Observational studyJournal of clinical monitoring and computing2020
Estimation of respiratory rate using infrared video in an inpatient population: an observational study.
Observational study in Journal of clinical monitoring and computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Uses of infrared thermography in acute illness: a systematic review.Frontiers in medicine · 2024Pooled it
- Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.Sensors (Basel, Switzerland) · 2021Trial
- Thermal Cameras for Continuous and Contactless Respiration Monitoring.Sensors (Basel, Switzerland) · 2024Review
- Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.Frontiers in bioengineering and biotechnology · 2024Review
- Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt.Scientific reports · 2022Article
- What is new in respiratory monitoring?Journal of clinical monitoring and computing · 2022Review
- Evaluation of respiratory rate monitoring using a microwave Doppler sensor mounted on the ceiling of an intensive care unit: a prospective observational study.Journal of clinical monitoring and computing · 2022Observational
- A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars.Frontiers in physiology · 2022Article
- Non-contact thermography-based respiratory rate monitoring in a post-anesthetic care unit.Journal of clinical monitoring and computing · 2021Article
- Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Respiratory rate (RR) is one of the most sensitive markers of a deteriorating patient. Despite this, there is significant inter-observer discrepancy when measured by clinical staff, and modalities used in clinical practice such as ECG bioimpedance are prone to error. This study utilized infrared thermography (IRT) to measure RR in a critically ill population in the Intensive Care Unit. This study was carried out in a Single Hospital Centre. Respiratory rate in 27 extubated ICU patients was counted by two observers and compared to ECG Bioimpedance and IRT-derived RR at distances of 0.4-0.6 m and > 1 m respectively. IRT-derived RR using two separate computer vision algorithms outperformed ECG derived RR at distances of 0.4-0.6 m. Using an Autocorrelation estimator, mean bias was - 0.667 breaths/min. Using a Fast Fourier Transform estimator, mean bias was - 1.000 breaths/min. At distances greater than 1 m no statistically significant signal could be obtained. Over all frequencies, there was a significant relationship between the RR estimated using IRT and via manual counting, with Pearson correlation coefficients between 0.796 and 0.943 (p < 0.001). Correlation between counting and ECG-derived RR demonstrated significance only at > 19 bpm (r = 0.562, p = 0.029). Overall agreement between IRT-derived RR at distances of 0.4-0.6 m and gold standard counting was satisfactory, and outperformed ECG derived bioimpedance. Contactless IRT derived RR may be feasible as a routine monitoring modality in wards and subacute inpatient settings.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.