Nocturnal Respiratory Rate Dynamics Enable Early Recognition of Impending Hospitalizations
Bibliographic record
Abstract
ABSTRACT 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’ 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 2 and 5.2 brpm 2 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 2 and 0.84 brpm 2 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".