Continuous pulse oximetry and respiratory rate trends predict short-term respiratory and growth outcomes in premature infants

Warburton, Alyssa; Monga, Ranjan; Sampath, Venkatesh; Kumar, Navin

doi:10.1038/s41390-018-0269-4

Cited by 14 publications

(15 citation statements)

References 27 publications

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“…This algorithm is just a physiological extrapolation and current practice in the authors' unit. [13] To examine the correlation between interval vital signs recorded by nursing staff and continuous monitor recordings, and to determine whether aggregated monitor recordings could better predict impending escalation of respiratory support in premature infants. Ninety-four infants with median GA 32 weeks were enrolled and events of hypoxia (< 90%) and tachypnea (RR > 70/min) were recorded.…”

Section: What Is Available In Current Literature On Use Of Histogrammentioning

confidence: 99%

Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

Sur

Paria

2020

Eur J Pediatr

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Despite robust evidence in favour of maintaining optimal oxygen saturation targets in the preterm infants, the titration of oxygen is largely dependent on manual observations and transcription. Similarly, notwithstanding the gaining popularity of non-invasive modalities like high-flow nasal therapy, the practices of weaning and escalating support are largely individualized and based on point of care observations. These are often erroneous and lack objectivity. Histogram analysis from patient monitors is an easy and objective way of quantifying vital parameters and their trends. We review the technology and evidence available behind this practice. Conclusions: Though there are no randomized controlled trials on this practice solely, we identify several quality improvement studies implementing this into practice with benefit. We also cite studies which have implemented histogram analysis in methodology, thus concluding that this is a useful clinical tool worth incorporating into clinical practice to reduce manual errors and bring more objectivity into decisions.

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Section: What Is Available In Current Literature On Use Of Histogrammentioning

confidence: 99%

Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

Sur

Paria

2020

Eur J Pediatr

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“…In some clinical situations, high accuracy may not be necessary. However, in the emergency department or intensive care unit, when it is crucial to determine if a patient has crossed a diagnostic threshold, accurate RR measurement can enable early identification and expedited management of patient deterioration [14][15][16][17]. In research laboratories, accuracy and precision are essential for determining how a new device or method compares with the reference method.…”

Section: Introductionmentioning

confidence: 99%

Assessment of neonatal respiratory rate variability

Coleman¹,

Ginsburg

Macharia

et al. 2022

J Clin Monit Comput

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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.

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“…There is a high risk of mortality during the neonatal period, particularly in resource-constrained settings 1 . Continuous monitoring of neonatal vital signs enables early detection of physiological deterioration and potential opportunities for lifesaving interventions [2][3][4] . The development of new, innovative, non-invasive, multiparameter continuous physiological monitors specifically for neonates offers the promise of improving clinical outcomes in this vulnerable population.…”

Section: Introductionmentioning

confidence: 99%

Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

et al. 2021

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Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.

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Continuous pulse oximetry and respiratory rate trends predict short-term respiratory and growth outcomes in premature infants

Cited by 14 publications

References 27 publications

Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

Histogram analysis for bedside respiratory monitoring in not critically ill preterm neonates: a proposal for a new way to look at the monitoring data

Assessment of neonatal respiratory rate variability

Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

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