Abstract
During surgery, various haemodynamic variables are monitored and optimised to maintain organ perfusion pressure and oxygen delivery – and to eventually improve outcomes. Important haemodynamic variables that provide an understanding of most pathophysiologic haemodynamic conditions during surgery include heart rate, arterial pressure, central venous pressure, pulse pressure variation/stroke volume variation, stroke volume, and cardiac output. A basic physiologic and pathophysiologic understanding of these haemodynamic variables and the corresponding monitoring methods is essential. We therefore revisit the pathophysiologic rationale for intraoperative monitoring of haemodynamic variables, describe the history, current use, and future technological developments of monitoring methods, and finally briefly summarise the evidence that haemodynamic management can improve patient-centred outcomes.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
1 Introduction
Haemodynamic monitoring is the serial or continuous measurement of haemodynamic variables. Guiding therapeutic interventions based on haemodynamic monitoring is referred to as haemodynamic management. During surgery, the core objectives of haemodynamic monitoring and management are to ensure patient safety and to maintain organ perfusion pressure and oxygen delivery. Both adequate perfusion pressure and oxygen delivery are essential to maintain cellular metabolism of vital organs (Fig. 1) [1, 2]. The overarching goal of intraoperative haemodynamic monitoring and management is to maintain organ function and improve patient outcomes.
In this narrative review, we revisit the pathophysiologic rationale for intraoperative monitoring of haemodynamic variables. We further describe the history, current use, and future technological developments of monitoring methods. Finally, we briefly summarise the evidence that haemodynamic management can improve patient-centred outcomes.
1.1 Heart rhythm and heart rate
Heart rate, together with stroke volume, is a main determinant of cardiac output (CO). Heart rate regulation is complex and includes neural und humoral control systems for short- and long-term adaptation of heart rate to metabolic needs. The normal heart rhythm is sinus rhythm, and the normal resting heart rate in adults is 60–100 beats per minute. Intraoperative heart rhythm and heart rate monitoring allows identifying cardiac arrythmias and abnormal high or low heart rates. Heart rhythm and heart rate monitoring are essential to ensure patient safety during surgery.
First attempts to monitor intraoperative heart rhythm and heart rate date back to 1896 when heart rate was assessed using a stethoscope to understand the effects of chloroform on cardiac physiology [3]. Later, this method was proposed to continuously monitor heart rhythm and rate [4]. In 1918, electrocardiography was first used to monitor intraoperative heart rate [5]. Four years later, a prospective study used electrocardiography to investigate the effects of anaesthesia and surgery on heart rhythm and rate [6]. In 1952, Himmelstein and Scheiner used an instrument called cardiotachoscope to continuously display the electrocardiogram and heart rate on a cathode ray screen [7]. Around 20 years later, intraoperative electrocardiography was proposed to detect acute myocardial ischemia [8].
Today, intraoperative heart rhythm and rate monitoring with electrocardiography is mandated by the European recommendations for standards of monitoring during anaesthesia and recovery [9] and the American Society of Anesthesiologists Standards for Basic Anesthetic Monitoring [10]. In the operating room, electrocardiography systems with three and five electrodes are most commonly used. The limb leads are typically placed on the shoulders, and the placement of the single precordial lead is variable and depends on the surgical procedure. The V5 lead is most sensitive to ST-segment changes, capturing 75% of events; V4 captures about 60% of ST-segment changes; the other precordial locations are significantly less sensitive [11]. Most electrocardiography systems use computerised ST-segment algorithms that compare the ST-segment and the iso-electric point from the PR-interval [12]. In addition to electrocardiography, photoplethysmography and intraarterial blood pressure waveforms can be used to derive pulse rate that equals heart rate when the patient has no pulse deficit.
Heart rates during surgery with general anaesthesia are usually lower than heart rates during physiologic sleep [13]. Patients on chronic beta blocker therapy are especially prone to develop intraoperative bradycardia [14]. Although intraoperative bradycardia can cause a decrease in CO and hypotension, it remains unknown what constitutes physiologically important intraoperative bradycardia [15]. Whether intraoperative bradycardia is related to organ injury is scarcely investigated. However, intraoperative bradycardia should presumably be treated when it is accompanied by profound hypotension or low CO [16, 17].
Intraoperative tachycardia is also common and can indicate hypovolaemia, inadequate depth of anaesthesia, or insufficient analgesia. A single-centre cohort study [18] and a secondary analysis [19] of the VISION study [20] suggest that intraoperative heart rates above 100 beats per minute are associated with myocardial injury, myocardial infarction, and death in patients having noncardiac surgery. In contrast, another single-centre cohort study in noncardiac surgery patients found no association between intraoperative heart rates above 80, 90, and 100 beats per minute and a composite outcome of myocardial injury and death [21].
In post-cardiac surgery patients with temporary epicardial pacing, individualised heart rate optimisation can help increase CO [22]. However, during noncardiac surgery, heart rate is rarely directly targeted and modified. There are thus no studies on the effect of intraoperative targeted heart rate management and outcomes. Beta blockers – such as metoprolol – may prevent intraoperative tachycardia and decrease the incidence of perioperative myocardial infarction but extended-release metoprolol is associated with perioperative hypotension and increased postoperative mortality in noncardiac surgery patients [23].
1.2 Arterial pressure
Arterial pressure results from the interaction between CO and systemic vascular tone – and is characterised by three components, namely, systolic, mean, and diastolic arterial pressure. Mean arterial pressure – the mean pressure over the cardiac cycle – is the inflow pressure for most organs, while the outflow pressure is the higher of either central venous pressure (CVP) or extravascular pressure in a specific tissue, organ, or compartment [24, 25]. Systolic arterial pressure is determined by left ventricular stroke volume, vascular compliance, backward reflected waves, and pulse amplification and reflects left ventricular afterload [24, 25]. Diastolic arterial pressure is primarily determined by systemic vascular tone [24, 25]. The difference between systolic and diastolic arterial pressure is called pulse pressure and closely reflects stroke volume.
The beginning of intermittent oscillometric monitoring dates back to the beginning of the 19th century when sphygmomanometers were used to measure arterial pressure during surgery [26, 27]. However, it took around 50 years until arterial pressure gained attention in perioperative medicine. In 1951, shortly after introducing oscillometry during anaesthesia [28], systolic arterial pressures of 30 mmHg were considered safe in young and healthy patients [29]. First attempts to measure arterial pressure with a plastic catheter inserted into a peripheral artery in humans date back to 1949 [30]. However, at that time, this method was considered to be “unsuitable for routine use during anaesthesia” because it “requires the introduction of a thin plastic catheter into a peripheral artery” [31]. In 1986, the American Society of Anesthesiologists formulated that arterial pressure monitoring is mandatory during anaesthesia [32]. Today, the American Society of Anesthesiologists Standards for Basic Anesthetic Monitoring mandate the measurement of arterial pressure at least every five minutes [10].
Currently, two methods are routinely used to monitor arterial pressure during surgery: intermittent oscillometric monitoring with an upper-arm cuff and continuous intraarterial monitoring with an arterial catheter [33,34,35]. Continuous noninvasive monitoring with finger-cuffs is also available – but not yet implemented for routine use [33,34,35].
Automated oscillometry is noninvasive, easy to use, and comparatively cheap. An obvious limitation is that oscillometry provides arterial pressures only intermittently. Furthermore, the measurement performance of oscillometry is highly dependent on the measurement site [36], appropriate cuff size [37], and cuff position [38]. Notably, oscillometry overestimates low and underestimates high arterial pressures – and may thus miss hypotension and hypertension [39,40,41].
Intraarterial monitoring with an arterial catheter is the clinical reference method to continuously measure arterial pressure [42]. Serious complications caused by arterial catheters – such as ischemia or major bleeding – are very rare [43]. While intraarterial monitoring is more accurate than oscillometry, it does require that the measurement system is properly levelled or zeroed and damped to avoid overdamping or underdamping (Fig. 2) [35, 42]. Intraarterial monitoring may help reduce hypotension during and after anaesthetic induction as well as during surgery compared to intermittent oscillometric monitoring [44, 45].
An alternative to intermittent noninvasive oscillometric and continuous intraarterial monitoring is continuous noninvasive arterial pressure monitoring using the finger-cuff-based vascular-unloading technique [33]. Validation studies investigating the measurement performance of the vascular-unloading technique versus intraarterial monitoring revealed heterogeneous results [46]. Several studies demonstrated interchangeability between arterial pressure measurements with the vascular-unloading technique and intraarterial measurements, but only one third of studies reported accuracy and precision meeting current international standards [46]. Importantly, the vascular-unloading technique provides arterial pressures continuously and its measurement performance seems to be at least as good as that of intermittent oscillometry [36, 47]. However, in patients with circulatory shock or high-dose vasopressor therapy the vascular-unloading technique becomes unreliable because of impaired finger perfusion [48, 49]. While the accuracy of the vascular-unloading technique is yet to be fully established, continuous monitoring using the vascular-unloading technique reduces the incidence of post-induction and intraoperative hypotension when compared to intermittent oscillometric monitoring [50, 51]. Miniaturised wireless systems with sensors integrated in a finger-ring are currently being developed [52].
The pulse decomposition method allows continuously reconstructing arterial pressure waveforms from a finger-cuff and a piezo electric sensor [53]. First studies suggest that this new noninvasive method meets current international standards for arterial pressure monitoring both in surgical [54] and critically ill patients [55].
The hydraulic coupling method has been proposed to noninvasively measure arterial pressure [56]. The main advantage of this technology is that it increases the signal-to-noise ratio compared to conventional oscillometry by using silicon-oil instead of air to transmit oscillations [56, 57]. A first validation study performed by the developers reported good agreement with intraarterial measurements from femoral arterial catheters [56]. In addition, the hydraulic coupling method allows reconstructing arterial pressure waveforms [58].
Artificial intelligence can be used to analyse the arterial pressure waveform to predict hypotension or identify underlying causes of hypotension. One of the first attempts to use artificial intelligence to predict hypotension by analysing arterial pressure waveform features is the hypotension prediction index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) [59]. A registry study suggests that using HPI-software monitoring may help clinicians reduce intraoperative hypotension during noncardiac surgery [60, 61]. However, trials investigating the effect of HPI-software monitoring on intraoperative hypotension revealed contradictory results: while a small trial suggested that HPI-software monitoring helps reduce hypotension [62], a larger trial did not [63]. There are ongoing scientific controversies around HPI-software validation [64] and on whether HPI values just reflect mean arterial pressure values or provide predictive capabilities beyond changes in mean arterial pressure per se [65, 66].
Artificial intelligence can also help identify root causes of hypotension. In patients having major abdominal surgery, artificial intelligence was used to identify endotypes of intraoperative hypotension [15]. During episodes of hypotension, an unsupervised machine learning algorithm grouped measurements of stroke volume index, heart rate, cardiac index, systemic vascular resistance index, and pulse pressure variation (PPV) into hypotension endotypes, namely, myocardial depression, bradycardia, vasodilation, hypovolaemia, and mixed endotype [15]. It remains to be determined if considering hypotension endotypes helps treat hypotension causally and improve outcomes.
Although underlying causes of intraoperative hypotension are well described, individual hypotension harm thresholds remain largely unknown. On a population basis, intraoperative mean arterial pressures below 60–70 mmHg are associated with organ injury [67,68,69,70,71,72,73]. Organ injury is a function of hypotension severity and duration. Harm from hypotension accrues at profoundly low pressures rather than from long exposure to moderately low pressures [74]. While the association between intraoperative hypotension and organ injury is well established, it remains largely unknown whether the association between intraoperative hypotension and complications observed in registry studies is indeed causal – and thus amenable to interventions. Additionally, although intraoperative hypotension at some level causes organ injury, harm thresholds for individual patients also remain unclear [75,76,77,78]. Universally targeting mean arterial pressures higher than 60 mmHg during surgery does not reduce postoperative complications in noncardiac surgery patients [77, 78]. In contrast, individualising intraoperative arterial pressure targets based on preoperative resting arterial pressures reduced postoperative complications compared to routine arterial pressure management in a multicentre trial of 298 noncardiac surgery patients [75]. Ongoing trials will provide more evidence on the effect of using fixed (NCT04884802) or individualised [79] intraoperative arterial pressure targets on outcomes of high-risk noncardiac surgery patients.
1.3 Pulse pressure variation and stroke volume variation
PPV and stroke volume variation (SVV) are dynamic variables that can be used to predict fluid responsiveness in mechanically ventilated patients (Fig. 3) [80, 81]. PPV and SVV primarily reflect cyclic changes in left ventricular stroke volume that are caused by positive pressure ventilation in mechanically ventilated patients [82].
Cyclic changes in venous return and aortic blood flow in mechanically ventilated patients were first reported in 1966 [83]. First attempts to quantify these changes date back to 1978 [84]. Research on the relation between systolic pressure variation and blood volume [85, 86] finally lead to the use of PPV [87] and SVV [88] to predict fluid responsiveness. Today, many regular bedside monitors automatically provide PPV. In contrast, SVV naturally requires the estimation of stroke volume with advanced haemodynamic monitoring systems. PPV also can be calculated based on noninvasively obtained continuous arterial pressure waveforms [58, 89]. Recently, it has been shown that the hydraulic coupling method [56] allows the reconstruction of the arterial pressure waveform and calculation of PPV [58].
PPV and SVV continuously provide information on fluid responsiveness and predict fluid responsiveness more accurately than static preload variables – e.g., CVP [90]. Additionally, predicting fluid responsiveness with PPV and SVV does not require fluid administration (like when performing fluid challenges [91]) or patient positioning (like when performing passive leg raising tests [92] that are usually impossible during surgery). However, certain clinical situations preclude the use of dynamic cardiac preload variables – including cardiac arrhythmias, tidal volumes of less than 7–8 ml/kg, and high intraabdominal pressure (e.g., during laparoscopic surgery) [93, 94]. In patients with low tidal volume ventilation, a tidal volume challenge may help increase the predictive value of PPV and SVV to reliably predict fluid responsiveness [95]. Whenever PPV or SVV cannot be used, other tests including fluid challenges [91], passive leg raising tests [92], and end-expiratory occlusion tests [96] may be used to assess fluid responsiveness. For both PPV and SVV thresholds of 11% have been suggested to predict fluid responsiveness [97]. However, PPV values in a ‘grey zone’ between 9 and 13% [98] are inconclusive regarding fluid responsiveness. PPV and SVV are frequently used to titrate fluid administration within perioperative goal-directed haemodynamic therapy protocols and may help reduce net fluid administration and postoperative complications [99, 100] – mainly when fluid management based on dynamic cardiac preload variables is combined with blood flow optimisation [101].
1.4 Central venous pressure
CVP is the venous pressure in the superior vena cava near the right atrium and thus an estimate of right atrial pressure.
The first catheterisation of a central vein was performed in 1733 with a glass tube introduced in the jugular vein of a horse to measure CVP [102]. About two centuries later, Werner Frossman inserted a urinary catheter into his arm vein up to his right heart [103]. Thereafter, it again took some years before clinicians started measuring CVP to guide haemodynamic therapy [104].
CVP can be measured in patients with a central venous catheter or a dedicated pulmonary artery catheter with a right atrial port. CVP should ideally be measured at end-expiration at the base of the c wave (so called z-point) because this point reflects the final pressure in the ventricle before onset of systole (Fig. 4) [105]. However, the c wave is not always easy to identify and in practice the mean CVP or the pressure at the base of the a wave is considered. CVP values and waveforms are influenced by numerous factors, including blood volume, cardiac function and pathologies, intrathoracic pressure, and venous compliance [105]. Although CVP is not a good marker of fluid responsiveness [90, 106] or volume status [107], it may be useful as a marker of right heart function. In addition to the absolute value of the CVP, the CVP waveform – which consists of a, c, and v waves, as well as x and y descents – may help identify pathophysiologic situations. For example, a loss of the a wave occurs in atrial fibrillation, and high v waves occur in patients with tricuspid regurgitation. Because absolute mean CVP values are normally close to zero in spontaneously breathing patients, correct levelling or zeroing of the measurement system is crucial [108]. Furthermore, considering transmural pressure is essential when interpreting CVP because CVP changes during the respiratory cycle and is affected by the positive end-expiratory pressure during mechanical ventilation.
Recent advances in CVP monitoring include noninvasive estimation of CVP using jugular near infrared spectroscopic sensors [109, 110]. However, as of today the number of validation studies investigating this method is very limited and the clinical usefulness remains unknown [109, 110]. CVP may be estimated without a central venous catheter from peripheral venous pressure as a surrogate for CVP [111,112,113,114].
During intraoperative haemodynamic management, CVP should not be used as a target variable. CVP should rather be considered a safety variable – with high (and especially rapidly increasing) CVP indicating haemodynamic problems such as acute right heart failure or right ventricular out-flow tract obstruction. Considering CVP changes in the context of other haemodynamic variables may help understand haemodynamic alterations.
1.5 Cardiac output and stroke volume
CO is the product of stroke volume and heart rate. CO – together with arterial oxygen content – determines oxygen delivery. Tissue hypoperfusion may result in organ injury [2]. The rationale to monitor CO during surgery thus is to avoid organ injury by maintaining oxygen delivery. Additionally, knowing stroke volume and CO helps understand the underlying mechanisms of haemodynamic instability that may include a decrease in blood flow or a decrease in vascular tone. Current guidelines suggest that CO or stroke volume monitoring may be considered in patients with a high risk for complications [115].
First attempts to compute CO in animals date back to 1870, when Adolf Fick described the Fick’s principle. Fick’s original principle was based on the extraction of oxygen through the systemic circulation. Later on, the Fick principle was adapted and used for the development of indicator dilution methods in 1897 [116] and thermodilution methods in 1954 [117]. In parallel, Otto Frank started the first attempts to estimate stroke volume and CO using pulse wave analysis based on the Windkessel model – a model that assumes that at a steady haemodynamic state, the amount of blood entering a blood vessel is equal to the amount of blood leaving the vessel during the cardiac cycle [118]. Based on the initial research of Fick and Frank, two CO monitoring methods that are still routinely used today were developed: thermodilution [119] and pulse wave analysis [120,121,122].
There are numerous methods to measure stroke volume and CO in patients having surgery – including pulmonary artery or transpulmonary thermodilution, pulse wave analysis, oesophageal Doppler, and bioreactance/bioimpedance techniques (Fig. 5) [123,124,125,126]. Thermodilution methods are considered clinical reference methods to measure CO [119], but – due to their invasiveness – they are rarely used in patients having noncardiac surgery. Thermodilution methods are thus reserved for special indications including liver transplant and cardiac surgery [124]. Pulse wave analysis is commonly used to measure CO during surgery [127].
Pulse wave analysis algorithms continuously analyse the arterial pressure waveform to estimate stroke volume and CO [120,121,122]. Pulse wave analysis systems can be classified as invasive/minimally-invasive or noninvasive – depending on whether the arterial pressure waveform is measured with an arterial catheter or a noninvasive sensor (Fig. 6) [120,121,122]. The systems can additionally be classified considering the type of calibration. Externally calibrated systems are calibrated using an external measurement technique that usually is an indicator dilution method [120,121,122]. Internally calibrated systems use biometric, demographic, and haemodynamic data to calibrate pulse wave analysis-derived CO values [120,121,122]. Uncalibrated systems rely on special algorithms such as the pressure recording analytical method allowing beat-to-beat impedance estimations and further calculation of haemodynamic variables [120,121,122]. An advantage of pulse wave analysis is the continuous estimation of stroke volume. However, the measurement performance of pulse wave analysis can be impaired when vascular tone is substantially altered or rapidly changing [128]. For pulse wave analysis being able to accurately estimate stroke volume, the analysed arterial pressure waveform needs to be correctly damped, i.e. the dynamic response of the measurement system needs to be adequate [42, 129]. Using mechanical or electronic filters to identify and correct abnormal waveform damping can help improve pulse wave analysis-derived stroke volume and CO measurements [129].
With the oesophageal Doppler method stroke volume is estimated based on blood flow velocity in the descending aorta and the aortic cross-sectional area (assuming that blood flow distribution between the upper and lower parts of the arterial system is constant) [130]. The oesophageal Doppler method allows continuous beat-to-beat stroke volume estimation independent from changes in vascular tone, but the measurement performance substantially depends on the correct estimation of the diameter of the aorta used to calculate the aortic cross-sectional area [131]. Additionally, the oesophageal Doppler probe needs to be frequently repositioned and thus requires user attention and is operator dependent.
Thoracic bioimpedance/bioreactance are noninvasive methods to estimate stroke volume and CO by measuring the frequency modulation when an oscillating voltage is applied across the thorax [126, 132,133,134]. In short, these methods estimate the volume of electrically conducting blood moving in and out of the chest as a surrogate for stroke volume [126, 132,133,134]. Bioimpedance/bioreactance measurements can be disturbed by motion, electrical interference, arrhythmias, pleural effusion, pulmonary oedema, and mechanical ventilation [126, 132,133,134].
CO is determined by metabolic needs [135]. Therefore, there is no “normal CO”. Resting CO varies substantially among individuals – but generally decreases with age [136]. Although arterial pressure and CO are physiologically coupled, there is no clinically meaningful correlation between arterial pressure and CO in patients having surgery [137]. Perioperative CO-guided management – often subsumed under the umbrella term “goal-directed haemodynamic therapy” [138] – was proposed in the 1970s by William C. Shoemaker [139]. Since then, numerous – mainly small and fragile – trials investigated the effect of different CO-guided management strategies on patient outcome. While CO targets and therapeutic interventions substantially differ among trials [140, 141], cumulative evidence suggests that CO-guided management may help reduce postoperative complications and even mortality [99, 142,143,144,145,146]. However, in the largest trial so far, the OPTIMISE II trial, maximising stroke volume using fluids and dobutamine did not reduce the incidence of postoperative infectious complications or any other complication (OPTIMISE II trial [147]; Presented at EBPOM World Congress of Prehabilitation Medicine 2023 in London on July 6, 2023).
Recent developments in the field of intraoperative CO monitoring focus on accessibility and sustainability [148]. To be implemented in routine care, CO monitoring systems need to be accessible. Costs of haemodynamic monitoring equipment is still perceived as a major barrier to hospital adoption [149, 150]. Sustainability is an increasing concern in anaesthesiology [151]. Disposable-free monitoring solutions have the advantage to decrease plastic waste, carbon dioxide emission, and costs [148].
2 Summary
During surgery, various haemodynamic variables are monitored and optimised to ensure patient safety, maintain organ perfusion pressure and oxygen delivery, and eventually avoid organ injury and improve patient outcomes. Important haemodynamic variables that provide an understanding of most pathophysiologic haemodynamic conditions during surgery include heart rate, arterial pressure, CVP, PPV, SVV, stroke volume, and CO. Future research should focus on the development of accessible and sustainable monitoring methods that reliably measure haemodynamic variables – preferably in a wireless, interconnected, and noninvasive manner.
Data availability
No datasets were generated or analysed during the current study.
References
Saugel B, Vincent JL, Wagner JY. Personalized hemodynamic management. Curr Opin Crit Care. 2017;23:334–41. https://doi.org/10.1097/MCC.0000000000000422.
Parker T, Brealey D, Dyson A, Singer M. Optimising organ perfusion in the high-risk surgical and critical care patient: a narrative review. Br J Anaesth. 2019;123:170–6. https://doi.org/10.1016/j.bja.2019.03.027.
Kirk R. On auscultation of the Heart during Chloroform Narcosis. Br Med J. 1896;2:1704–6. https://doi.org/10.1136/bmj.2.1876.1704.
Teter CK. (1909) Thirteen thousand administrations of nitrous oxid with oxygen as an anesthetic. JAMA 448–54.
Heard JDSA. A report on the electrocardiographic study of two cases of nodal rhythm exhibiting R-P interval. Am J Med Soc. 1918;75:238–51.
Lennox WG. An Electrocardiographic Study of fifty patients during operation. Arch Intern Med. 1922;30:57–72. https://doi.org/10.1001/archinte.1922.00110070060004.
Himmelstein A, Scheiner M. The cardiotachoscope. Anesthesiology. 1952;13:62–4. https://doi.org/10.1097/00000542-195201000-00007.
Kaplan JA, King SB 3rd. The precordial electrocardiographic lead (V5) in patients who have coronary-artery disease. Anesthesiology. 1976;45:570–4.
Klein AA, Meek T, Allcock E, Cook TM, Mincher N, Morris C, Nimmo AF, Pandit JJ, Pawa A, Rodney G, Sheraton T, Young P. Recommendations for standards of monitoring during anaesthesia and recovery 2021: Guideline from the Association of Anaesthetists. Anaesthesia. 2021;76:1212–23. https://doi.org/10.1111/anae.15501.
Committee on Standards and Practice Parameters. (2020) Standards for Basic Anesthetic Monitoring of the American Society of Anesthesiologists. Available online: https://www.asahq.org/standards-and-practice-parameters/standards-for-basic-anesthetic-monitoring. Last access date: 09.11.2023.
London MJ, Hollenberg M, Wong MG, Levenson L, Tubau JF, Browner W, Mangano DT. Intraoperative myocardial ischemia: localization by continuous 12-lead electrocardiography. Anesthesiology. 1988;69:232–41.
Ansley DM, O’Connor JP, Merrick PM, Ricci DR, Dolman J, Kapnoudhis P. On line ST-segment analysis for detection of myocardial ischaemia during and after coronary revascularization. Can J Anaesth. 1996;43:995–1000. https://doi.org/10.1007/BF03011899.
Kouz K, Hoppe P, Reese P, Burfeindt C, Flick M, Briesenick L, Nitzschke R, Pinnschmidt H, Saugel B. Relationship between intraoperative and preoperative ambulatory nighttime heart rates: a secondary analysis of a prospective observational study. Anesth Analg. 2021;133:406–12. https://doi.org/10.1213/ANE.0000000000005625.
Cheung CC, Martyn A, Campbell N, Frost S, Gilbert K, Michota F, Seal D, Ghali W, Khan NA. Predictors of intraoperative hypotension and bradycardia. Am J Med. 2015;128:532–8. https://doi.org/10.1016/j.amjmed.2014.11.030.
Kouz K, Brockmann L, Timmermann LM, Bergholz A, Flick M, Maheshwari K, Sessler DI, Krause L, Saugel B. Endotypes of intraoperative hypotension during major abdominal surgery: a retrospective machine learning analysis of an observational cohort study. Br J Anaesth. 2023;130:253–61. https://doi.org/10.1016/j.bja.2022.07.056.
Kusumoto FM, Schoenfeld MH, Barrett C, Edgerton JR, Ellenbogen KA, Gold MR, Goldschlager NF, Hamilton RM, Joglar JA, Kim RJ, Lee R, Marine JE, McLeod CJ, Oken KR, Patton KK, Pellegrini CN, Selzman KA, Thompson A, Varosy PD. 2018 ACC/AHA/HRS Guideline on the evaluation and management of patients with Bradycardia and Cardiac Conduction Delay: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice guidelines and the Heart Rhythm Society. Circulation. 2019;140:e382–482. https://doi.org/10.1161/CIR.0000000000000628.
Watterson LM, Morris RW, Westhorpe RN, Williamson JA. Crisis management during anaesthesia: bradycardia. Qual Saf Health Care. 2005;14:e9. https://doi.org/10.1136/qshc.2002.004481.
Shcherbakov A, Bisharat N. Associations between different measures of intra-operative tachycardia during noncardiac surgery and adverse postoperative outcomes: a retrospective cohort analysis. Eur J Anaesthesiol. 2022;39:145–51. https://doi.org/10.1097/EJA.0000000000001618.
Abbott TEF, Pearse RM, Archbold RA, Ahmad T, Niebrzegowska E, Wragg A, Rodseth RN, Devereaux PJ, Ackland GL. A prospective International Multicentre Cohort Study of Intraoperative Heart Rate and systolic blood pressure and myocardial Injury after noncardiac surgery: results of the VISION Study. Anesth Analg. 2018;126:1936–45. https://doi.org/10.1213/ANE.0000000000002560.
Vascular Events In Noncardiac Surgery Patients Cohort Evaluation Study Investigators, Devereaux PJ, Chan MT, Alonso-Coello P, Walsh M, Berwanger O, Villar JC, Wang CY, Garutti RI, Jacka MJ, Sigamani A, Srinathan S, Biccard BM, Chow CK, Abraham V, Tiboni M, Pettit S, Szczeklik W, Lurati Buse G, Botto F, Guyatt G, Heels-Ansdell D, Sessler DI, Thorlund K, Garg AX, Mrkobrada M, Thomas S, Rodseth RN, Pearse RM, Thabane L, McQueen MJ, VanHelder T, Bhandari M, Bosch J, Kurz A, Polanczyk C, Malaga G, Nagele P, Le Manach Y, Leuwer M, Yusuf S. Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. JAMA. 2012;307:2295–304. https://doi.org/10.1001/jama.2012.5502.
Ruetzler K, Yilmaz HO, Turan A, Zimmerman NM, Mao G, Hung MH, Kurz A, Sessler DI. Intra-operative tachycardia is not associated with a composite of myocardial injury and mortality after noncardiac surgery: a retrospective cohort analysis. Eur J Anaesthesiol. 2019;36:105–13. https://doi.org/10.1097/EJA.0000000000000925.
Tavazzi G, Kontogeorgis A, Guarracino F, Bergsland N, Martinez-Naharro A, Pepper J, Price S. Heart Rate Modification of Cardiac output following cardiac surgery: the importance of Cardiac Time intervals. Crit Care Med. 2017;45:e782–8. https://doi.org/10.1097/CCM.0000000000002410.
Devereaux PJ, Yang H, Yusuf S, Guyatt G, Leslie K, Villar JC, Xavier D, Chrolavicius S, Greenspan L, Pogue J, Pais P, Liu L, Xu S, Malaga G, Avezum A, Chan M, Montori VM, Jacka M, Choi P. Effects of extended-release metoprolol succinate in patients undergoing non-cardiac surgery (POISE trial): a randomised controlled trial. Lancet. 2008;371:1839–47. https://doi.org/10.1016/s0140-6736(08)60601-7.
Saugel B, Sessler DI. Perioperative blood pressure management. Anesthesiology. 2021;134:250–61. https://doi.org/10.1097/ALN.0000000000003610.
Ackland GL, Brudney CS, Cecconi M, Ince C, Irwin MG, Lacey J, Pinsky MR, Grocott MP, Mythen MG, Edwards MR, Miller TE, Perioperative Quality Initiative-3 workgroup. Perioperative Quality Initiative consensus statement on the physiology of arterial blood pressure control in perioperative medicine. Br J Anaesth. 2019;122:542–51. https://doi.org/10.1016/j.bja.2019.01.011. Postoperative blood pressure group.
Cushing H. On routine determinations of arterial tension in operating Room and Clinic. Boston Med Surg J. 1903;148:250–6. https://doi.org/10.1056/nejm190303051481002.
Hill L, Barnard H. A simple and accurate form of sphygmometer or arterial pressure gauge contrived for clinical use. Br Med J. 1897;2:904. https://doi.org/10.1136/bmj.2.1918.904.
Barry CT. Oscillometry during anaesthesia. Anaesthesia. 1950;5:26–35. https://doi.org/10.1111/j.1365-2044.1950.tb13684.x.
Organe GS. Hypotension in anaesthesia. Curr Res Anesth Analg. 1953;32:19–22.
Peterson LH, Dripps RD, Risman GC. A method for recording the arterial pressure pulse and blood pressure in man. Am Heart J. 1949;37:771–82. https://doi.org/10.1016/0002-8703(49)90175-1.
Downing DM. Continuous blood-pressure indication. Anaesthesia. 1954;9:35–7. https://doi.org/10.1111/j.1365-2044.1954.tb01501.x.
Eichhorn JH, Cooper JB, Cullen DJ, Maier WR, Philip JH, Seeman RG. Standards for patient monitoring during anesthesia at Harvard Medical School. JAMA. 1986;256:1017–20.
Saugel B, Dueck R, Wagner JY. Measurement of blood pressure. Best Pract Res Clin Anaesthesiol. 2014;28:309–22. https://doi.org/10.1016/j.bpa.2014.08.001.
Bartels K, Esper SA, Thiele RH. Blood pressure monitoring for the anesthesiologist: a practical review. Anesth Analg. 2016;122:1866–79. https://doi.org/10.1213/ANE.0000000000001340.
Roach JK, Thiele RH. Perioperative blood pressure monitoring. Best Pract Res Clin Anaesthesiol. 2019;33:127–38. https://doi.org/10.1016/j.bpa.2019.05.001.
Schumann R, Meidert AS, Bonney I, Koutentis C, Wesselink W, Kouz K, Saugel B. Intraoperative blood pressure monitoring in obese patients. Anesthesiology. 2021;134:179–88. https://doi.org/10.1097/ALN.0000000000003636.
Ishigami J, Charleston J, Miller ER 3rd, Matsushita K, Appel LJ, Brady TM. Effects of Cuff size on the accuracy of blood pressure readings: the Cuff(SZ) randomized crossover trial. JAMA Intern Med. 2023. https://doi.org/10.1001/jamainternmed.2023.3264.
Netea RT, Lenders JW, Smits P, Thien T. Both body and arm position significantly influence blood pressure measurement. J Hum Hypertens. 2003;17:459–62. https://doi.org/10.1038/sj.jhh.1001573.
Wax DB, Lin HM, Leibowitz AB. Invasive and concomitant noninvasive intraoperative blood pressure monitoring: observed differences in measurements and associated therapeutic interventions. Anesthesiology. 2011;115:973–8. https://doi.org/10.1097/ALN.0b013e3182330286.
Meidert AS, Dolch ME, Muhlbauer K, Zwissler B, Klein M, Briegel J, Czerner S. Oscillometric versus invasive blood pressure measurement in patients with shock: a prospective observational study in the emergency department. J Clin Monit Comput. 2021;35:387–93. https://doi.org/10.1007/s10877-020-00482-2.
Picone DS, Schultz MG, Otahal P, Aakhus S, Al-Jumaily AM, Black JA, Bos WJ, Chambers JB, Chen CH, Cheng HM, Cremer A, Davies JE, Dwyer N, Gould BA, Hughes AD, Lacy PS, Laugesen E, Liang F, Melamed R, Muecke S, Ohte N, Okada S, Omboni S, Ott C, Peng X, Pereira T, Pucci G, Rajani R, Roberts-Thomson P, Rossen NB, Sueta D, Sinha MD, Schmieder RE, Smulyan H, Srikanth VK, Stewart R, Stouffer GA, Takazawa K, Wang J, Westerhof BE, Weber F, Weber T, Williams B, Yamada H, Yamamoto E, Sharman JE. Accuracy of Cuff-measured blood pressure: systematic reviews and Meta-analyses. J Am Coll Cardiol. 2017;70:572–86. https://doi.org/10.1016/j.jacc.2017.05.064.
Saugel B, Kouz K, Meidert AS, Schulte-Uentrop L, Romagnoli S. How to measure blood pressure using an arterial catheter: a systematic 5-step approach. Crit Care. 2020;24:172. https://doi.org/10.1186/s13054-020-02859-w.
Scheer B, Perel A, Pfeiffer UJ. Clinical review: complications and risk factors of peripheral arterial catheters used for haemodynamic monitoring in anaesthesia and intensive care medicine. Crit Care. 2002;6:199–204. https://doi.org/10.1186/cc1489.
Kouz K, Wegge M, Flick M, Bergholz A, Moll-Khosrawi P, Nitzschke R, Trepte CJC, Krause L, Sessler DI, Zollner C, Saugel B. Continuous intra-arterial versus intermittent oscillometric arterial pressure monitoring and hypotension during induction of anaesthesia: the AWAKE randomised trial. Br J Anaesth. 2022;129:478–86. https://doi.org/10.1016/j.bja.2022.06.027.
Naylor AJ, Sessler DI, Maheshwari K, Khanna AK, Yang D, Mascha EJ, Suleiman I, Reville EM, Cote D, Hutcherson MT, Nguyen BM, Elsharkawy H, Kurz A. Arterial Catheters for Early Detection and Treatment of Hypotension during major noncardiac surgery: a Randomized Trial. Anesth Analg. 2020;131:1540–50. https://doi.org/10.1213/ANE.0000000000004370.
Saugel B, Hoppe P, Nicklas JY, Kouz K, Korner A, Hempel JC, Vos JJ, Schon G, Scheeren TWL. Continuous noninvasive pulse wave analysis using finger cuff technologies for arterial blood pressure and cardiac output monitoring in perioperative and intensive care medicine: a systematic review and meta-analysis. Br J Anaesth. 2020;125:25–37. https://doi.org/10.1016/j.bja.2020.03.013.
Vos JJ, Poterman M, Mooyaart EA, Weening M, Struys MM, Scheeren TW, Kalmar AF. Comparison of continuous non-invasive finger arterial pressure monitoring with conventional intermittent automated arm arterial pressure measurement in patients under general anaesthesia. Br J Anaesth. 2014;113:67–74. https://doi.org/10.1093/bja/aeu091.
Martina JR, Westerhof BE, van Goudoever J, de Beaumont EM, Truijen J, Kim YS, Immink RV, Jobsis DA, Hollmann MW, Lahpor JR, de Mol BA, van Lieshout JJ. Noninvasive continuous arterial blood pressure monitoring with Nexfin(R). Anesthesiology. 2012;116:1092–103. https://doi.org/10.1097/ALN.0b013e31824f94ed.
Monnet X, Dres M, Ferre A, Le Teuff G, Jozwiak M, Bleibtreu A, Le Deley MC, Chemla D, Richard C, Teboul JL. Prediction of fluid responsiveness by a continuous non-invasive assessment of arterial pressure in critically ill patients: comparison with four other dynamic indices. Br J Anaesth. 2012;109:330–8. https://doi.org/10.1093/bja/aes182.
Maheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, Cywinski JB, Argalious M, Kurz A, Sessler DI. A randomized trial of continuous noninvasive blood pressure monitoring during noncardiac surgery. Anesth Analg. 2018;127:424–31. https://doi.org/10.1213/ANE.0000000000003482.
Kouz K, Weidemann F, Naebian A, Lohr A, Bergholz A, Thomsen KK, Krause L, Petzoldt M, Moll-Khosrawi P, Sessler DI, Flick M, Saugel B. Continuous finger-cuff versus intermittent oscillometric arterial pressure monitoring and hypotension during induction of anesthesia and noncardiac surgery: the DETECT Randomized Trial. Anesthesiology. 2023;139:298–308. https://doi.org/10.1097/ALN.0000000000004629.
Fortin J, Rogge DE, Fellner C, Flotzinger D, Grond J, Lerche K, Saugel B. A novel art of continuous noninvasive blood pressure measurement. Nat Commun. 2021;12:1387. https://doi.org/10.1038/s41467-021-21271-8.
Baruch MC, Warburton DE, Bredin SS, Cote A, Gerdt DW, Adkins CM. Pulse decomposition analysis of the digital arterial pulse during hemorrhage simulation. Nonlinear Biomed Phys. 2011;5:1. https://doi.org/10.1186/1753-4631-5-1.
Gratz I, Deal E, Spitz F, Baruch M, Allen IE, Seaman JE, Pukenas E, Jean S. Continuous non-invasive finger cuff CareTaker(R) comparable to invasive intra-arterial pressure in patients undergoing major intra-abdominal surgery. BMC Anesthesiol. 2017;17:48. https://doi.org/10.1186/s12871-017-0337-z.
Kwon Y, Stafford PL, Enfield K, Mazimba S, Baruch MC. Continuous noninvasive blood pressure monitoring of Beat-By-Beat blood pressure and heart rate using Caretaker compared with invasive arterial catheter in the Intensive Care Unit. J Cardiothorac Vasc Anesth. 2022;36:2012–21. https://doi.org/10.1053/j.jvca.2021.09.042.
Briegel J, Bahner T, Kreitmeier A, Conter P, Fraccaroli L, Meidert AS, Tholl M, Papadakis G, Deunert A, Bauer A, Hoeft A, Pfeiffer UJ. Clinical evaluation of a high-fidelity Upper Arm Cuff to measure arterial blood pressure during noncardiac surgery. Anesthesiology. 2020;133:997–1006. https://doi.org/10.1097/ALN.0000000000003472.
Saugel B, Kouz K, Sessler DI, Anesthesiology. https://doi.org/10.1097/ALN.0000000000003530.
Conter P, Briegel J, Baehner T, Kreitmeier A, Meidert AS, Tholl M, Schwimmbeck F, Bauer A, Pfeiffer UJ. Noninvasive Assessment of arterial pulse-pressure variation during General Anesthesia: clinical evaluation of a New High-Fidelity Upper Arm Cuff. J Cardiothorac Vasc Anesth. 2023;37:1382–9. https://doi.org/10.1053/j.jvca.2023.03.040.
Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension based on high-fidelity arterial pressure Waveform Analysis. Anesthesiology. 2018;129:663–74. https://doi.org/10.1097/ALN.0000000000002300.
Kouz K, Monge Garcia MI, Cerutti E, Lisanti I, Draisci G, Frassanito L, Sander M, Ali Akbari A, Frey UH, Grundmann CD, Davies SJ, Donati A, Ripolles-Melchor J, Garcia-Lopez D, Vojnar B, Gayat E, Noll E, Bramlage P, Saugel B. Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT). BJA Open. 2023;6:100140. https://doi.org/10.1016/j.bjao.2023.100140.
Kouz K, Scheeren TWL, van den Boom T, Saugel B. Hypotension prediction index software alarms during major noncardiac surgery: a post hoc secondary analysis of the EU-HYPROTECT registry. BJA Open. 2023;8:100232. https://doi.org/10.1016/j.bjao.2023.100232.
Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. Effect of a machine learning-derived early warning system for Intraoperative Hypotension vs Standard Care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial. JAMA. 2020;323:1052–60. https://doi.org/10.1001/jama.2020.0592.
Maheshwari K, Shimada T, Yang D, Khanna S, Cywinski JB, Irefin SA, Ayad S, Turan A, Ruetzler K, Qiu Y, Saha P, Mascha EJ, Sessler DI. Hypotension Prediction Index for Prevention of Hypotension during Moderate- to high-risk noncardiac surgery. Anesthesiology. 2020;133:1214–22. https://doi.org/10.1097/ALN.0000000000003557.
Enevoldsen J, Vistisen ST. Performance of the Hypotension Prediction Index May be overestimated due to Selection Bias. Anesthesiology. 2022;137:283–9. https://doi.org/10.1097/ALN.0000000000004320.
Mulder MP, Harmannij-Markusse M, Donker DW, Fresiello L, Potters JW. Is continuous intraoperative monitoring of Mean arterial pressure as good as the Hypotension Prediction Index Algorithm? Research Letter. Anesthesiology. 2023;138:657–8. https://doi.org/10.1097/ALN.0000000000004541.
Michard F, Biais M, Futier E, Romagnoli S. Mirror, mirror on the wall, who is going to become hypotensive? Eur J Anaesthesiol. 2023;40:72–4. https://doi.org/10.1097/EJA.0000000000001740.
Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013;119:507–15. https://doi.org/10.1097/ALN.0b013e3182a10e26.
Salmasi V, Maheshwari K, Yang D, Mascha EJ, Singh A, Sessler DI, Kurz A. Relationship between intraoperative hypotension, defined by either reduction from baseline or Absolute Thresholds, and Acute kidney and myocardial Injury after noncardiac surgery: a retrospective cohort analysis. Anesthesiology. 2017;126:47–65. https://doi.org/10.1097/ALN.0000000000001432.
Stapelfeldt WH, Yuan H, Dryden JK, Strehl KE, Cywinski JB, Ehrenfeld JM, Bromley P. The SLUScore: a Novel Method for detecting hazardous hypotension in adult patients undergoing Noncardiac Surgical procedures. Anesth Analg. 2017;124:1135–52. https://doi.org/10.1213/ANE.0000000000001797.
Ahuja S, Mascha EJ, Yang D, Maheshwari K, Cohen B, Khanna AK, Ruetzler K, Turan A, Sessler DI. Associations of Intraoperative Radial arterial systolic, Diastolic, Mean, and pulse pressures with myocardial and acute kidney Injury after noncardiac surgery: a retrospective cohort analysis. Anesthesiology. 2020;132:291–306. https://doi.org/10.1097/ALN.0000000000003048.
Mathis MR, Naik BI, Freundlich RE, Shanks AM, Heung M, Kim M, Burns ML, Colquhoun DA, Rangrass G, Janda A, Engoren MC, Saager L, Tremper KK, Kheterpal S, Aziz MF, Coffman T, Durieux ME, Levy WJ, Schonberger RB, Soto R, Wilczak J, Berman MF, Berris J, Biggs DA, Coles P, Craft RM, Cummings KC, Ellis TA 2nd, Fleishut PM, Helsten DL, Jameson LC, van Klei WA, Kooij F, LaGorio J, Lins S, Miller SA, Molina S, Nair B, Paganelli WC, Peterson W, Tom S, Wanderer JP, Wedeven C, Multicenter Perioperative Outcomes Group Investigators. Preoperative risk and the Association between Hypotension and postoperative acute kidney Injury. Anesthesiology. 2020;132:461–75. https://doi.org/10.1097/ALN.0000000000003063.
Gregory A, Stapelfeldt WH, Khanna AK, Smischney NJ, Boero IJ, Chen Q, Stevens M, Shaw AD. Intraoperative hypotension is Associated with adverse clinical outcomes after noncardiac surgery. Anesth Analg. 2021;132:1654–65. https://doi.org/10.1213/ANE.0000000000005250.
Shaw AD, Khanna AK, Smischney NJ, Shenoy AV, Boero IJ, Bershad M, Hwang S, Chen Q, Stapelfeldt WH. Intraoperative hypotension is associated with persistent acute kidney disease after noncardiac surgery: a multicentre cohort study. Br J Anaesth. 2022;129:13–21. https://doi.org/10.1016/j.bja.2022.03.027.
Wesselink EM, Wagemakers SH, van Waes JAR, Wanderer JP, van Klei WA, Kappen TH. Associations between intraoperative hypotension, duration of surgery and postoperative myocardial injury after noncardiac surgery: a retrospective single-centre cohort study. Br J Anaesth. 2022;129:487–96. https://doi.org/10.1016/j.bja.2022.06.034.
Futier E, Lefrant JY, Guinot PG, Godet T, Lorne E, Cuvillon P, Bertran S, Leone M, Pastene B, Piriou V, Molliex S, Albanese J, Julia JM, Tavernier B, Imhoff E, Bazin JE, Constantin JM, Pereira B, Jaber S, INPRESS Study Group. Effect of individualized vs standard blood pressure management strategies on postoperative organ dysfunction among high-risk patients undergoing major surgery: a Randomized Clinical Trial. JAMA. 2017;318:1346–57. https://doi.org/10.1001/jama.2017.14172.
Hu AM, Qiu Y, Zhang P, Zhao R, Li ST, Zhang YX, Zheng ZH, Hu BL, Yang YL, Zhang ZJ. Higher versus lower mean arterial pressure target management in older patients having non-cardiothoracic surgery: a prospective randomized controlled trial. J Clin Anesth. 2021;69:110150. https://doi.org/10.1016/j.jclinane.2020.110150.
Wanner PM, Wulff DU, Djurdjevic M, Korte W, Schnider TW, Filipovic M. Targeting higher intraoperative blood pressures does not reduce adverse Cardiovascular events following noncardiac surgery. J Am Coll Cardiol. 2021;78:1753–64. https://doi.org/10.1016/j.jacc.2021.08.048.
Marcucci M, Painter TW, Conen D, Lomivorotov V, Sessler DI, Chan MTV, Borges FK, Leslie K, Duceppe E, Martinez-Zapata MJ, Wang CY, Xavier D, Ofori SN, Wang MK, Efremov S, Landoni G, Kleinlugtenbelt YV, Szczeklik W, Schmartz D, Garg AX, Short TG, Wittmann M, Meyhoff CS, Amir M, Torres D, Patel A, Ruetzler K, Parlow JL, Tandon V, Fleischmann E, Polanczyk CA, Lamy A, Jayaram R, Astrakov SV, Wu WKK, Cheong CC, Ayad S, Kirov M, de Nadal M, Likhvantsev VV, Paniagua P, Aguado HJ, Maheshwari K, Whitlock RP, McGillion MH, Vincent J, Copland I, Balasubramanian K, Biccard BM, Srinathan S, Ismoilov S, Pettit S, Stillo D, Kurz A, Belley-Cote EP, Spence J, McIntyre WF, Bangdiwala SI, Guyatt G, Yusuf S, Devereaux PJ. POISE-3 Trial investigators and study groups (2023) hypotension-avoidance Versus hypertension-avoidance strategies in noncardiac surgery: an International Randomized Controlled Trial. Ann Intern Med 176:605–14. https://doi.org/10.7326/M22-3157.
Bergholz A, Meidert AS, Flick M, Krause L, Vettorazzi E, Zapf A, Brunkhorst FM, Meybohm P, Zacharowski K, Zarbock A, Sessler DI, Kouz K, Saugel B. Effect of personalized perioperative blood pressure management on postoperative complications and mortality in high-risk patients having major abdominal surgery: protocol for a multicenter randomized trial (IMPROVE-multi). Trials. 2022;23:946. https://doi.org/10.1186/s13063-022-06854-0.
Perel A. Using dynamic variables to Guide Perioperative Fluid Management. Anesthesiology. 2020;133:929–35. https://doi.org/10.1097/ALN.0000000000003408.
Teboul JL, Monnet X, Chemla D, Michard F. Arterial pulse pressure variation with mechanical ventilation. Am J Respir Crit Care Med. 2019;199:22–31. https://doi.org/10.1164/rccm.201801-0088CI.
Michard F. Changes in arterial pressure during mechanical ventilation. Anesthesiology. 2005;103:419–28. https://doi.org/10.1097/00000542-200508000-00026. quiz 449-5.
Morgan BC, Martin WE, Hornbein TF, Crawford EW, Guntheroth WG. Hemodynamic effects of intermittent positive pressure respiration. Anesthesiology. 1966;27:584–90. https://doi.org/10.1097/00000542-196609000-00009.
Rick JJ, Burke SS. Respirator paradox. South Med J. 1978;71:1376–8. https://doi.org/10.1097/00007611-197811000-00018.
Perel A, Pizov R, Cotev S. Systolic blood pressure variation is a sensitive indicator of hypovolemia in ventilated dogs subjected to graded hemorrhage. Anesthesiology. 1987;67:498–502. https://doi.org/10.1097/00000542-198710000-00009.
Pizov R, Eden A, Bystritski D, Kalina E, Tamir A, Gelman S. Arterial and plethysmographic waveform analysis in anesthetized patients with hypovolemia. Anesthesiology. 2010;113:83–91. https://doi.org/10.1097/ALN.0b013e3181da839f.
Michard F, Chemla D, Richard C, Wysocki M, Pinsky MR, Lecarpentier Y, Teboul JL. Clinical use of respiratory changes in arterial pulse pressure to monitor the hemodynamic effects of PEEP. Am J Respir Crit Care Med. 1999;159:935–9. https://doi.org/10.1164/ajrccm.159.3.9805077.
Berkenstadt H, Margalit N, Hadani M, Friedman Z, Segal E, Villa Y, Perel A. Stroke volume variation as a predictor of fluid responsiveness in patients undergoing brain surgery. Anesth Analg. 2001;92:984–9. https://doi.org/10.1097/00000539-200104000-00034.
Flick M, Hoppe P, Matin Mehr J, Briesenick L, Kouz K, Greiwe G, Fortin J, Saugel B. Non-invasive measurement of pulse pressure variation using a finger-cuff method (CNAP system): a validation study in patients having neurosurgery. J Clin Monit Comput. 2022;36:429–36. https://doi.org/10.1007/s10877-021-00669-1.
Marik PE, Cavallazzi R, Vasu T, Hirani A. Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature. Crit Care Med. 2009;37:2642–7. https://doi.org/10.1097/CCM.0b013e3181a590da.
Cecconi M, Parsons AK, Rhodes A. What is a fluid challenge? Curr Opin Crit Care. 2011;17:290–5. https://doi.org/10.1097/MCC.0b013e32834699cd.
Monnet X, Teboul JL. Passive leg raising: five rules, not a drop of fluid! Crit Care. 2015;19:18. https://doi.org/10.1186/s13054-014-0708-5.
Michard F, Chemla D, Teboul JL. Applicability of pulse pressure variation: how many shades of grey? Crit Care. 2015;19:144. https://doi.org/10.1186/s13054-015-0869-x.
Yang X, Du B. Does pulse pressure variation predict fluid responsiveness in critically ill patients? A systematic review and meta-analysis. Crit Care. 2014;18:650. https://doi.org/10.1186/s13054-014-0650-6.
Myatra SN, Monnet X, Teboul J-L. Use of ‘tidal volume challenge’ to improve the reliability of pulse pressure variation. Crit Care. 2017;21:60. https://doi.org/10.1186/s13054-017-1637-x.
Gavelli F, Teboul J-L, Monnet X. The end-expiratory occlusion test: please, let me hold your breath! Crit Care. 2019;23:274. https://doi.org/10.1186/s13054-019-2554-y.
Messina A, Pelaia C, Bruni A, Garofalo E, Bonicolini E, Longhini F, Dellara E, Saderi L, Romagnoli S, Sotgiu G, Cecconi M, Navalesi P. Fluid challenge during anesthesia: a systematic review and Meta-analysis. Anesth Analg. 2018;127:1353–64. https://doi.org/10.1213/ANE.0000000000003834.
Cannesson M, Le Manach Y, Hofer CK, Goarin JP, Lehot JJ, Vallet B, Tavernier B. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a gray zone approach. Anesthesiology. 2011;115:231–41. https://doi.org/10.1097/ALN.0b013e318225b80a.
Jessen MK, Vallentin MF, Holmberg MJ, Bolther M, Hansen FB, Holst JM, Magnussen A, Hansen NS, Johannsen CM, Enevoldsen J, Jensen TH, Roessler LL, Lind PC, Klitholm MP, Eggertsen MA, Caap P, Boye C, Dabrowski KM, Vormfenne L, Hoybye M, Henriksen J, Karlsson CM, Balleby IR, Rasmussen MS, Paelestik K, Granfeldt A, Andersen LW. Goal-directed haemodynamic therapy during general anaesthesia for noncardiac surgery: a systematic review and meta-analysis. Br J Anaesth. 2022;128:416–33. https://doi.org/10.1016/j.bja.2021.10.046.
Benes J, Giglio M, Brienza N, Michard F. The effects of goal directed fluid therapy based on dynamic parameters on post-surgical outcome: a meta-analysis of randomized controlled trials. Crit Care. 2014;18:584. https://doi.org/10.1186/s13054-014-0584-z.
Deng QW, Tan WC, Zhao BC, Wen SH, Shen JT, Xu M. Is goal-directed fluid therapy based on dynamic variables alone sufficient to improve clinical outcomes among patients undergoing surgery? A meta-analysis. Crit Care. 2018;22:298. https://doi.org/10.1186/s13054-018-2251-2.
Kalso E. A short history of central venous catheterization. Acta Anaesthesiol Scand Suppl. 1985;81:7–10. https://doi.org/10.1111/j.1399-6576.1985.tb02313.x.
Forssmann W. Die Sondierung Des Rechten Herzens. Klinische Wochenschrift. 1929;8:2085–7. https://doi.org/10.1007/bf01875120.
Ryan GM, Howland WS. An evaluation of central venous pressure monitoring. Anesth Analg. 1966;45:754–9.
Magder S. Understanding central venous pressure: not a preload index? Curr Opin Crit Care. 2015;21:369–75. https://doi.org/10.1097/MCC.0000000000000238.
Osman D, Ridel C, Ray P, Monnet X, Anguel N, Richard C, Teboul JL. Cardiac filling pressures are not appropriate to predict hemodynamic response to volume challenge. Crit Care Med. 2007;35:64–8. https://doi.org/10.1097/01.CCM.0000249851.94101.4F.
Gelman S. Venous function and central venous pressure: a physiologic story. Anesthesiology. 2008;108:735–48. https://doi.org/10.1097/ALN.0b013e3181672607.
Figg KK, Nemergut EC. Error in central venous pressure measurement. Anesth Analg. 2009;108:1209–11. https://doi.org/10.1213/ane.0b013e318196482c.
Sathish N, Singh NG, Nagaraja PS, Sarala BM, Prabhushankar CG, Dhananjaya M, Manjunatha N. Comparison between noninvasive measurement of central venous pressure using near infrared spectroscopy with an invasive central venous pressure monitoring in cardiac surgical Intensive Care Unit. Ann Card Anaesth. 2016;19:405–9. https://doi.org/10.4103/0971-9784.185520.
Pellicori P, Clark AL, Kallvikbacka-Bennett A, Zhang J, Urbinati A, Monzo L, Dierckx R, Anker SD, Cleland JGF. Non-invasive measurement of right atrial pressure by near-infrared spectroscopy: preliminary experience. A report from the SICA-HF study. Eur J Heart Fail. 2017;19:883–92. https://doi.org/10.1002/ejhf.825.
Tugrul M, Camci E, Pembeci K, Al-Darsani A, Telci L. Relationship between peripheral and central venous pressures in different patient positions, catheter sizes, and insertion sites. J Cardiothorac Vasc Anesth. 2004;18:446–50. https://doi.org/10.1053/j.jvca.2004.05.022.
Hoftman N, Braunfeld M, Hoftman G, Mahajan A. Peripheral venous pressure as a predictor of central venous pressure during orthotopic liver transplantation. J Clin Anesth. 2006;18:251–5. https://doi.org/10.1016/j.jclinane.2005.09.031.
Hadimioglu N, Ertug Z, Yegin A, Sanli S, Gurkan A, Demirbas A. Correlation of peripheral venous pressure and central venous pressure in kidney recipients. Transpl Proc. 2006;38:440–2. https://doi.org/10.1016/j.transproceed.2005.12.057.
Sherif L, Joshi VS, Ollapally A, Jain P, Shetty K, Ribeiro KS. Peripheral venous pressure as a reliable predictor for monitoring central venous pressure in patients with burns. Indian J Crit Care Med. 2015;19:199–202. https://doi.org/10.4103/0972-5229.154548.
Saugel B, Annecke T, Bein B, Flick M, Goepfert M, Gruenewald M, Habicher M, Jungwirth B, Koch T, Kouz K, Meidert AS, Pestel G, Renner J, Sakka SG, Sander M, Treskatsch S, Zitzmann A, Reuter DA. Intraoperative haemodynamic monitoring and management of adults having non-cardiac surgery: guidelines of the German society of Anaesthesiology and Intensive Care Medicine in collaboration with the German Association of the Scientific Medical Societies. J Clin Monit Comput. 2024. https://doi.org/10.1007/s10877-024-01132-7.
Stewart GN. Researches on the circulation time and on the influences which affect it. J Physiol. 1897;22:159–83. https://doi.org/10.1113/jphysiol.1897.sp000684.
Fegler G. Measurement of cardiac output in anaesthetized animals by a thermodilution method. Q J Exp Physiol Cogn Med Sci. 1954;39:153–64. https://doi.org/10.1113/expphysiol.1954.sp001067.
Frank O. Die Grundform Des Arteriellen Pulses. Z Biol. 1899;37:483–526.
Reuter DA, Huang C, Edrich T, Shernan SK, Eltzschig HK. Cardiac output monitoring using indicator-dilution techniques: basics, limits, and perspectives. Anesth Analg. 2010;110:799–811. https://doi.org/10.1213/ANE.0b013e3181cc885a.
Saugel B, Kouz K, Scheeren TWL, Greiwe G, Hoppe P, Romagnoli S, de Backer D. Cardiac output estimation using pulse wave analysis-physiology, algorithms, and technologies: a narrative review. Br J Anaesth. 2021;126:67–76. https://doi.org/10.1016/j.bja.2020.09.049.
Kouz K, Scheeren TWL, de Backer D, Saugel B. Pulse Wave Analysis to Estimate Cardiac output. Anesthesiology. 2021;134:119–26. https://doi.org/10.1097/ALN.0000000000003553.
Thomsen KK, Kouz K, Saugel B. Pulse wave analysis: basic concepts and clinical application in intensive care medicine. Curr Opin Crit Care. 2023;29:215–22. https://doi.org/10.1097/MCC.0000000000001039.
Teboul JL, Saugel B, Cecconi M, De Backer D, Hofer CK, Monnet X, Perel A, Pinsky MR, Reuter DA, Rhodes A, Squara P, Vincent JL, Scheeren TW. Less invasive hemodynamic monitoring in critically ill patients. Intensive Care Med. 2016;42:1350–9. https://doi.org/10.1007/s00134-016-4375-7.
Saugel B, Vincent JL. Cardiac output monitoring: how to choose the optimal method for the individual patient. Curr Opin Crit Care. 2018;24:165–72. https://doi.org/10.1097/mcc.0000000000000492.
De Backer D, Bakker J, Cecconi M, Hajjar L, Liu DW, Lobo S, Monnet X, Morelli A, Myatra SN, Perel A, Pinsky MR, Saugel B, Teboul JL, Vieillard-Baron A, Vincent JL. Alternatives to the Swan-Ganz catheter. Intensive Care Med. 2018;44:730–41. https://doi.org/10.1007/s00134-018-5187-8.
Saugel B, Thiele RH, Hapfelmeier A, Cannesson M. Technological Assessment and objective evaluation of minimally invasive and noninvasive cardiac output Monitoring systems. Anesthesiology. 2020;133:921–8. https://doi.org/10.1097/ALN.0000000000003483.
Ahmad T, Beilstein CM, Aldecoa C, Moreno RP, Molnar Z, Novak-Jankovic V, Hofer CK, Sander M, Rhodes A, Pearse RM. Variation in haemodynamic monitoring for major surgery in European nations: secondary analysis of the EuSOS dataset. Perioper Med (Lond). 2015;4:8. https://doi.org/10.1186/s13741-015-0018-8.
Slagt C, Malagon I, Groeneveld AB. Systematic review of uncalibrated arterial pressure waveform analysis to determine cardiac output and stroke volume variation. Br J Anaesth. 2014;112:626–37. https://doi.org/10.1093/bja/aet429.
Foti L, Michard F, Villa G, Ricci Z, Romagnoli S. The impact of arterial pressure waveform underdamping and resonance filters on cardiac output measurements with pulse wave analysis. Br J Anaesth. 2022;129:e6–8. https://doi.org/10.1016/j.bja.2022.03.024.
Singer M. Oesophageal Doppler. Curr Opin Crit Care. 2009;15:244–8. https://doi.org/10.1097/MCC.0b013e32832b7083.
Monnet X, Chemla D, Osman D, Anguel N, Richard C, Pinsky MR, Teboul JL. Measuring aortic diameter improves accuracy of esophageal doppler in assessing fluid responsiveness. Crit Care Med. 2007;35:477–82. https://doi.org/10.1097/01.Ccm.0000254725.35802.17.
Thiele RH, Bartels K, Gan TJ. Cardiac output monitoring: a contemporary assessment and review. Crit Care Med. 2015;43:177–85. https://doi.org/10.1097/CCM.0000000000000608.
Saugel B, Cecconi M, Wagner JY, Reuter DA. Noninvasive continuous cardiac output monitoring in perioperative and intensive care medicine. Br J Anaesth. 2015;114:562–75. https://doi.org/10.1093/bja/aeu447.
Saugel B, Cecconi M, Hajjar LA. Noninvasive cardiac output monitoring in cardiothoracic surgery patients: available methods and future directions. J Cardiothorac Vasc Anesth. 2019;33:1742–52. https://doi.org/10.1053/j.jvca.2018.06.012.
Guyton AC. Regulation of cardiac output. Anesthesiology. 1968;29:314–26.
Eyeington CT, Ancona P, Cioccari L, Luethi N, Glassford NJ, Eastwood GM, Proimos HK, Franceschi F, Chan MJ, Jones D, Bellomo R. Non-invasive estimation of cardiac index in healthy volunteers. Anaesth Intensive Care. 2018;46:290–6. https://doi.org/10.1177/0310057X1804600306.
Kouz K, Bergholz A, Timmermann LM, Brockmann L, Flick M, Hoppe P, Briesenick L, Schulte-Uentrop L, Krause L, Maheshwari K, Sessler DI, Saugel B. The Relation between Mean arterial pressure and Cardiac Index in Major abdominal surgery patients: a prospective Observational Cohort Study. Anesth Analg. 2022;134:322–9. https://doi.org/10.1213/ANE.0000000000005805.
Saugel B, Thomsen KK, Maheshwari K. Goal-directed haemodynamic therapy: an imprecise umbrella term to avoid. Br J Anaesth. 2023;130:390–3. https://doi.org/10.1016/j.bja.2022.12.022.
Shoemaker WC. Editorial: protocol medicine. Crit Care Med. 1974;2:279. https://doi.org/10.1097/00003246-197409000-00010.
Kaufmann T, Clement RP, Scheeren TWL, Saugel B, Keus F, van der Horst ICC. Perioperative goal-directed therapy: a systematic review without meta-analysis. Acta Anaesthesiol Scand. 2018;62:1340–55. https://doi.org/10.1111/aas.13212.
Saugel B, Kouz K, Scheeren TWL. The ‘5 ts’ of perioperative goal-directed haemodynamic therapy. Br J Anaesth. 2019;123:103–7. https://doi.org/10.1016/j.bja.2019.04.048.
Hamilton MA, Cecconi M, Rhodes A. A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients. Anesth Analg. 2011;112:1392–402. https://doi.org/10.1213/ANE.0b013e3181eeaae5.
Cecconi M, Corredor C, Arulkumaran N, Abuella G, Ball J, Grounds RM, Hamilton M, Rhodes A. Clinical review: goal-directed therapy-what is the evidence in surgical patients? The effect on different risk groups. Crit Care. 2013;17:209. https://doi.org/10.1186/cc11823.
Pearse RM, Harrison DA, MacDonald N, Gillies MA, Blunt M, Ackland G, Grocott MP, Ahern A, Griggs K, Scott R, Hinds C, Rowan K, OPTIMISE Study Group. Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: a randomized clinical trial and systematic review. JAMA. 2014;311:2181–90. https://doi.org/10.1001/jama.2014.5305.
Ripolles-Melchor J, Espinosa A, Martinez-Hurtado E, Abad-Gurumeta A, Casans-Frances R, Fernandez-Perez C, Lopez-Timoneda F, Calvo-Vecino JM. Perioperative goal-directed hemodynamic therapy in noncardiac surgery: a systematic review and meta-analysis. J Clin Anesth. 2016;28:105–15. https://doi.org/10.1016/j.jclinane.2015.08.004.
Chong MA, Wang Y, Berbenetz NM, McConachie I. Does goal-directed haemodynamic and fluid therapy improve peri-operative outcomes? A systematic review and meta-analysis. Eur J Anaesthesiol. 2018;35:469–83. https://doi.org/10.1097/EJA.0000000000000778.
Edwards MR, Forbes G, MacDonald N, Berdunov V, Mihaylova B, Dias P, Thomson A, Grocott MP, Mythen MG, Gillies MA, Sander M, Phan TD, Evered L, Wijeysundera DN, McCluskey SA, Aldecoa C, Ripolles-Melchor J, Hofer CK, Abukhudair H, Szczeklik W, Grigoras I, Hajjar LA, Kahan BC, Pearse RM. Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial: study protocol for a multicentre international trial of cardiac output-guided fluid therapy with low-dose inotrope infusion compared with usual care in patients undergoing major elective gastrointestinal surgery. BMJ Open. 2019;9:e023455. https://doi.org/10.1136/bmjopen-2018-023455.
Michard F, Futier E, Desebbe O, Biais M, Guinot PG, Leone M, Licker MJ, Molliex S, Pirracchio R, Provenchere S, Schoettker P, Zieleskiewicz L. Pulse contour techniques for perioperative hemodynamic monitoring: a nationwide carbon footprint and cost estimation. Anaesth Crit Care Pain Med. 2023;42:101239. https://doi.org/10.1016/j.accpm.2023.101239.
Cannesson M, Pestel G, Ricks C, Hoeft A, Perel A. Hemodynamic monitoring and management in patients undergoing high risk surgery: a survey among north American and European anesthesiologists. Crit Care. 2011;15:R197. https://doi.org/10.1186/cc10364.
Flick M, Joosten A, Scheeren TWL, Duranteau J, Saugel B. Haemodynamic monitoring and management in patients having noncardiac surgery. EJAIC. 2023;2:pe0017. https://doi.org/10.1097/ea9.0000000000000017.
Buhre W, De Robertis E, Gonzalez-Pizarro P. The Glasgow declaration on sustainability in Anaesthesiology and Intensive Care. Eur J Anaesthesiol. 2023;40:461–4. https://doi.org/10.1097/EJA.0000000000001862.
Acknowledgements
Not applicable.
Funding
Open Access funding enabled and organized by Projekt DEAL. This work was supported solely from institutional and/or departmental sources.
Open Access funding enabled and organized by Projekt DEAL.
Author information
Authors and Affiliations
Contributions
Conception of the review: KK, BS. Literature search, writing of the manuscript: all authors. Final approval of the version to be published: all authors. Agreement to be accountable for all aspects of the work thereby ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: all authors.
Corresponding author
Ethics declarations
Conflict of interest
KK is a consultant for and has received honoraria for giving lectures from Edwards Lifesciences (Irvine, CA, USA). KK is a consultant for Vygon (Aachen, Germany). RT performs consulting work on advanced monitoring technology for Medtronic, Edwards Lifesciences, and Philips Medical. RT received an investigator-initiated award from Apple to explore the use of exercise training in patients undergoing cancer surgery. RT has an NIH Grant (R-21, NIBIB) to develop near-infrared spectroscopy equipment to measure the oxidation state of cytochrome aa non-invasively. FM is the founder and managing director of MiCo (MichardConsulting.com), a consulting and research firm based in Switzerland. MiCo does not sell any medical devices, and FM does not own shares and does not receive patent royalties from any medical device company. BS is a consultant for and has received institutional restricted research grants and honoraria for giving lectures from Edwards Lifesciences (Irvine, CA, USA). BS is a consultant for Philips North America (Cambridge, MA, USA) and has received honoraria for giving lectures from Philips Medizin Systeme Böblingen (Böblingen, Germany). BS has received institutional restricted research grants and honoraria for giving lectures from Baxter (Deerfield, IL, USA). BS is a consultant for and has received institutional restricted research grants and honoraria for giving lectures from GE Healthcare (Chicago, IL, USA). BS has received institutional restricted research grants and honoraria for giving lectures from CNSystems Medizintechnik (Graz, Austria). BS is a consultant for Maquet Critical Care (Solna, Sweden). BS has received honoraria for giving lectures from Getinge (Gothenburg, Sweden). BS is a consultant for and has received institutional restricted research grants and honoraria for giving lectures from Pulsion Medical Systems (Feldkirchen, Germany). BS is a consultant for and has received institutional restricted research grants and honoraria for giving lectures from Vygon (Aachen, Germany). BS is a consultant for and has received institutional restricted research grants from Retia Medical (Valhalla, NY, USA). BS has received honoraria for giving lectures from Masimo (Neuchâtel, Switzerland). BS is a consultant for Dynocardia (Cambridge, MA, USA). BS has received institutional restricted research grants from Osypka Medical (Berlin, Germany). BS was a consultant for and has received institutional restricted research grants from Tensys Medical (San Diego, CA, USA). BS is an Editor of the British Journal of Anaesthesia.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kouz, K., Thiele, R., Michard, F. et al. Haemodynamic monitoring during noncardiac surgery: past, present, and future. J Clin Monit Comput 38, 565–580 (2024). https://doi.org/10.1007/s10877-024-01161-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10877-024-01161-2