Remote Auscultation at Home After Asthma Exacerbation: A Case from Discharge to Readmission

Abstract

Patients with asthma are at high risk for relapse in the weeks following an exacerbation. Admission following a relapse is expensive and leads to poor quality of life. Automated respiratory sound monitoring offers an objective measure of a traditionally used marker of disease activity. Strados Remote Electronic Stethoscope Platform (RESP) was developed for remote automated auscultation using a wearable sensor. We present a case of hospital readmission following an asthma exacerbation, with daily respiratory sound changes captured from the day of discharge to readmission. The HIPAA rights of the patient is maintained in this report. Case: A man in his sixties with a long history of poorly controlled asthma was hospitalized for three weeks for an asthma exacerbation, during which he was intubated twice. On discharge, the patient was monitored daily by RESP. RESP recordings were obtained daily in the morning and at night in a sitting position. Each recording were protocolized to last for nine minutes and included two minutes of normal breathing, followed by three minutes of deep breathing with one deep breath every ten seconds, followed by two minutes of coughing with a cough every 10 seconds, and ended with two minutes of normal breathing. The recording was manually analyzed by two board-certified physicians to mark adventitious lung sounds which include wheezes and rhonchi. Concurrently, the patient was asked to grade his respiratory symptoms daily into one of the following: severe, moderate, mild, or no shortness of breath. Recordings were made from the day of discharge (day 1) through the day before admission (day 6). The patient was readmitted on day 7 for a two-day stay. The patient's rate of adventitious lung sounds decreased steadily until Day 4 post-discharge, and then increased steadily over three days until readmission on Day 7. Concurrently, the patient reported worsening symptoms in the two days leading up to readmission. Discussion: While prior studies have demonstrated the feasibility of using lung sounds to diagnose and monitor respiratory diseases, they were done in controlled environments as there was no integrated platform for daily monitoring of lung sounds at home. This case demonstrates the use of lung sound monitoring at home. Additionally, we detected presymptomatic changes in lung sounds that could have driven earlier interventions. With the advent of machine-learning technology, frequent lung sound monitoring at home is a practical and cost-effective tool in the management of chronic respiratory diseases.