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Journal of Mathematical Biology Oct 2022All schoolchildren know how often they breathe, but even experts don't know exactly why. The aim of this publication is to develop a model of the resting spontaneous...
All schoolchildren know how often they breathe, but even experts don't know exactly why. The aim of this publication is to develop a model of the resting spontaneous breathing rate using physiological, physical and mathematical methods with the aid of the principle that evolution pushes physiology in a direction that is as economical as possible. The respiratory rate then follows from an equation with the parameters [Formula: see text]-production rate of the organism, resistance, static compliance and dead space of the lungs, the inspiration duration: expiration duration - ratio and the end-expiratory [Formula: see text] fraction. The derivation requires exclusively secondary school mathematics. Using the example of an adult human or a newborn child, data from the literature then result in normal values for their breathing rate at rest. The reason for the higher respiratory rate of a newborn human compared to an adult is the relatively high [Formula: see text]-production rate together with the comparatively low compliance of the lungs. A side result is the fact that the common alveolar pressure throughout the lungs and the common time constant is a consequence of the economical principle as well. Since the above parameters are not human-specific, there is no reason to assume that the above equation could not also be applicable to many animals breathing through lungs within a thorax, especially mammals. Not only physiology and biology, but also medicine, could benefit: Applicability is being discussed in pulmonary function diagnostics, including pathophysiology. However, the present publication only claims to be a theoretical concept of the spontaneous quiet breathing rate. In the absence of comparable animal data, this publication is intended to encourage further scientific tests.
Topics: Humans; Adult; Animals; Infant, Newborn; Child; Respiratory Rate; Lung; Mammals
PubMed: 36282355
DOI: 10.1007/s00285-022-01790-8 -
Respiratory Medicine Dec 2023Respiratory rate is a commonly used vital sign with various clinical applications. It serves as a crucial marker of acute health issues and any significant alteration in...
PURPOSE
Respiratory rate is a commonly used vital sign with various clinical applications. It serves as a crucial marker of acute health issues and any significant alteration in respiratory rate may be an early warning sign of major issues such as infections in the respiratory tract, respiratory failure, or cardiac arrest. Timely recognition of changes in respiratory rate enables prompt medical action, while neglecting to detect a change may lead to adverse patient outcomes. Here, we report on the performance of respiratory rate determined using a depth sensing camera system (RR) which allows for continuous, non-contact 'touchless' monitoring of this important vital sign.
METHODS
Thirty adult volunteers undertook a range of set breathing rates to cover a target breathing range of 4-40 breaths/min. Depth information was acquired from the torso region of the subjects using an Intel D415 RealSense camera positioned above the bed. The depth information was processed to generate a respiratory signal from which RR was calculated. This was compared to a manually scored capnograph reference (RR).
RESULTS
An overall RMSD accuracy of 0.77 breaths/min was achieved across the target respiratory rate range with a corresponding bias of 0.05 breaths/min. This corresponded to a line of best fit given by RR = 1.01 x RR - 0.22 breaths/min with an associated high degree of correlation (R = 0.997). A breakdown of the performance with respect to sub-ranges corresponding to respiratory rates or ≤7, >7-10, >10-20, >20-30, >30 breaths/min all exhibited RMSD accuracies of less than 1.00 breaths/min. We also had the opportunity to test the performance of spontaneous breathing of the subjects which occurred during the study and found an overall RMSD accuracy of 1.20 breaths/min with corresponding accuracies ≤1.30 breaths/min across each of the individual sub-ranges.
CONCLUSIONS
We have conducted an investigative study of a prototype depth sensing camera system for the non-contact monitoring of respiratory rate. The system achieved good performance with high accuracy across a wide range of rates including both clinically important high and low rates.
Topics: Adult; Humans; Respiratory Rate; Respiration; Respiratory System; Technology; Monitoring, Physiologic
PubMed: 37993024
DOI: 10.1016/j.rmed.2023.107463 -
Best Practice & Research. Clinical... May 2021Early warning scores (EWS) have the objective to provide a preventive approach for detecting those patients in general wards at risk of deterioration before it begins.... (Review)
Review
Early warning scores (EWS) have the objective to provide a preventive approach for detecting those patients in general wards at risk of deterioration before it begins. Well implemented and combined with a tiered response, the EWS expect to be a relevant tool for patient safety. Most of the evidence for their use has been published for the general EWS. Their strengths, such as objectivity and systematic response, health provider training, universal applicability and automatization potential need to be highlighted to counterbalance the weakness and limitations that have also been described. The near future will probably increase availability of EWS, reliability and predictive value through the spread and acceptability of continuous monitoring in general ward, its integration in decision support algorithms with automatic alerts and the elaboration of temporal vital signs patterns that will finally allow to perform a personal modelling depending on individual patient characteristics.
Topics: Clinical Deterioration; Early Warning Score; Heart Rate; Hospital Rapid Response Team; Humans; Patient Safety; Respiratory Rate; Vital Signs
PubMed: 33742570
DOI: 10.1016/j.bpa.2020.12.013 -
Chest Jan 2022Clinicians use several measures to ascertain whether individual patients will tolerate liberation from mechanical ventilation, including the rapid shallow breathing... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Clinicians use several measures to ascertain whether individual patients will tolerate liberation from mechanical ventilation, including the rapid shallow breathing index (RSBI).
RESEARCH QUESTION
Given varied use of different thresholds, patient populations, and measurement characteristics, how well does RSBI predict successful extubation?
STUDY DESIGN AND METHODS
We searched six databases from inception through September 2019 and selected studies reporting the accuracy of RSBI in the prediction of successful extubation. We extracted study data and assessed quality independently and in duplicate.
RESULTS
We included 48 studies involving RSBI measurements of 10,946 patients. Pooled sensitivity for RSBI of < 105 in predicting extubation success was moderate (0.83 [95% CI, 0.78-0.87], moderate certainty), whereas specificity was poor (0.58 [95% CI, 0.49-0.66], moderate certainty) with diagnostic ORs (DORs) of 5.91 (95% CI, 4.09-8.52). RSBI thresholds of < 80 or 80 to 105 yielded similar sensitivity, specificity, and DOR. These findings were consistent across multiple subgroup analyses reflecting different patient characteristics and operational differences in RSBI measurement.
INTERPRETATION
As a stand-alone test, the RSBI has moderate sensitivity and poor specificity for predicting extubation success. Future research should evaluate its role as a permissive criterion to undergo a spontaneous breathing trial (SBT) for patients who are at intermediate pretest probability of passing an SBT.
TRIAL REGISTRY
PROSPERO; No.: CRD42020149196; URL: www.crd.york.ac.uk/prospero/.
Topics: Airway Extubation; Clinical Decision Rules; Clinical Decision-Making; Humans; Respiration, Artificial; Respiratory Rate; Tidal Volume; Ventilator Weaning
PubMed: 34181953
DOI: 10.1016/j.chest.2021.06.030 -
Respiratory Physiology & Neurobiology Aug 2019Respiratory frequency plasticity is a long-lasting increase in breathing frequency due to a perturbation. Mechanisms underlying respiratory frequency are poorly... (Review)
Review
Respiratory frequency plasticity is a long-lasting increase in breathing frequency due to a perturbation. Mechanisms underlying respiratory frequency are poorly understood, and there is little evidence of frequency plasticity in neonates. This hybrid review/research article discusses available literature regarding frequency plasticity and highlights potential research opportunities. Also, we include data demonstrating a model of frequency plasticity using isolated neonatal rat brainstem-spinal cord preparations. Specifically, substance P (SubP) application induced a long-lasting (>60 min) increase in spontaneous respiratory motor burst frequency, particularly in brainstem-spinal cords with the pons attached; there were no male/female differences. SubP-induced frequency plasticity is dependent on the application pattern, such that intermittent (rather than sustained) SubP applications induce more frequency plasticity. SubP-induced frequency plasticity was blocked by a neurokinin-1 receptor antagonist. Thus, the newborn rat respiratory control system has the capacity to express frequency plasticity. Identifying mechanisms that induce frequency plasticity may lead to novel methods to safely treat breathing disorders in premature and newborn infants.
Topics: Animals; Animals, Newborn; Brain Stem; Growth and Development; Neuronal Plasticity; Neurotransmitter Agents; Rats; Respiratory Rate; Spinal Cord; Substance P
PubMed: 31055188
DOI: 10.1016/j.resp.2019.04.014 -
Physiological Measurement Aug 2019Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR... (Review)
Review
Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.
Topics: Algorithms; Humans; Monitoring, Physiologic; Movement; Respiratory Function Tests; Respiratory Rate; Signal Processing, Computer-Assisted
PubMed: 31195383
DOI: 10.1088/1361-6579/ab299e -
Journal of Clinical Monitoring and... Dec 2022Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could...
Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.
Topics: Infant, Newborn; Humans; Respiratory Rate; Kenya; Capnography; Monitoring, Physiologic; Respiration
PubMed: 35332406
DOI: 10.1007/s10877-022-00840-2 -
Journal of Clinical Monitoring and... Jun 2022The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system...
The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RR) and tidal volume (TV) estimates. The bias and root mean squared difference (RMSD) accuracy between RR and the ventilator reference, RR, across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RR = 0.96 × RR + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TV and the reference TV across the whole data set was found to be - 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TV = 0.79 × TV-0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RR is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TV may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.
Topics: Humans; Monitoring, Physiologic; Respiration, Artificial; Respiratory Rate; Tidal Volume; Ventilators, Mechanical
PubMed: 33743106
DOI: 10.1007/s10877-021-00691-3 -
Chest Jul 2020
Topics: Critical Care; Humans; Pulmonary Gas Exchange; Respiration, Artificial; Respiratory Dead Space; Respiratory Rate; Tidal Volume
PubMed: 32654726
DOI: 10.1016/j.chest.2020.02.033 -
IEEE Transactions on Bio-medical... Mar 2020The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves)....
OBJECTIVE
The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated.
METHODS
The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals.
RESULTS
Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015 ± 0.021 Hz and 0.019 ± 0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034 ± 0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA.
CONCLUSION
The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed.
SIGNIFICANCE
The respiratory rate can be robustly estimated from the ECG in the presence of AF.
Topics: Aged; Aged, 80 and over; Atrial Fibrillation; Electrocardiography; Female; Humans; Male; Respiratory Rate; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio
PubMed: 31226064
DOI: 10.1109/TBME.2019.2923587