van den Bosch et al. Crit Care (2021) 25:280 https://doi.org/10.1186/s13054-021-03716-0 REVIEW Open Access Breathing variability—implications for anaesthesiology and intensive care Oscar F. C. van den Bosch* , Ricardo Alvarez‑Jimenez, Harm‑Jan de Grooth, Armand R. J. Girbes and Stephan A. Loer Abstract The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in signifcant fuctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory fow profles. Breath‑ ing variability is infuenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that afect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care. Keywords: Respiratory variability, Control of breathing, Spontaneous respiration, Coefcient of variation, Detrended fuctuation analysis, Entropy analysis, Perioperative period, Spontaneous breathing trial Background breathing variability is infuenced by several factors, such Te control of breathing involves a complex system that as aging, cognitive load, sleep pattern, and hypoxia, as balances the opposing goals of efciency, redundancy, well as medical conditions such as anxiety, obstructive or responsiveness, and stability [1]. It is characterised by restrictive lung disease, and arterial hypertension. During myriad inputs, internal pacemakers, positive and nega- anaesthesia and intensive care, additional factors, such as tive feedback loops, and nonlinear interactions between drugs and the efects of mechanical ventilation, may also diferent components (Fig. 1). Tis results in fuctuations infuence breathing variability. in breathing parameters, including the respiratory rate, Tis review describes the methods for quantifying tidal volume, and airfow profles (Table 1). breathing variability, summarises the conditions and If this regulation is too rigid, respiratory variability is comorbidities that afect breathing variability, and dis- low or absent, and the respiratory system cannot ade- cusses the potential implications of breathing variability quately react to stimuli. In contrast, if the respiratory sys- for anaesthesia and intensive care. tem is overreacting to internal and external stimuli, the system shows large fuctuations and loses control. Normal respiratory variability Te exact determinants of breathing variability are Te regulation of breathing facilitates adequate gas not precisely known; however, it has been shown that exchange for metabolic needs. Te impulses for inspi- the increase and decrease in breathing variability are ration and expiration are generated within the respira- strongly associated with pathological states. Normal tory centre in the medulla oblongata after receiving and processing input from various subsystems (Fig. 1 and *Correspondence: [email protected] Table 2). Consequently, respiration is characterised by Departments of Anesthesiology and Intensive Care, Amsterdam constant fuctuations in rate, rhythm, depth, and dura- UMC, VUMC, ZH 6F 003, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands tion [2, 3]. For instance, this physiological variability © The Author(s) 2021. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. van den Bosch et al. Crit Care (2021) 25:280 Page 2 of 13 Fig. 1 Input to the respiratory centre in the medulla oblongata and the pons Table 1 Tidal breathing parameters Te respiratory system receives input mainly from cen- RR Respiratory rate tral and peripheral chemoreceptors, mechanoreceptors within the airways and alveoli, locomotion receptors TI Inspiratory time of muscles and joints, and the (para)limbic system. Te TE Expiratory time breathing pattern may be infuenced to an extent and vol- Ttot Total breath cycle time untarily controlled, shortly, via input from the cerebral Ti/Ttot Ratio inspiratory time: total respiratory time cortex. For this purpose, the forebrain sends signals to VT Tidal volume the respiratory centre via independent pathways, over- VT/Ti Mean inspiratory fow ruling other inputs. Further factors include aferent input MV Minute ventilation from the vagus nerve and its branches, such as the supe- VE CO2 Expired CO2 volume per breath rior laryngeal nerves. External vagal stimulation has been FE CO2 Mixed expired CO2 fraction per breath shown to decrease respiratory rate [4], while stimulation Fet CO2 End‑expiratory CO2 fraction of the superior laryngeal nerves may afect the chest wall and airway muscles [5]. Inspiratory time and tidal volume remain strongly correlated, suggesting a constant fow at a steady chemical drive [6, 7]. may range between 19 and 34% for tidal volume and Diferent models have been developed to predict between 16 and 22% for respiratory rate, expressed as respiratory variability. One of the frst models used a the coefcient of variation, in awake persons (Table 3). semi-mechanistic approach based on feedback loops of van den Bosch et al. Crit Care (2021) 25:280 Page 3 of 13 Table 2 Control of spontaneous respiration Location Stimulus Pathway Central chemoreceptors Ventral medulla CSF pH Direct efect Peripheral chemoreceptors Bifurcation of common carotid artery, aortic Arterial pH, PaO2, PaCO2, Glossopharyngeal nerve, vagal nerve arch Mechanoreceptors Tracheal and bronchial muscle spindles Stretching of lung parenchyma Vagal nerve Airways and alveoli Temperature, dust, noxious gases Vagal nerve Locomotion receptors Muscles and joints Limb movement Spinal pathways Other Cerebral cortex and (para)limbic system Emotion, pain, arousal Monosynaptic connections Cerebral cortex Conscious/voluntary control Corticospinal pathways 1 2 measured (cardiac output and mixed venous blood CO2 N (x−x) CV(x) = x (1) partial pressure) and estimated parameters (CO2 sen- sitivity, mean lung volume for CO2, circulating time) and integrated the result into a compartmental model N ( ) = 1 (| − |2) using diferential Eqs. [8]. For these calculations, several RMSSD x N xi−1 xi (2) assumptions were necessary, including constant hemo- i=1 dynamic parameters, absence of intracardiac or pulmo- nary shunting, and instant intra-alveolar equilibration of Quantitative time series analysis: CV: coefcient of variation; RMSSD: root mean square of successive dif- CO2 and oxygen tensions. Another more recent approach ferences; N: number of samples; x : measured variable; x : used spectral analysis of all variables to predict oscillatory x rhythmicity [9]. Tis model incorporated inspiratory and mean of . expiratory times and volumes, as well as end-tidal CO 2 Detrended fuctuation analysis partial pressures and driving parameters. Of note, both models were based on measurements of healthy partici- While quantitative time series analysis is used to measure pants and can be extrapolated to patients with caution. short- and long-term variations, detrended fuctuation analysis (DFA) is used to detect long-range correlations in time series [10, 11]. We will address detrended fuc- Quantifcation of respiratory variability tuation analysis as “correlated variability.” It is based Diferent methods are available for the quantifcation of on the assumption that variations are due to extrinsic breathing variability, including quantitative time series stimuli that cause local efects or the intrinsic dynam- analysis, detrended fuctuation analysis, entropy analysis, ics of the system causing long-range correlations. To frequency distribution analysis, spectrum analysis, and quantify the intrinsic variability of the system, the local power-law analysis [1]. In this section, we focus on the efects are subtracted. Te algorithm consists of several frst three techniques. steps, starting with a time series of the measured vari- able x , such as tidal volume or respiratory rate. A new Quantitative time series analysis integrated time series XT is calculated by summing the A quantitative time series analysis evaluates the standard diferences between the individual value xi and the aver- deviation or interquartile range over time, such as the age x for all values (Eq. 3). Subsequently, this trend func- standard deviation of tidal volume or respiratory rate. tion XT is divided into epochs of length n , and the local Te coefcient of variation