{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:42:34Z","timestamp":1781739754101,"version":"3.54.5"},"posted":{"date-parts":[[2022,3,14]]},"group-title":"Respiratory Medicine","reference-count":35,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2022,3,14]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  The days and weeks preceding hospitalization are poorly understood because they transpire before patients are seen in conventional clinical care settings. Home health sensors offer opportunities to learn signatures of impending hospitalizations and facilitate early interventions, however the relevant biomarkers are unknown. Nocturnal respiratory rate (NRR) is an activity-independent biomarker that can be measured by adherence-independent sensors in the home bed. Here, we report automated longitudinal monitoring of NRR dynamics in a cohort of high-risk recently hospitalized patients using non-contact mechanical sensors under patients\u2019 home beds. Since the distribution of nocturnal respiratory rates in populations is not well defined, we first quantified it in 2,000 overnight sleep studies from the NHLBI Sleep Heart Health Study. This revealed that interpatient variability was significantly greater than intrapatient variability (NRR variances of 11.7 brpm\n                  <jats:sup>2<\/jats:sup>\n                  and 5.2 brpm\n                  <jats:sup>2<\/jats:sup>\n                  respectively, n=1,844,110 epochs), which motivated the use of patient-specific references when monitoring longitudinally. We then performed adherence-independent longitudinal monitoring in the home beds of 34 high-risk patients and collected raw waveforms (sampled at 80 Hz) and derived quantitative NRR statistics and dynamics across 3,403 patient-nights (n= 4,326,167 epochs). We observed 23 hospitalizations for diverse causes (a 30-day hospitalization rate of 20%). Hospitalized patients had significantly greater NRR deviations from baseline compared to those who were not hospitalized (NRR variances of 3.78 brpm\n                  <jats:sup>2<\/jats:sup>\n                  and 0.84 brpm\n                  <jats:sup>2<\/jats:sup>\n                  respectively, n= 2,920 nights). These deviations were concentrated prior to the clinical event, suggesting that NRR can identify impending hospitalizations. We analyzed alarm threshold tradeoffs and demonstrated that nominal values would detect 11 of the 23 clinical events while only alarming 2 times in non-hospitalized patients. Taken together, our data demonstrate that NRR dynamics change days to weeks in advance of hospitalizations, with longer prodromes associating with volume overload and heart failure, and shorter prodromes associating with acute infections (pneumonia, septic shock, and covid-19), inflammation (diverticulitis), and GI bleeding. In summary, adherence-independent longitudinal NRR monitoring has potential to facilitate early recognition and management of pre-symptomatic disease.\n                <\/jats:p>","DOI":"10.1101\/2022.03.10.22272238","type":"posted-content","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T20:01:04Z","timestamp":1647288064000},"source":"Crossref","is-referenced-by-count":3,"title":["Nocturnal Respiratory Rate Dynamics Enable Early Recognition of Impending Hospitalizations"],"prefix":"10.64898","author":[{"given":"Nicholas","family":"Harrington","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David Torres","family":"Barba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan M.","family":"Bui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Wassell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sukhdeep","family":"Khurana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo B.","family":"Rubarth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert L.","family":"Owens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parag","family":"Agnihotri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1472-2189","authenticated-orcid":false,"given":"Kevin R.","family":"King","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2022031607450506000_2022.03.10.22272238v1.1","doi-asserted-by":"crossref","first-page":"e06","DOI":"10.15420\/cfr.2019.12","article-title":"Telemonitoring in Heart Failure Management","volume":"6","year":"2020","journal-title":"Card Fail Rev"},{"key":"2022031607450506000_2022.03.10.22272238v1.2","doi-asserted-by":"crossref","first-page":"e007219","DOI":"10.1161\/CIRCHEARTFAILURE.120.007219","article-title":"Secondary Impact of the COVID-19 Pandemic on Patients With Heart Failure","volume":"13","year":"2020","journal-title":"Circ Heart Fail"},{"key":"2022031607450506000_2022.03.10.22272238v1.3","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000004224"},{"key":"2022031607450506000_2022.03.10.22272238v1.4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.18553\/jmcp.2019.25.2.205","article-title":"Health Care Resource Utilization and Exacerbation Rates in Patients with COPD Stratified by Disease Severity in a Commercially Insured Population","volume":"25","year":"2019","journal-title":"Journal of managed care & specialty pharmacy"},{"key":"2022031607450506000_2022.03.10.22272238v1.5","doi-asserted-by":"crossref","unstructured":"P. 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