratory-based biomarkers in models (disease agnostic
 and disease specific) to predict exacerbations - benchmarking to be done versus
 follow-up questionnaires and phone calls

 2. To validate level of compliance, drop-out rate and if additional measures are required
 to get patients to follow-on
Inclusion Criteria:

 1. Subject age 18 or older

 2. Receives all primary and specialty care within the MassGeneral Brigham system

 3. A history of one of the following diagnoses:

 1. Asthma

 2. Cystic Fibrosis

 3. Chronic obstructive pulmonary disease

 4. Congestive heart failure

 4. At least four documented exacerbations of the above disease in the past 12 months as
 defined by the following corresponding criteria:

 a. Asthma exacerbation: i. a minimum 3-day course of oral steroids ii. for patients on
 chronic steroids, an increased dose of steroids.

 b. Cystic fibrosis exacerbation: a minimum 7-day course of systemic antibiotics (not
 including any chronic suppressive antibiotics).

 c. Chronic obstructive pulmonary disease exacerbation: all three of (1) increase in
 frequency and severity or severity of cough, (2) increase in volume and/or change of
 character of sputum production, and (3) increase in dyspnea, and requiring treatment
 with short-acting bronchodilators, antibiotics, and oral or intravenous
 glucocorticoids.

 d. Congestive heart failure exacerbation: volume overload (as evidenced by weight gain
 or elevated BNP [>100 pg/mL]/NT-proBNP [>300 pg/mL)) plus dyspnea plus diuretic
 treatment (new or increase from baseline).

 5. Subject able to provide informed consent.

 Exclusion Criteria:

 1. Subjects with a history of adhesive or tape allergy or skin reaction.

 2. Subjects with pacemaker, Automatic Implantable Cardioverter Defibrillator (AICD) and
 other implantable electronic devices.

 3. Subjects with neuromuscular disease, seizures and/or Parkinson's disease.

 4. Subjects with expected out of state travel within a 90-day period or travel to a
 location with no internet access.

 5. Subjects enrolled in hospice care or life expectancy less than three months.

 6. Subjects living more than 60 miles away from Massachusetts General Hospital. -