Changes in Heart Rate in Response to Cold Pressor Test (HRVW1)
|First Received Date ICMJE||May 13, 2008|
|Last Updated Date||September 19, 2010|
|Start Date ICMJE||May 2008|
|Primary Completion Date||October 2008 (final data collection date for primary outcome measure)|
|Current Primary Outcome Measures ICMJE||Not Provided|
|Original Primary Outcome Measures ICMJE||Not Provided|
|Change History||Complete list of historical versions of study NCT00678262 on ClinicalTrials.gov Archive Site|
|Current Secondary Outcome Measures ICMJE||Not Provided|
|Original Secondary Outcome Measures ICMJE||Not Provided|
|Current Other Outcome Measures ICMJE||Not Provided|
|Original Other Outcome Measures ICMJE||Not Provided|
|Brief Title ICMJE||Changes in Heart Rate in Response to Cold Pressor Test|
|Official Title ICMJE||Changes in Heart Rate in Response to Cold Pressor Test|
Pain, a subjective sensation, has been increasingly studied, as it has been recognized as an important factor in patients' recovery and quality of life. Pain is charted today as one of the vital signs. For standardization, pain is charted by a number from 0 to 10 indicating its level. The most common practiced pain assessment tool today is the VAS- Visual Analog Score (facial or numerical), by which the patient himself indicates the level of the pain he or she endures. It has been found that the correlation between the reported pain by the patient and the assessed pain by the caregivers or the medical personnel becomes poor as pain intensifies.
Objective assessment of anesthesia using the heart rate and its spectral analyses was done in the past. By using this modality, works on neonatal pain were conducted. In adults, works have shown that there is possibility to assess pain using this modality, though no repeated proof for its ability to detect pain was published.
We know that physiological signals such as ECG consist of mixtures of variety of patterns and phenomena accruing at different patterns and time points. Traditional analysis methods are designed and optimized to handle signals that include a single class of patterns such as pure harmonics or piece-wise constant functions. However, such basic operations that use a single representation method usually yield mediocre results when applied to real complex biological signals as ECG and EEG especially in the case where the Signal to Noise Ratio (SNR) is very low. Recent trends in digital signal processing (DSP) use the novel idea of merging several different representation methods to create a so called over-complete dictionary, examples of this approach include the Matching Pursuit algorithm and the Basis Pursuit algorithm. We intend to develop and apply the novel signal processing tools to the ECG signals during painful experience for the first time. We believe that such tools have the potential to provide much better insight of the signal basic components and their relation to pain.
Whereas temperature, pulse, respirations, and blood pressure are all objectively measured, pain is inherently subjective. Given this fundamental difficulty, it is no wonder that failure to properly assess pain is a common cause of its poor control and lack of treatment in patients in different settings (1). Moreover, pain is multidimensional, with "sensory-discriminatory, cognitive-evaluative, and affective-motivational" components (2), or in other words, it affects body, mind, and spirit, and its complexity makes it hard to measure (1). Since pain has been recognized as an important modality, influencing recovery and quality of life in patients, it has been termed "the fifth vital sign" and is now being evaluated and registered in the patients' charts during routine checkups. Pain itself is repeatedly rated by the patients and the caregivers or the medical personnel, it is followed and treated.
For routine standardization, pain is charted on a scale of 0(no pain) to 10 (worst pain possible). At pain intensities of 0 to 4 patients describe the interference with function as mild (as reflected in daily activities and mood), at 5 to 6 it is described as moderate, and at 7 to 10 as severe (2). For standard scaling, different assessment tools were developed. One-dimensional pain scales, in which the patient is asked to describe the intensity of pain, are the most used tool today (1). These are the VAS- Visual analog Score (e.g., the patient places a mark on a 10-cm line to indicate the intensity of pain; one end of the line is labeled "no pain" and the other "the worst possible pain"), the Numeric ("please rate the intensity of pain on a scale of 1 to 10") and the Categorical ("please rate the pain as none, mild, moderate, or severe"). These scales are reliable and valid and can be used in conjunction with the World Health Organization (WHO) analgesic ladder guideline. (3)
The one-dimensional intensity scales can be modified to produce a pain relief scale, a patient pain satisfaction scale, or a pain management index (4). Moreover, comprehensive multidimensional pain assessment tools such as the Brief Pain Inventory were developed to help pain management specialist's measure and assess the effect of pain on mood, activities, and quality of life—which one-dimensional tools cannot do. (2) These tools are more difficult for the patients and the medical personnel to complete and are usually not used in daily practice (1).
In recent years the growing interest in pain and its treatment has generated a greater number of studies for the assessment of these tools. The tools are basically regarded reliable but Grossman SA et al (5) have found that the intensity of pain expressed by patients on self-assessment scales correlates poorly with caregivers' assessments of pain, and the greater the intensity of the pain, the poorer the correlation between patient and caregiver. Moreover, patients, who can not communicate with the medical staff such as unconscious, or sedated patients, young pediatric patients or psychiatric or mentally retarded patients, are evaluated subjectively by the caregivers or the medical staff, this with wide variations which do not always assess correctly the pain status of the suffering patient. For this reason objective pain assessment tools have been suggested in recent past, for more accurate and comprehensive indication of this modality in patients in different settings including sedation and anesthesia.
Objective pain assessment:
Different mathematical tools, mostly the heart rate variability, the heart rate spectral analysis were studied to assess the depth of anesthesia and pain (6-7). Most of the works done on human patients were done on pain recognition in neonates ( ). Some works on adult patients have been done too, trying to define pain using the heart rate and its analysis. Since the acute effect of pain to increase heart rate is well known, it was hypothesized by Storella et al. (10), in their work that there may be adaptive effects of chronic pain on the autonomic regulation of the cardiovascular system that could be reversed by analgesia. They examined whether the acute relief of chronic pain affects heart rate variability and concluded their study, based on experimental data that acute relief of chronic pain is accompanied by an analgesia-specific increase in heart rate variability in many patients. Ray et al. in their work used this method for ECG signal processing and developed a new monitor for pain assessment during anesthesia (11). Using the heart rate signal, the R-R intervals and the spectral analysis were calculated. The output containing the information about the respiratory cycles from which the RSA (respiratory sinus arrhythmia) was drawn to which they assumed the level of consciousness was proportional. That study has demonstrated the feasibility of this method, but their article states many drawbacks and restriction for this system and clinically this method is not used today.
We know that physiological signals such as the ECG and the EEG consist of mixtures of variety of patterns and phenomena accruing at different patterns and different time points during each recording. Some occur over very short time intervals, others last longer and some repeat periodically. Another characteristic of these signals is the presence of a high leveled noise, both systematic and unsystematic.
Traditional analysis methods are designed and optimized to handle signals that include a single class of patterns such as pure harmonics (Fourier Representation/ dictionary) or piece-wise constant functions (Wavelets Representation/ dictionary), in simplified and unreal case, simple operations such as threshold calculations or filtering in the appropriate representation space, can be very effective for separation of signal and noise (denoising), decomposition into basic components, pattern detection and more. However, such basic operations that use a single representation method usually yield mediocre results when applied to real complex biological signals as mentioned above especially in the case where the Signal to Noise Ratio (SNR) is very low. Recent trends in digital signal processing (DSP) use the novel idea of merging several different representation methods to create a so called over-complete dictionary, examples of this approach include the Matching Pursuit algorithm, described by Mallat et al (12) and the Basis Pursuit algorithm, described by Donoho et. al (13).
The matching pursuit and the basic pursuit can achieve near-optimal solutions for different kinds of analyses of complex signals provided that the appropriate representation methods are used. We intend to develop and apply this novel signal processing tools to the ECG signals for the first time. We believe that such tools have the potential to provide much better insight of the signal basic components and their relation to different physiological states than the traditional analysis methods that are practiced today which are based on a single dictionary.
In this study we intend to apply the advanced supervised learning methods, developed by Elad (14) in order to adaptively generate optimal dictionaries and representation methods for ECG signals and use these dictionaries to develop highly effective over complete dictionary based analysis methods in order to separate the complex ECG signal into its basic components and noise. We then intend to apply advanced statistical and data mining techniques in order to relate the basic patterns of the ECG signals to the pain sampled in the designed groups.
In this study we intend to sample the ECG from two groups. The first group will comprise of 20 healthy young adults who voluntarily will be inflicted by thermal, pressure and neural stimuli pain using the Cold pressor test, (this study is to be authorized by the Soroka University Medical Center IRB Committee); these subjects will be VAS and ECG monitored before, during and after the thermal pain induction. It is important to state that this instrument inflicts thermal pain by an authorized protocol, which does not harm or produce any tissue damage.
Our hypothesis is that pain can be detected, discriminated from noise and diagnosed by routine ECG samplings using these processing techniques.
|Study Type ICMJE||Observational|
|Study Design ICMJE||Observational Model: Case Control
Time Perspective: Prospective
|Target Follow-Up Duration||Not Provided|
|Sampling Method||Non-Probability Sample|
|Intervention ICMJE||Not Provided|
|Study Group/Cohort (s)||Not Provided|
|Publications *||Not Provided|
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
|Recruitment Status ICMJE||Completed|
|Estimated Enrollment ICMJE||20|
|Completion Date||October 2008|
|Primary Completion Date||October 2008 (final data collection date for primary outcome measure)|
|Eligibility Criteria ICMJE||
|Ages||20 Years to 40 Years|
|Accepts Healthy Volunteers||Yes|
|Contacts ICMJE||Contact information is only displayed when the study is recruiting subjects|
|Location Countries ICMJE||Israel|
|NCT Number ICMJE||NCT00678262|
|Other Study ID Numbers ICMJE||SOR470008CTIL|
|Has Data Monitoring Committee||No|
|Responsible Party||Zvia Rudich MD, Soroka Medical Center Beer-Sheva, Israel|
|Study Sponsor ICMJE||Soroka University Medical Center|
|Collaborators ICMJE||Not Provided|
|Information Provided By||Soroka University Medical Center|
|Verification Date||September 2010|
ICMJE Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP