Detecting respiratory distress in nursing home patients with advanced ADRD using radio sensors
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PROJECT SUMMARY Overview: The proposal aims to advance the science of geriatric palliative care by assessing the feasibility and acceptability of implementing radio frequency (RF) sensors to capture cardiopulmonary measures in nursing home (NH) patients with Alzheimer's Disease and related dementias (ADRD) and using machine learning to help predict when a patient may be experiencing an episode of respiratory distress. Background: In NH patients with advanced ADRD, respiratory distress is common and can occur in up to 80% of patients. Respiratory distress can lead to negative patient outcomes such as increased suffering, poor quality of life, and unwanted care transitions. Given that ADRD approximately patients make up 48% of all NH residents, with 60% of those having moderate to severe cognitive impairment,it is critical that we find ways to better identify and detect respiratory distress in this vulnerable patient population. Research plan: The proposal aims to (1) assess the feasibility and acceptability of implementing RF sensors to capture cardiopulmonary waveforms in NH patients with advanced ADRD and (2) develop a machine learning algorithm that will autonomously detect and predict respiratory distress using collected cardiopulmonary recordings. Furthermore, interviews will be conducted with NH staff and legal authorized representatives of patients with dementia to gain insight into the potential use and challenges of implementing RF sensors to help in the detection and prediction of respiratory distress in patients with advanced ADRD. Environment: The study team is a distinguished group with expertise in geriatrics, palliative medicine, pulmonary medicine, and biosensor technology/artificial intelligence. By combining the resources at Weill Cornell Medicine, Cornell University, Archcare, the proposal has the potential to lead to advances in how respiratory distress is monitored and detected in NH patients with ADRD, with the goal of reducing patient suffering and burdensome care transitions.
