Pulse oximetry—one of the most used monitoring technologies in medicine—consists of an optical spectrometer1 and plethysmograph.2 Most publications on pulse oximetry in the fields of anesthesia, critical care, emergency, and respiratory medicine mainly describe its function as a spectrometer for the measurement of arterial hemoglobin saturation (Spo2).3–6 The noninvasive and real-time monitoring of Spo2 is one of the most important breakthroughs in medicine.7 Its reliability and accuracy to reflect real arterial hemoglobin saturation (Sao2) has been solidly validated.1,8–10 This technology allows early diagnosis and treatment of hypoxemia, substantially reducing the incidence and severity of hypoxemic episodes by a factor of 1.5 to 3 when compared with patients without oximeters.11–13 Thus, pulse oximetry has become the standard of care for monitoring patients under mechanical ventilation.14
Beyond this important function, there are numerous reports highlighting the use of pulse oximeters as a plethysmograph.2,15–21 Plethysmographic analysis of the peripheral pulse wave provides relevant hemodynamic information that has been used for the diagnosis and follow-up of chronic cardiovascular diseases. However, this optical feature of oximeters has not been developed further to allow for a real-time monitoring of acute hemodynamic events in critically ill patients.
Nowadays, clinicians are facing an overwhelming amount of readily available information, and, therefore, it is not surprising that many interesting aspects of pulse oximetry remain hidden below the detection level of routine clinical management. This is also, in part, due to the fact that most monitoring devices focus on, and thus only display, information related to oxygen saturation. We believe that the 2 basic functionalities of pulse oximetry, spectrometry and plethysmography, are largely underused in critical care practice. The intention of this special article is to introduce the clinical potential of the wide range of optical features of pulse oximetry and to discuss further uses for the advanced monitoring of patients under mechanical ventilation.
PULSE OXIMETERS AS SPECTROMETER
Pulse oximetry is particularly useful during mechanical ventilation, because its main objective is to support and ensure the maintenance of an optimal gas exchange.3–6 The principal use of pulse oximetry in mechanically ventilated patients is the detection of hypoxemia—that is, a decrease in the arterial oxygen content—because hemoglobin is the principal carrier of oxygen in blood. Oximeters do not measure Pao2, and for this reason, they can diagnose directly neither arterial hypoxia nor hyperoxia, defined as a partial pressure of oxygen in the arterial blood (Pao2) <60 mm Hg or >120 mm Hg, respectively.22 What oximeters essentially do is “estimate” arterial hypoxia any time Spo2 falls <90% based on the oxyhemoglobin dissociation curve, the well-described and predictable relationship between hemoglobin saturation and Pao2.
Improving the Monitoring of Gas Exchange Abnormalities with Pulse Oximetry
A known limitation of the dissociation curve is revealed any time supplemental oxygen is administered to a patient, as fully saturated hemoglobin cannot detect arterial hyperoxia due to the flattening of the upper part of the curve. This is important because the presence of hyperoxia by no means implies that gas exchange is normal. Here, we provide a simple example: an acute respiratory distress syndrome patient with an Spo2 of 97% and a Pao2 of 190 mm Hg ventilated with pure oxygen presents with arterial hyperoxia while suffering from a significant impairment of gas exchange if Pao2/Fio2 is taken into account. Therefore, the oxyhemoglobin curve and the Spo2 values lose sensitivity and specificity for monitoring gas exchange during oxygen therapy. Ignoring this important fact can lead to gross misinterpretations of the actual status of gas exchange.
Luckily there are ways to overcome the masking effect of oxygen therapy on Spo2. One ingenious approach has been described by Sapsford and Jones23 who proposed the use of a Spo2/Fio2 diagram. This method noninvasively determines an individual’s oxyhemoglobin curve using Spo2 instead of Sao2 and replacing Pao2 by Fio2 (Figure 1). First, a decremental Fio2 titration is performed from 100% to 21%, in 10% steps, marking at each Fio2 level the corresponding Spo2 reading with a dot in the Spo2/Fio2 chart. The line connecting these dots creates the modified patient-specific “oxyhemoglobin curve” (Figure 1A, red line). Thereafter, this individual curve is compared with the theoretical normal one (Figure 1A, black line). The smart twist introduced by this concept temporarily suppresses the effect of supplemental oxygen to discover the real arterial oxygenation. (Figure 1)
These authors described this method as a tool for distinguishing shunting from low ventilation/perfusion ratio (V/Q) areas.23,24 Any time the curve is shifted downward compared with the normal reference, a shunt condition is present, whereas any right-hand displacement indicates a low V/Q problem (Figure 1A). However, as expected, most mechanically ventilated patients show both gas exchange defects at the same time: shunt induced by atelectasis and low V/Q zones created by small airway closure.25–28
The Fio2/Spo2 diagram has been tested and validated in several publications. Sapsford and Jones23 found a good fit between the diagrams constructed in volunteers and in patients with the results obtained by mathematically modeling gas exchange. Later, the same group successfully tested the Fio2/Spo2 diagram in different patient populations and clinical scenarios.24,29,30 We emphasize that this method provides an early, fast, and readable available bedside estimate of shunt and low V/Q.
It should be noted that this concept can be used for monitoring ventilatory treatments. For example, in a mechanically ventilated patient with Spo2 of 92% breathing room air, the effect of a particular change in the ventilator settings on arterial oxygenation can be evaluated within a few breaths. If this new ventilatory pattern results in a positive clinical effect on arterial oxygenation, Spo2 must increase despite the low Fio2.
Recently, we used the same principle to assess the effect of a lung recruitment maneuver in 20 anesthetized morbidly obese patients.31Figure 1B shows the Spo2/Fio2 diagram obtained in those patients performed under general anesthesia. Standard lung-protective ventilation resulted in an estimated shunt of about 25% as described by such a diagram (green dots). Later on, after lung recruitment and optimal positive end-expiratory pressure (PEEP), the Spo2/Fio2 diagram “normalized” recovering normal shunt values (blue dots).
In summary, the Spo2/Fio2 diagram allows a simple, dynamic, and noninvasive characterization of the status of gas exchange that is surely more complete than a “static photograph” given by single Spo2, Sao2, or Pao2 readings obtained at constant Fio2. Figure 2 summarizes the oxygenation conditions according to the Spo2 and the applied Fio2, taking an Spo2 ≥97% as the normal theoretical value when breathing air.23,24,31 This method provides a simple estimate of shunt, requiring neither arterial blood samples nor a pulmonary artery catheter. Unfortunately, this tool has not been adopted in operating rooms and intensive care units despite its intriguing simplicity and clinical relevance (Figure 2).
Characterizing the Status of Gas Exchange During Mechanical Ventilation
The flow chart in Figure 3 proposes a diagnostic sequence to be used before taking the decision to start invasive or noninvasive mechanical ventilation. This guide is based on the principles suggested by Sapsford and Jones23 and consists of the following 3 simple steps (Figure 3).
First, baseline Spo2 during spontaneous breathing of air in the supine position is determined. This step will assess the patient’s oxygenation status according to Figure 2. This is an important step because the aging process, smoking, obesity, or pulmonary diseases will all lower baseline Spo2 and can confound clinicians during and after ventilator treatment. For example, in an elderly patient breathing air, an Spo2 of 93% could erroneously be considered abnormal after surgery if the anesthesiologist omitted to take a reference value, which, in this case, would have been 93%. In this respect, a preoperative baseline Spo2 ≤96% breathing air in the supine position has recently been shown to be a strong predictor of an increased risk to develop postoperative pulmonary complications.32
Second, the Spo2 response to an increase in Fio2 to 1 immediately after intubation also provides relevant information. The question then becomes as follows: Is the patient’s hemoglobin able to saturate to its maximum or not? If full saturation is not reached at Fio2 1, an oxygenation problem should be suspected, as shown in Figures 1 and 2.
Third, the reduction of Fio2 from pure oxygen to room air either stepwise following the Spo2/Fio2 diagram or abruptly in 1 step (“air test”) while observing the effect on Spo2 will identify patients with a shunt problem. This can help decide which specific treatments such as PEEP, recruitment maneuvers, bronchodilators, etc, to apply. Finally, any time, clinicians suspect a deterioration of gas exchange during the course of a ventilatory treatment, and the decremental Fio2 maneuver can be repeated for diagnostic and clinical decision-making purposes.
ADVANCED USES OF THE PULSE OXIMETER AS A PLETHYSMOGRAPH
The origin and meaning of the photoplethysmography (PPG) waveform has not been well understood for many decades. The ventricular-vascular interaction has provided the theoretical basis for a better understanding and interpretation of the PPG waveform.33–35 It represents the volume of blood versus time curve measured in a tissue during 1 cardiac cycle.36–41 This flow wave has a systolic-forward and a diastolic-backward component, similar to pulse pressure and Doppler waveforms33–35,40–42 (Figure 4). Changes in aortic wall elasticity and vascular tone alter PPG, arterial pulse pressure, and Doppler waveform morphology in a predictable and similar manner.33,40–42 These facts support the notion that all waves represent the same vascular phenomenon, confirming that PPG has the potential to characterize the status of the vascular system in a simple and noninvasive manner (Figure 4).
Role of PPG for Monitoring Mechanical Ventilation
Most of the areas of research around PPG have dealt with the prevention, diagnosis, follow-up, and ambulatory treatment of chronic cardiovascular diseases.17–21 Although clinicians involved in the care of mechanically ventilated patients use oximeters all the time, the clinical application of PPG for hemodynamic monitoring is only minimally used, if at all.
The link between PPG and mechanical ventilation has its foundation in the physiologic principles governing cardiopulmonary interactions. Hemodynamic impairment during mechanical ventilation is most frequently related to the volemic status and to fast changes in vascular tone. Hypo- and hypervolemia as well as hypotension due to prolonged vasodilation are associated with high morbidity and mortality.43–45 Therefore, it is of great clinical relevance to be able to determine the volemia state and vascular tone of the patient to choose the right therapeutic intervention. However, this has been a difficult undertaking at the bedside of mechanically ventilated patients, even with standard invasive monitoring methods. Due to its noninvasiveness, PPG may play a potential role as first-line monitoring of these 2 important hemodynamic parameters. The reader is asked to follow the explanations and descriptions given in Tables 1 and 2 while reading through the next 2 sections.
PPG for Monitoring Preload Dependency
The assessment of fluid responsiveness (ie, the identification of preload-dependent patients in whom IV fluids will increase cardiac index) constitutes the basis of many goal-directed fluid therapy protocols aimed at both optimizing volemia and avoiding fluid overload in mechanically ventilated patients. Table 1 shows the PPG-derived parameters related to the assessment of preload dependency based on the changes in the morphology of the PPG waveform.46
As PPG shares the same physiologic principles with arterial pulse pressure, it has been suggested that variations in the PPG waveform during mechanical breaths correspond to the delta up and down, as described for the pulse pressure waveform. Partridge47 was the first to describe in 1987 shifts in PPG baseline in synchrony with breathing. Murray and Foster48 described the same phenomenon. Shamir et al49 studied the role of PPG waveform variations in anesthetized patients after removing 10% of blood volume and after replacing it by colloids. The authors found a good correlation between changes in PPG and arterial systolic pulse pressure variation (r = 0.85; P = 0.0009). Natalini et al50 showed that the ventilation-induced pulse variations of both arterial and PPG waveforms were similar. Taking these data into account, a PPG variation of 9% was the threshold value for predicting fluid responsiveness, which corresponded to a pulse pressure variation of >13% (sensitivity 100%, specificity 75%, and area under the receiver operating characteristic curve 0.90). Cannesson et al51 also obtained a good correlation and agreement between the arterial pulse pressure variation and the PPG signals in mechanically ventilated patients (r2 = 0.83, P < 0.001). The same authors demonstrated that a change of >13% in the plethysmographic waveform amplitude can predict fluid responsiveness with a sensitivity of 80% and a specificity of 90% during general anesthesia.52 This group also validated the plethysmographic variability index for clinical use53,54 (Table 1).
Another parameter related to the diagnosis of hypovolemia is the left ventricular ejection time (LVET) or the time between the onset of the systolic upstroke and the dicrotic notch. LVET measured by PPG was highly correlated with the one determined by Doppler from the aortic flow (r = 0.89, P < 0.05).55 Using PPG to calculate LVET, Geeraerts et al56 demonstrated that LVET decreased proportionally to a simulated hypovolemic condition in healthy volunteers. They found that central LVET measured by a strain-gauge transducer in the carotid artery was similar to the LVET measured by PPG in the periphery. Middleton et al57 obtained similar results in an elegant model of hypovolemia in volunteers donating blood. Shortening of PPG-derived LVET and prolongation of pulse transit time were observed in 81% and 91% of 43 subjects, respectively.
The preejection period, that is, the time interval between the R wave on the electrocardiogram (ECG) and the beginning of the upstroke of the radial artery pulse pressure, has been shown to be another useful parameter for predicting fluid responsiveness in mechanically ventilated patients.58 Feissel et al59 demonstrated, in septic ventilated patients, a good correlation between the variation in the preejection period measured by PPG and cardiac index after a fluid challenge (r2 = 0.70, P < 0.001). This parameter accurately identified responders defined as a change in cardiac index >15% (area under the curve 0.94) (Table 1).
PPG for Monitoring Left Ventricle Impedance
Pulse wave velocity and vascular tone are factors greatly affecting left ventricular outflow impedance,33 which changes dynamically during the course of ventilatory treatment. Any time the vascular tree becomes stiffer due to vasoconstriction, the faster pulse wave returns to the LV early in systole, thereby increasing outflow impedance. The opposite situation is observed during vasodilation.20 Such interaction between the left ventricle and the vasculature can be monitored by PPG-derived parameters as described in Table 2.
On the one hand, pulse wave velocity is represented by the time between peak-to-peak forward and backward waves that can be clearly determined by the PPG-derived stiffness index60,61 (Figure 4). The stiffness index depends on the presence and detection of the dicrotic notch, which is not well defined or even absent in some patients. In those latter cases, the dicrotic notch may yet be identifiable in the first and second derivatives of the PPG wave. Although this index was initially created for the study of the aging process,60–62 we believe that this concept can be easily applied also in ventilated patients for the beat-by-beat evaluation of LV impedance. The same is true for the indices derived from the second derivative such as the aging index described by Takazawa et al63 and Imanaga et al.64
On the other hand, the PPG contour provides valuable online qualitative information about the vascular tone, which, in clinical practice, is rarely assessed simply by observing the PPG amplitude.48 Vasodilation increases the amplitude of the PPG, as observed during hyperthermia or vasodilator infusion, whereas vasoconstriction decreases PPG amplitude as described during tracheal intubation, painful stimuli, or hypothermia.65–68 Other features of the PPG waveform have also been related to the changes in vascular impedance. Awad et al69 found that the PPG width was directly proportional to systemic vascular resistance and its best indicator among other PPG features in anesthetized patients. Lee et al,70 using a multivariate approach instead of a single PPG-dependent variable, could accurately discriminate a low systemic vascular resistance with a sensitivity of 85% and specificity of 86%.
There are PPG-derived indices, such as the reflection index and the peripheral perfusion index, that are indirectly related to the vascular impedance. The reflection index represents the degree of wave reflection based on the ratio of the heights of the backward and the forward wave. The perfusion index is calculated from the pulsatile (alternant) and nonpulsatile (continuous) components of light absorption. The clinical value of these indices has been clearly demonstrated in different kinds of patients and clinical scenarios71–74 (Table 2).
The position of the “dicrotic notch,” or the incision between forward and backward waves, has been extensively studied in hypertension, diabetes mellitus, and atherosclerosis,75–77 and it has been established that, with vasoconstriction, the notch position moves toward the left into the systolic wave. Based on this, Dawber et al77 classified the PPG into 4 categories for the diagnosis of arterial hypertension. However, this classification is incomplete because it includes only vasoconstriction but not vasodilation. As can be expected, arterial vasodilation also has a clear effect on the PPG contour, which is characterized by a decrease in the height of the backward wave.65,78,79
Because of the predictable nature of its changes, PPG contour analysis could be used to describe and classify the changes in vascular impedance in mechanically ventilated patients. Figure 5 presents a proposal for such a classification that includes all the described features of PPG that have been used to assess vascular impedance. Such classification, based on the PPG shape, will allow clinicians to perform a real-time and noninvasive diagnosis, treatment, and assessment of vasoconstriction and vasodilation in mechanically ventilated patients at the bedside. Even though many publications strongly support the principles on which this proposed classification is based, its definitive interpretation and validity must be tested and confirmed in future studies (Figure 5).
In summary, PPG contour analysis provides useful, real-time, and noninvasive information about cardiopulmonary interactions during mechanical ventilation. Its monitoring capabilities are beyond the known concept of preload dependency. Particularly, the assessment of the vascular impedance is clinically relevant because this information remains hidden to most clinicians, thereby limiting diagnostic options and treatment decisions. For example, changes in vascular tone decrease both the sensitivity and specificity of pulse pressure variation to reliably diagnose preload dependency.80–82 This means that vasoconstriction and vasodilation could falsely suggest a normovolemic state when the patient is in fact hypovolemic and a hypovolemic state when the patient is in fact normovolemic. Vasodilation is a known and common finding in mechanically ventilated patients,83 and a failure to recognize this condition subjects patients to the known negative consequences of an iatrogenic positive fluid balance.
Limitations and Drawbacks of Pulse Oximetry
In general, pulse oximeters are robust, safe, accurate, reliable, and easy to operate and require no calibration. However, clinicians must be aware about the limitations of this technology and the common drawbacks of most noninvasive measurements. The performance of the advanced pulse oximetry–derived functions described in this article will depend not only on patient-related factors (such as local temperature and blood flow) but also on the specific technical capabilities of pulse oximeters used (such as time response, noises, margin of error, etc).
Shelley84 clearly described the desirable characteristics of oximeters to perform a correct pulse oximetry analysis. Newer pulse oximeters should provide at least the following functions: the ability to disable the autogain and autocenter functions, to eliminate certain signal filtering, to change the time scale, to improve the time resolution, and to show red-infrared data and the continuous component of PPG. The access to real and unfiltered raw data could enable new clinical applications and/or improve the standard ones.
Let us illustrate the above with an example. Some commercial oximeters have a time response of Spo2 between 8 to 12 seconds. An improved time resolution allowing a beat-by-beat calculation of Spo2 would increase the accuracy and reliability of the Spo2/Fio2 diagram because it will provide more data points per unit of time. A better diagram will allow a better analysis. Using standard oximeters, a step change in Fio2 of 10% will result in no >5 to 8 Spo2 values per minute in a patient with a heart rate of 80, but in 80 data points if a beat-by-beat calculation of Spo2 was available.
The sensitivity and specificity of a particular pulse oximeter or one of its features will depend on many technical factors. Manufacturers know these technical factors and the limitations of their devices. Therefore, it is time for them to change the way they analyze and interpret raw data, but, more importantly, they should change the way they present clinically relevant information to the users. Such highly appreciated technical improvements will increase the clinical impact of both current and future pulse oximeters.
Furthermore, as with any noninvasive technology, the performance of oximeter-dependent calculations will be below the accuracy of standard invasive techniques. The main advantage of pulse oximetry resides in its simplicity and availability as a first-line monitoring system not only for mechanically ventilated patients. One of the next steps will be to investigate in more depth the accuracy and reliability of these unusual capabilities of current pulse oximeters.
Pulse oximetry is an indispensable monitoring modality in respiratory medicine, because it provides real-time, continuous, and noninvasive information on arterial oxygenation. However, the optical spectrometric and plethysmographic capabilities of pulse oximetry are largely underused in the monitoring of patients undergoing mechanical ventilation. The extended clinical applications proposed here are related to the diagnosis of hidden oxygenation deficits using the Spo2/Fio2 diagram and to the detection of preload dependencies and the monitoring of changes in arterial vascular impedance. Such advanced uses of pulse oximetry will necessarily be less precise than standard invasive techniques but will constitute a valuable first-line monitoring approach for most mechanically ventilated patient in whom more invasive monitoring means are not indicated.
The future challenge will be to identify and better characterize these pulse oximetry functionalities and to further develop them for use in the acute care setting. Many of these potential uses have been described, and this accumulated knowledge should be incorporated into our pulse oximetry devices, providing more holistic and intelligent solutions for the assessment and treatment of oxygenation and hemodynamic problems during positive pressure ventilation.
Name: Gerardo Tusman, MD.
Contribution: This author helped prepare the manuscript.
Name: Stephan H. Bohm, MD.
Contribution: This author helped design the concept and prepare the manuscript.
Name: Fernando Suarez-Sipmann, PhD.
Contribution: This author helped design the concept and prepare the manuscript.
This manuscript was handled by: Maxime Cannesson, MD, PhD.
1. Yoshiya I, Shimada Y, Tanaka K. Spectrophotometric monitoring of arterial oxygen saturation in the fingertip. Med Biol Eng Comput 1980;18:2732.
2. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007;28:R139.
3. Alexander CM, Teller LE, Gross JB. Principles of pulse oximetry: theoretical and practical considerations. Anesth Analg 1989;68:36876.
4. Schnapp LM, Cohen NH. Pulse oximetry. Uses and abuses. Chest 1990;98:124450.
5. Mendelson Y. Pulse oximetry: theory and applications for noninvasive monitoring. Clin Chem 1992;38:16017.
6. Kyriacou PA. Pulse oximetry in the oesophagus. Physiol Meas 2006;27:R135.
7. Severinghaus JW. Takuo Aoyagi: discovery of pulse oximetry. Anesth Analg 2007;105:S14.
8. Yelderman M, New W Jr. Evaluation of pulse oximetry. Anesthesiology 1983;59:34952.
9. Mihm FG, Halperin BD. Noninvasive detection of profound arterial desaturations using a pulse oximetry device. Anesthesiology 1985;62:857.
10. Rebuck AS, Chapman KR, D’Urzo A. The accuracy and response characteristics of a simplified ear oximeter. Chest 1983;83:8604.
11. Moller JT, Jensen PF, Johannessen NW, Espersen K. Hypoxaemia is reduced by pulse oximetry monitoring in the operating theatre and in the recovery room. Br J Anaesth 1992;68:14650.
12. Canet J, Ricos M, Vidal F. Postanesthetic hypoxemia and oxygen administration. Anesthesiology 1991;74:11612.
13. Pedersen T, Hovhannisyan K, Moller AM. Pulse oximetry for perioperative monitoring. Cochrane Database Syst Rev 2009;4:CD002013.
14. Eichhorn JH, Cooper JB, Cullen DJ, Maier WR, Philip JH, Seeman RG. Standards for patient monitoring during anesthesia at Harvard Medical School. JAMA 1986;256:101720.
15. Reisner A, Shaltis PA, McCombie D, Asada HH. Utility of the photoplethysmogram in circulatory monitoring. Anesthesiology 2008;108:9508.
16. Guillot B. Techniques used to evaluate the cutaneous microcirculation: application of photoplethysmography to the assessment of a phlebotropic agent in the treatment of leg ulcers. Angiology 1994;45:5428.
17. Fronek A. Photoplethysmography in the diagnosis of venous disease. Dermatol Surg 1995;21:646.
18. Ando J, Kawarada A, Shibata M, Yamakoshi K, Kamiya A. Pressure-volume relationships of finger arteries in healthy subjects and patients with coronary atherosclerosis measured non-invasively by photoelectric plethysmography. Jpn Circ J 1991;55:56775.
19. Bortolotto LA, Blacher J, Kondo T, Takazawa K, Safar ME. Assessment of vascular aging and atherosclerosis in hypertensive subjects: second derivative of photoplethysmogram versus pulse wave velocity. Am J Hypertens 2000;13:16571.
20. Chowienczyk PJ, Kelly RP, MacCallum H, Millasseau SC, Andersson TL, Gosling RG, Ritter JM, Anggård EE. Photoplethysmographic assessment of pulse wave reflection: blunted response to endothelium-dependent beta2-adrenergic vasodilation in type II diabetes mellitus. J Am Coll Cardiol 1999;34:200714.
21. Nasimi SG, Mearns AJ, Harness JB, Heath I. Quantitative measurement of sympathetic neuropathy in patients with diabetes mellitus. J Biomed Eng 1991;13:2038.
22. Wandrup JH. Quantifying pulmonary oxygen transfer deficits in critically ill patients. Acta Anaesthesiol Scand Suppl 1995;107:3744.
23. Sapsford DJ, Jones JG. The PIO2
diagram: a non-invasive measure of pulmonary oxygen exchange. Eur J Anaesthesiol 1995;12:37586.
24. Jones JG, Jones SE. Discriminating between the effect of shunt and reduced VA/Q on arterial oxygen saturation is particularly useful in clinical practice. J Clin Monit Comput 2000;16:33750.
25. Kjaergaard S, Rees S, Malczynski J, Nielsen JA, Thorgaard P, Toft E, Andreassen S. Non-invasive estimation of shunt and ventilation-perfusion mismatch. Intensive Care Med 2003;29:72734.
26. Hedenstierna G, Tokics L, Strandberg A, Lundquist H, Brismar B. Correlation of gas exchange impairment to development of atelectasis during anaesthesia and muscle paralysis. Acta Anaesthesiol Scand 1986;30:18391.
27. Magnusson L, Spahn DR. New concepts of atelectasis during general anaesthesia. Br J Anaesth 2003;91:6172.
28. Gattinoni L, Caironi P, Pelosi P, Goodman LR. What has computed tomography taught us about the acute respiratory distress syndrome? Am J Respir Crit Care Med 2001;164:170111.
29. Smith HL, Jones JG. Non-invasive assessment of shunt and ventilation/perfusion ratio in neonates with pulmonary failure. Arch Dis Child Fetal Neonatal Ed 2001;85:F12732.
30. de Gray L, Rush EM, Jones JG. A noninvasive method for evaluating the effect of thoracotomy on shunt and ventilation perfusion inequality. Anaesthesia 1997;52:6305.
31. Tusman G, Groisman I, Fiolo FE, Scandurra A, Arca JM, Krumrick G, Bohm SH, Sipmann FS. Noninvasive monitoring of lung recruitment maneuvers in morbidly obese patients: the role of pulse oximetry and volumetric capnography. Anesth Analg 2014;118:13744.
32. Canet J, Gallart L, Gomar C, Paluzie G, Vallès J, Castillo J, Sabaté S, Mazo V, Briones Z, Sanchis J; ARISCAT Group. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology 2010;113:133850.
33. O’Rourke MF, Yaginuma T, Avolio AP. Physiological and pathophysiological implications of ventricular/vascular coupling. Ann Biomed Eng 1984;12:11934.
34. Nichols WW, O’Rourke MF.McDonald’s Blood Flow in Arteries. Theoretical, Experimental and Clinical Principles. 1999London: Edward Arnold.
35. O’Rourke MF. Pressure and flow waves in systemic arteries and the anatomical design of the arterial system. J Appl Physiol 1967;23:13949.
36. Korpas D, Hálek J, Dolezal L. Parameters describing the pulse wave. Physiol Res 2009;58:4739.
37. de Trafford J, Lafferty K. What does photoplethysmography measure? Med Biol Eng Comput 1984;22:47980.
38. Sherebrin MH, Sherebrin RZ. Frequency analysis of the peripheral pulse wave detected in the finger with a photoplethysmograph. IEEE Trans Biomed Eng 1990;37:3137.
39. Lawson D, Norley I, Korbon G, Loeb R, Ellis J. Blood flow limits and pulse oximeter signal detection. Anesthesiology 1987;67:599603.
40. Wisely NA, Cook LB. Arterial flow waveforms from pulse oximetry compared with measured Doppler flow waveforms apparatus. Anaesthesia 2001;56:55661.
41. Millasseau SC, Guigui FG, Kelly RP, Prasad K, Cockcroft JR, Ritter JM, Chowienczyk PJ. Noninvasive assessment of the digital volume pulse. Comparison with the peripheral pressure pulse. Hypertension 2000;36:9526.
42. Almond NE, Jones DP, Cooke ED. Noninvasive measurement of the human peripheral circulation: relationship between laser Doppler flowmeter and photoplethysmograph signals from the finger. Angiology 1988;39:81929.
43. Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M; Early Goal-Directed Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med 2001;345:136877.
44. National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials NetworkWiedemann HP, Wheeler AP, Bernard GR, Thompson BT, Hayden D, deBoisblanc B, Connors AF Jr, Hite RD, Harabin AL: National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials Network, Comparison of two fluid management strategies in acute lung injury. N Engl J Med 2006; 354:256475.
45. Monk TG, Bronsert MR, Henderson WG, Mangione MP, Sum-Ping ST, Bentt DR, Nguyen JD, Richman JS, Meguid RA, Hammermeister KE. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology 2015;123:30719.
46. Alian AA, Galante NJ, Stachenfeld NS, Silverman DG, Shelley KH. Impact of central hypovolemia on photoplethysmographic waveform parameters in healthy volunteers. Part 1: time domain analysis. J Clin Monit Comput 2011;25:37785.
47. Partridge BL. Use of pulse oximetry as a noninvasive indicator of intravascular volume status. J Clin Monit 1987;3:2638.
48. Murray WB, Foster PA. The peripheral pulse wave: information overlooked. J Clin Monit 1996;12:36577.
49. Shamir M, Eidelman LA, Floman Y, Kaplan L, Pizov R. Pulse oximetry plethysmographic waveform during changes in blood volume. Br J Anaesth 1999;82:17881.
50. Natalini G, Rosano A, Taranto M, Faggian B, Vittorielli E, Bernardini A. Arterial versus plethysmographic dynamic indices to test responsiveness for testing fluid administration in hypotensive patients: a clinical trial. Anesth Analg 2006;103:147884.
51. Cannesson M, Besnard C, Durand PG, Bohé J, Jacques D. Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients. Crit Care 2005;9:R5628.
52. Cannesson M, Attof Y, Rosamel P, Desebbe O, Joseph P, Metton O, Bastien O, Lehot JJ. Respiratory variations in pulse oximetry plethysmographic waveform amplitude to predict fluid responsiveness in the operating room. Anesthesiology 2007;106:110511.
53. Cannesson M, Desebbe O, Rosamel P, Delannoy B, Robin J, Bastien O, Lehot JJ. Pleth variability index to monitor the respiratory variations in the pulse oximeter plethysmographic waveform amplitude and predict fluid responsiveness in the operating theatre. Br J Anaesth 2008;101:2006.
54. Cannesson M, Delannoy B, Morand A, Rosamel P, Attof Y, Bastien O, Lehot JJ. Does the Pleth variability index indicate the respiratory-induced variation in the plethysmogram and arterial pressure waveforms? Anesth Analg 2008;106:118994.
55. Chan GS, Middleton PM, Celler BG, Wang L, Lovell NH. Automatic detection of left ventricular ejection time from a finger photoplethysmographic pulse oximetry waveform: comparison with Doppler aortic measurement. Physiol Meas 2007;28:43952.
56. Geeraerts T, Albaladejo P, Declère AD, Duranteau J, Sales JP, Benhamou D. Decrease in left ventricular ejection time on digital arterial waveform during simulated hypovolemia in normal humans. J Trauma 2004;56:8459.
57. Middleton PM, Chan GS, O’Lone E, Steel E, Carroll R, Celler BG, Lovell NH. Changes in left ventricular ejection time and pulse transit time derived from finger photoplethysmogram and electrocardiogram during moderate haemorrhage. Clin Physiol Funct Imaging 2009;29:1639.
58. Bendjelid K, Suter PM, Romand JA. The respiratory change in preejection period: a new method to predict fluid responsiveness. J Appl Physiol (1985) 2004;96:33742.
59. Feissel M, Badie J, Merlani PG, Faller JP, Bendjelid K. Pre-ejection period variations predict the fluid responsiveness of septic ventilated patients. Crit Care Med 2005;33:25349.
60. Arnett DK, Evans GW, Riley WA. Arterial stiffness: a new cardiovascular risk factor? Am J Epidemiol 1994;140:66982.
61. Millasseau SC, Kelly RP, Ritter JM, Chowienczyk PJ. Determination of age-related increases in large artery stiffness by digital pulse contour analysis. Clin Sci (Lond) 2002;103:3717.
62. Allen J, Murray A. Age-related changes in the characteristics of the photoplethysmographic pulse shape at various body sites. Physiol Meas 2003;24:297307.
63. Takazawa K, Tanaka N, Fujita M, Matsuoka O, Saiki T, Aikawa M, Tamura S, Ibukiyama C. Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform. Hypertension 1998;32:36570.
64. Imanaga I, Hara H, Koyanagi S, Tanaka K. Correlation between wave components of the second derivative of plethysmogram and arterial distensibility. Jpn Heart J 1998;39:77584.
65. Lund F. Digital pulse plethysmography (DPG) in studies of the hemodynamic response to nitrates—a survey of recording methods and principles of analysis. Acta Pharmacol Toxicol (Copenh) 1986;59(Suppl 6):7996.
66. Ezri T, Steinmetz A, Geva D, Szmuk P. Skin vasomotor reflex as a measure of depth of anesthesia. Anesthesiology 1998;89:12812.
67. Zhang XY, Zhang YT. The effect of local mild cold exposure on pulse transit time. Physiol Meas 2006;27:64960.
68. Awad AA, Ghobashy MA, Ouda W, Stout RG, Silverman DG, Shelley KH. Different responses of ear and finger pulse oximeter wave form to cold pressor test. Anesth Analg 2001;92:14836.
69. Awad AA, Haddadin AS, Tantawy H, Badr TM, Stout RG, Silverman DG, Shelley KH. The relationship between the photoplethysmographic waveform and systemic vascular resistance. J Clin Monit Comput 2007;21:36572.
70. Lee QY, Chan GS, Redmond SJ, Middleton PM, Steel E, Malouf P, Critoph C, Flynn G, O’Lone E, Lovell NH. Multivariate classification of systemic vascular resistance using photoplethysmography. Physiol Meas 2011;32:111732.
71. Lima AP, Beelen P, Bakker J. Use of a peripheral perfusion index derived from the pulse oximetry signal as a noninvasive indicator of perfusion. Crit Care Med 2002;30:12103.
72. De Felice C, Latini G, Vacca P, Kopotic RJ. The pulse oximeter perfusion index as a predictor for high illness severity in neonates. Eur J Pediatr 2002;161:5612.
73. Yousef Q, Reaz MBI, Ali MAM. The analysis of PPG morphology: investigating the effects of aging on arterial compliance. Measurement Sci Rev 2012;12:26671.
74. Rubins U, Grube J, Kukulis I. Photoplethysmography analysis of artery properties in patients with cardiovascular diseases. In 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, 16–20 June 2008. Springer, 319–322.
75. Dillon JB, Hertzman AB. The form of the volume pulse in the finger pad in health, arteriosclerosis, and hypertension. Am Heart J 1941; 21:172190.
76. Lax H, Feinberg AW, Cohen BM. Studies of the arterial pulse wave. I. The normal pulse wave and its modification in the presence of human arteriosclerosis. J Chronic Dis 1956;3:61831.
77. Dawber TR, Thomas HE Jr, McNamara PM. Characteristics of the dicrotic notch of the arterial pulse wave in coronary heart disease. Angiology 1973;24:24455.
78. Morikawa Y. Characteristic pulse wave caused by organic nitrates. Nature 1967;213:8412.
79. Stengele E, Winkler F, Trenk D, Jähnchen E, Petersen J, Roskamm H. Digital pulse plethysmography as a non-invasive method for predicting drug-induced changes in left ventricular preload. Eur J Clin Pharmacol 1996;50:27982.
80. Nouira S, Elatrous S, Dimassi S, Besbes L, Boukef R, Mohamed B, Abroug F. Effects of norepinephrine on static and dynamic preload indicators in experimental hemorrhagic shock. Crit Care Med 2005;33:233943.
81. Westphal GA, Gonçalves AR, Bedin A, Steglich RB, Silva E, Poli-de-Figueiredo LF. Vasodilation increases pulse pressure variation, mimicking hypovolemic status in rabbits. Clinics (Sao Paulo) 2010;65:18994.
82. Hadian M, Severyn DA, Pinsky MR. The effects of vasoactive drugs on pulse pressure and stroke volume variation in postoperative ventilated patients. J Crit Care 2011; 26:328.e1328.e8.
83. Akata T. General anesthetics and vascular smooth muscle: direct actions of general anesthetics on cellular mechanisms regulating vascular tone. Anesthesiology 2007;106:36591.
84. Shelley KH. Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate. Anesth Analg 2007;105:S316.