Which one of the following choices is the most frequently used method for taking a pulse?

Physiologic Management

As part of the preoperative assessment, a series of blood pressure and heart rate measurements should be obtained to define patient-specific acceptable ranges for perioperative management. Blood pressure should be maintained in the high–normal range throughout the procedure, particularly during the period of carotid clamping, in an attempt to increase collateral blood flow to prevent cerebral ischemia.

Hemodynamic fluctuations are common during CEA. Hypotension is more common under general anesthesia and often occurs immediately after the induction of anesthesia or after carotid unclamping and cerebral reperfusion.18 In patients with contralateral internal carotid artery occlusion or severe stenosis, induced hypertension to approximately 10% to 20% above baseline may be helpful during carotid clamping when neurophysiologic monitoring is not used. Blood pressure preservation or augmentation can be accomplished by appropriate intravascular hydration, avoiding unnecessarily deep levels of general anesthesia, and vasopressor therapy, such as phenylephrine and ephedrine. In the absence of severe left ventricular systolic dysfunction, phenylephrine may be preferred over ephedrine because increased contractility and heart rate associated with ephedrine increases myocardial oxygen consumption to a greater extent than the increase in wall stress caused by phenylephrine.

Because intraoperative hypertension and tachycardia can increase myocardial oxygen consumption, blood pressure elevations above 20% of the baseline and tachycardia should be avoided. Furthermore, such increases in blood pressure might put the patient with a recent stroke at risk of hemorrhagic conversion of the infarcted brain tissue. Hypertension should be treated appropriately, taking into account the stage of the operation, the level of anesthesia, and the patient’s heart rate. Deepening the anesthetic, beta-blocker therapy, nicardipine, and clevidipine are commonly used. Agents with a short half-life are preferred.

Bradycardia might occur during surgical manipulation of the carotid sinus or direct stimulation of the vagus nerve during dissection. Prophylactic injection of local anesthetic between the internal and external carotid arteries before manipulation of these vessels can attenuate bradycardia. However, local anesthetic infiltration may increase intraoperative and postoperative hypertension. Administration of anticholinergic drugs can result in tachycardia, excessive hypertension, and increased myocardial oxygen requirements, and their use should therefore be avoided.

Severe carotid stenosis, in particular during cross-clamping, represents a state of gross regional flow inequality and collateral dependence. The vessels supplying ischemic areas are usually maximally vasodilated and blood flow could be diverted from these vascular beds to those already adequately perfused by cerebral vasodilators such as carbon dioxide, volatile anesthetic agents, and nitrates (i.e., cerebral steal). The reverse may occur in hypocapnia or as a result of intravenous anesthetic agents such as thiopental and propofol (i.e., inverse steal). On balance, normocarbia is recommended and low levels of volatile anesthetic agents are acceptable.

There is sufficient evidence that hyperglycemia in nondiabetic patient is associated with poor outcome. Persistent hyperglycemia, defined as hyperglycemia within 6 hours of stroke and at 24 hours, is inversely associated with neurological improvement, 30-day favorable functional outcome, and 90-day negligible dependence.19

QT/heart rate hysteresis

Frequently, little attention is paid to the potentially substantial errors in QTc intervals due to incorrect heart rate measurements. That is, even if the QT interval is measured accurately, erroneous QTc values may be obtained if the QT interval duration is corrected for an RR interval that does not correspond to the heart rate that truly underlies the QT duration.

A most important source of these inaccuracies is the so-called QT/heart rate (or QT/RR) hysteresis. It has been repeatedly described that QT interval duration does not depend on (and thus should not be corrected for) instantaneously measured heart rate but that it responds to heart rate instability with a considerable delay [18–20]. This means that if QT interval is corrected for simultaneously measured RR interval while the heart rate is accelerating, erroneously long QTc value is obtained since the QT interval duration is still under the influence of the slower heart rate in the past. Correspondingly, if QT interval is corrected for simultaneously measured RR interval while the heart rate is decelerating, artificially short QTc value is produced.

Example of the problem is shown in Fig. 9.3, which shows two 10-s ECGs recorded in a healthy subject who was in a strict supine position for more than 5 min prior to the first ECG. These two tracings were separated by only a 10-s gap between them, and still, their heart rate differed by more than 20 beats per minute. The figure also shows that the uncorrected QT interval was the same as the time that elapsed between the two recordings was too short for the QT interval to adapt to the new or transient heart rate levels. When the QT interval was corrected for instantaneously measured 10-s heart rate, Bazett and Fridericia correction showed difference of 73 and 47 ms, respectively. However, when the 5-min heart rate history was used for individual QT/RR hysteresis correction (as described further), the corrected QTc intervals differed by only 2 ms.

Several ways to account for QT/RR hysteresis have been proposed [21–23]. All these proposals were based on formulas that process longer series of RR intervals preceding the QT interval measurement and, based on this history of RR interval development, produce a duration of RR interval that should be used for the correction of the QT interval [24,25].

Perhaps, most experience exists with the so-called exponential decay hysteresis model that is based on the following consideration: For a QT interval measurement, the sequence of preceding RR intervals {RRi}i=0N (RR0 closest to the QT measurement) is considered. The RR interval representing the heart rate underlying the QT measurement is then calculated as

RR'=∑i=0NωiRR i

where for each j=0,…,N,

∑i=0j ωi=(1−e−λ∑i=0jRRi∑i=0NRRi) /1−e−λ

The coefficient λ characterizes the subject-specific QT/RR hysteresis, i.e., the speed with which QT interval adapted to changing heart rate. To obtain the coefficient λ, multiple QT interval measurements at different heart rate and with different profiles of the RR interval history are needed. Subsequently, the coefficient λ is calculated by minimizing the spread of QT/RR′ values.

This principle is shown in Fig. 9.4, which shows scatter diagrams of QT/RR values obtained in two different healthy subjects in whom multiple QT interval measurements were made. The figure shows that relating the same QT interval durations to different expressions of RR intervals representing the underlying heart rate, the spread of the scatter diagrams is gradually reduced. When incorporating the hysteresis derived RR′ intervals, the individual-specific shape of the QT/RR′ relationship becomes compact and shows that there is little QT interval variability beyond the properly characterized heart rate influence.

Since the value of λ represents the speed with which the QT interval duration adapts, it is customary to convert it into the so-called hysteresis time constant, i.e., the time needed for the QT interval to reach 95% of its new value after a heart rate change.

Similar to fixed heart rate corrections, it was also proposed that universal hysteresis correction is possible allowing to correct for hysteresis without optimizing the coefficient λ in each subject separately. Since, in healthy subjects, the 95% adaptation of the QT interval after heart rate changes is on average achieved after 2 min, the hysteresis correction model with λ = 7.4622 (which corresponds to the 2-min 95% adaptation) may be used universally in physiologic studies [26]. In cardiac patients, however, different hysteresis correction models are needed [27].

Intrinsic

One mechanistic etiology of IST is referred to as an intrinsic mechanism. In 1994, Morillo et al. provided an in-depth phenotypic evaluation, IHR measurement, and autonomic balance of a small series of IST patients (all female) compared to control group. An infusion of propranolol (0.2 mg/kg) and atropine (0.04 mg/kg) was used to achieve pharmacologic autonomic denervation to measure the IHR. The IHR was 2 standard deviations higher than formula predicted value (see IHR section above), and has been corroborated by several other studies including Ref. [7,11]. The observations of Ref. [11] can be appreciated from Fig. 40.3 indicating an increased resting HR and IHR. These authors hypothesized that this pointed to an intrinsic abnormality of the SN. In the Morillo et al. study, the IST group demonstrated an exaggerated response to beta-adrenergic stimulation and in contrast a depressed response to cardiovagal stimulation, Fig. 40.4.

Enhanced or exaggerated sympathetic response has recently been reported to be due to an increased cAMP response from a rare HCN4 mutation. Baruscotti et al. performed functional studies from this HCN4 mutation and showed a gain of function, which is in contrast to the loss-of-function HCN4 mutations causing bradycardia and SN dysfunction [9]. The reported gain of HCN4 function mutation is located in the cyclonucleotide-binding domain (CNBD) where cAMP binds to HCN4. Expressed in heterologous expression system, the HCN4 mutation causes channel activation at more depolarized voltages and increased cAMP sensitivity. This same mutation expressed in murine neonatal cardiomyocytes is associated with an increased intrinsic heart rate and a shift in activation voltage to more depolarized voltages.

The endogenous purine nucleoside adenosine has several antiarrhythmic effects and is known to slow sinus rates, particularly with higher atrial rates. Still et al. demonstrated that in IST patients, sinus rates were only minimally slowed even at the maximum adenosine dose regardless of autonomic tone. Moreover, IST patients exhibited an impairment of the negative chronotropic action of adenosine, which was surprising given the rate-dependent action of adenosine. In that study, the authors concluded that with the impaired response to adenosine, excessive sympathetic input seemed less likely to be the primary mechanism and suggested an intrinsic malfunction of the acetylcholine- and adenosine-sensitive potassium channel.

Methods of Recording

Maximal heart rate is the most important determinant of cardiac output during exercise, particularly at high levels. One issue of concern in the past was the method of maximal heart rate measurement. Although measuring a patient's maximal heart rate should be a simple matter, the different ways of recording it and differences in the type of exercise used can pose problems. The best way to measure maximal heart rate is to use a standard electrocardiogram (ECG) recorder and calculate instantaneous heart rate from the RR intervals. Methods using the arterial pulse or capillary blush techniques are much more affected by artifact than ECG techniques. Some investigators have used averaging over the last minute of exercise or in immediate recovery; both of these averaging methods are inaccurate. Heart rate drops quickly in recovery and can climb steeply even in the last seconds of exercise. Premature beats can affect averaging and must be excluded in order to obtain the actual heart rate. Cardiotachometers are available but may fail to trigger or may trigger inappropriately on T waves, artifact, or aberrant beats, thus yielding inaccurate results. Not all cardiotachometers have the accuracy of the ECG paper technique. From a study of heart rate measurement during exercise testing, it was concluded that the number of RR intervals from a 6-second rhythm strip at the end of each minute multiplied by 10 represented a reasonable balance between convenience and precision for measuring heart rate during exercise, both in patients in normal sinus rhythm21 and in those with an irregular ventricular response (atrial fibrillation).

(a) Heart rate measurements

Heart rate is a valuable measure of physiological strain. The level of strain measured by heart rate per minute is strongly related to the physical work load and to the physical work capacity of the subject. Heart rate measurements can be combined with the activity studies using Edholm scale or with the VO2 measurements. Several reliable techniques over 8 to 12 hours continuous recordings are available (e.g. Sport tester). The heart rate data should be analysed to identify the following aspects:

work tasks producing the highest strain;

number of minutes over 50% of relative aerobic strain (RAS);

recovery during breaks;

trends during the 8-hour work shift.

The heart rate can be analysed as a % of maximum heart rate (%HRmax) or % of heart range (%HRR). If HR and VO2 have been measured simultaneously during work, the HR/VO2 relationship can be used for predicting the VO2 for other similar tasks, where the VO2 measurements have not been carried out.

Heart rate

There is a good correlation between the heart rate and embryonic size and age. Alterations of the embryonic heart rate such as arrhythmia and/or bradycardia may be associated with maldevelopment.22,25 Embryonic heart rate measurements in early pregnancy may be useful in the prediction of first-trimester spontaneous abortion after ultrasound-proven viability, but a heart rate below the 95% confidence interval of normal does not necessarily indicate a poor outcome.26 A general rule is that if the embryo has a CRL of 6 mm or more, the lack of heart activity is highly suspicious for intrauterine embryonic/fetal death. A significant relationship to abortion has been found when the heart rate is less than 1.2 SD from the mean.21

Methods of Recording.

From many hemodynamic studies performed over the years, maximal heart rate has emerged as clearly the most important determinant of cardiac output during exercise, particularly at high levels. One issue of concern in the past was the method of maximal heart rate measurement. Although measuring a patient's maximal heart rate should be a simple matter, the different ways of recording it and differences in the type of exercise used can pose problems. The best way to measure maximal heart rate is to use a standard ECG recorder and calculate instantaneous heart rate from the RR intervals. Methods using the arterial pulse or capillary blush techniques are much more affected by artifact than ECG techniques. Some investigators have used averaging over the last minute of exercise or in immediate recovery; both of these averaging methods are inaccurate. Heart rate drops quickly in recovery and can climb steeply even in the last seconds of exercise. Premature beats can affect averaging and must be eliminated in order to obtain the actual heart rate. Cardiotachometers are available but may fail to trigger or may trigger inappropriately on T waves, artifact, or aberrant beats, thus yielding inaccurate results. Not all cardiotachometers have the accuracy of the ECG paper technique.

Atwood et al37 compared nine different sampling intervals (1, 2, 3, 6, 10, 15, 20, 30, and 60 seconds) using calipers at rest and during exercise to determine the “ideal” method of measurement in subjects with normal sinus rhythm and patients with atrial fibrillation. This task is particularly difficult in patients with atrial fibrillation because of the irregularity of the ventricular response. The heart rate obtained from each interval was compared with true heart rate (determined by a 4-minute sample at rest and by the last 30 seconds of each minute during exercise). Among patients with atrial fibrillation, large differences were observed between the heart rate obtained and true heart rate, both at rest and during exercise, using small sampling intervals. The mean of these differences ranged between 16 ± 11 beats per minute (range 14 to 22) using 1-second sampling intervals and 2.2 ± 2.0 beats per minute (range 1.6 to 4.4) using 20-second sampling intervals during progressive exercise. Variability of the heart rate obtained from random heart rate samples was also high when short sampling intervals were used among patients with atrial fibrillation. These observations were contrasted by subjects in normal sinus rhythm, among whom neither variability nor measurement error was influenced remarkably by changing the sampling interval or increasing heart rate. It was concluded that the number of RR intervals from a 6-second rhythm strip at the end of each minute multiplied by 10 represented a reasonable balance between convenience and precision for measuring heart rate during exercise, both in patients with atrial fibrillation and those in normal sinus rhythm.

Physiological and perceptual response changes

As described earlier, the gold standard for exercise testing responses of cardiac patients is the use of both cardio (ECG) and respiratory (expired gas analysis) measures during an incremental peak/maximal test, e.g. the Bruce protocol (Bruce et al 1973). The use of expired gas analysis is rare in the UK except in specialist centres of research.

The four measures available, typically taken and recommended in the UK during early discharge or outpatient cardiology clinics, are the ECG (heart rate and ECG changes), blood pressure, estimated METs and RPE. With modern technology of wireless chest-strap telemetry heart rate monitors, it is very practicable to measure heart rate within a cardiac rehabilitation setting along with RPE. Therefore if the rehabilitation gym has a treadmill, then submaximal components of the Bruce or modified Bruce protocol can be repeated as a means of measuring improvement against initial clinical testing results taken previously for purposes of diagnosis and prognosis.

Theoretical and Analytical Considerations in Measuring Emotions

There is also theoretical work to be done. For example, it is widely agreed that emotions are multicomponential (Gross, 2010; Pekrun, 2011), but not whether different emotional expression components (experiential, behavioral, and physiological) will align in terms of expressing a common emotional state (i.e., coherence; Ekman, 1992; Gross & Barrett, 2011; Pekrun, 2011). Therefore, uncertainty exists in terms of whether divergent emotional classifications between methods and modalities that reflect different expression components (e.g., behavioral: automatic facial expression recognition software versus physiological: electrodermal activation devices) are the result of poor calibration or signal filtering or a feature of emotions themselves (see Harley et al., 2015). Moreover, expectations are not clearly defined regarding how differences in emotion classifications should be interpreted, especially in the absence of an empirically validated grounded truth measure, when encountered in measurements between methods that examine emotions from the same emotional expression component, but different modalities (e.g., behavioral: automatic facial expression recognition software versus automated postural sensors). Incongruous classifications from similar modalities and expression components, but different methods (e.g., physiological: electrodermal activation devices and heart rate measurement devices) can also raise the question of which one is right? It is therefore important to advance our theories of emotion (using empirical results), so that they may provide appropriate and contextualized hypotheses for comparing individuals’ emotional responses from experiential, behavioral, and physiological expression components that are measured using different modalities and methods.

In addition to other important future directions mentioned previously (theoretical and classifier advances, more research with multiple methods of emotion measurement, naturalistic data), the statistical techniques used to analyze learners’ emotions must evolve if we are to take full advantage of data with non-normal distributions, continuous, and nonparametric characteristics. These advances are critical if we are to evolve from the current best practices of time-sampling (which uses only portions of available data), averaging measures across episodes or sub-episodes together, and affective-state transitions (the likelihood of transitioning from one emotional state to another; Baker, Rodrigo, & Xolocotzin, 2007; D’Mello & Graesser, 2012a; McQuiggan, Robison, & Lester, 2010). These approaches do provide information on the temporal nature of emotions, but at a far larger grain size than we are currently able to measure with trace methods (e.g., log-files, facial expressions, physiological methods). For example, with time sampling, we can measure fluctuations of emotions over the course of a learning session, but not the moment-to-moment fluctuations that illustrate when an emotion was elicited and why. Crossing this analytical barrier will also help us answer important and informative theoretical questions about the temporal nature of emotions that many researchers have begun to investigate, but cannot presently fully answer. In other words, our analytical approaches have not yet caught up with our methodological ones (see Calvo & D’Mello, 2010, and Mauss & Robinson, 2009, for a discussion of these and other challenges and future directions related to emotion measurement).

Conclusion

Heart rate and blood lactate measurements during standardized field exercise tests are relevant to the management of all athletic horses. These measurements can assist with performance prediction and evaluation of fitness changes, and can be used to alert owners and trainers to problems such as lameness and respiratory disease. More effort is needed to adapt new technologies and refine approaches to design of field exercise tests. It is unlikely that veterinarians, trainers, or owners will be enthusiastic about equine fitness testing if the focus is not on simple approaches to field exercise tests. Simple tests, measuring the things that matter, constitute the approach in field studies in human sports laboratories. Heart rate and blood lactate measurements feature prominently in human field studies. Progress in technology transfer of applied exercise physiology might be greater if there was greater emphasis on field methods, using minimally invasive techniques. Every fitness test should answer a specific question, and results should be expressed in a way that helps a veterinarian, trainer, or owner make more informed decisions about the training, fitness, health, or management of horses.

A promising new technique for field fitness tests could be use of heart rate measurements in combination with measurement of velocity with differential global position system technology. Simultaneous logging of a horse's heart rate and velocity could be a powerful technique for field exercise tests.49–51

The reliance on treadmills for most equine exercise research may have contributed to poor rates of technology transfer to equine veterinarians, trainers, and horse owners. There are few established equine performance laboratories in the world, and many are located at considerable distances from racing populations. There will never be enough university-based treadmills to service all horses with sufficient facilities for fitness tests. Development of simple and user-friendly techniques for exercise studies of horses that do not depend on treadmills would therefore be a major advance.

New partnerships between equine exercise scientists and biomedical engineers could also generate new technologies for field studies. For example, field studies of breath-by-breath respiratory gas flows, and field ergometry, should be possible, building on the innovative studies performed in Germany.18,46 The techniques for field ergometry have been developed for human athletes, and could be refined for use in horses. However, the technical challenge of reliably and accurately measuring respiratory flow rates of over 100 L/s in a horse galloping in the field at 1000 m/min has yet to be conquered. Field ergometry, coupled with measurements of heart rate, respiratory function, and metabolic responses to exercise, would enable new fundamental studies in many areas of equine exercise physiology. Descriptions of the metabolic and energetic demands of different athletic events would be possible, and design of appropriate training programs would be facilitated. As well, clinical exercise testing would be more likely to be adopted by trainers and owners. Such ‘high tech’ clinical exercise tests would contribute to greater knowledge concerning limits to performance in different events (such as anaerobic or aerobic capacity, and maximal rates of oxygen uptake). Greater rates of technology transfer to industry participants are likely if researchers increase their use of normal horses in commercial training, and if technical developments free researchers from the constraints and limitations of treadmill fitness tests.