Remote Monitoring of Patients With Hematologic Malignancies at High Risk of Febrile Neutropenia: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in significant economic cost. OBJECTIVE: This exploratory study implemented a remote monitoring system comprising a digital infrared thermometer and a pulse oximeter with the capability to notify providers in real time of abnormalities in vital signs that could suggest early clinical deterioration and thereby improve clinical outcomes. METHODS: The remote monitoring system was implemented and compared to standard-of-care vital signs monitoring in hospitalized patients with underlying hematologic malignancies complicated by a febrile neutropenia episode in order to assess the feasibility and validity of the system. Statistical analysis was performed using the intraclass correlation coefficient (ICC) to assess the consistency between the measurements taken using traditional methods and those taken with the remote monitoring system for each of the vital sign parameters (temperature, heart rate, and oxygen saturation). A linear mixed-effects model with a random subject effect was used to estimate the variance components. Bland-Altman plots were created for the parameters to further delineate the direction of any occurring bias. RESULTS: A total of 23 patients were enrolled in the study (mean age 56, SD 23-75 years; male patients: n=11, 47.8%). ICC analysis confirmed the high repeatability and accuracy of the heart rate assessment (ICC=0.856), acting as a supplement to remote temperature assessment. While the sensitivity and specificity for capturing tachycardia above a rate of 100 bpm were excellent (88% and 97%, respectively), the sensitivity of the remote monitoring system in capturing temperatures >37.8 °C and oxygen saturation <92% was 45% and 50%, respectively. CONCLUSIONS: Overall, this novel approach using temperature, heart rate, and oxygen saturation assessments successfully provided real-time, clinically valuable feedback to providers. While temperature and oxygen saturation assessments lagged in terms of sensitivity compared to a standard in-hospital system, the heart rate assessment provided highly accurate complementary data. As a whole, the system provided additional information that can be applied to a clinically vulnerable population. By transitioning its application to high-risk patients in the outpatient setting, this system can help prevent additional use of health care services through early provider intervention and potentially improve outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".