setting up pain body diagram for pain assessment

A Novel Approach for Pain Assessment from Pain Body Diagrams

Chronic pain is a debilitating neuropsychiatric condition affecting over 50 million adults in the United States1. However, common clinical tools to track subjective pain intensity (such as the numeric rating scale [NRS] or visual analog scale [VAS]) are reductionistic and fail to communicate the complex nature of pain symptom intensity spanning somatosensory, cognitive, or affective domains2,3. Accurately tracking an individual&#39;s pain intensity is critical to the diagnosis of pain syndromes, monitoring disease progression, and assessing the potential efficacy of therapies such as medications or brain stimulation.
The widely used NRS pain intensity tool requires the subject to rate pain intensity as an integer value from 0-10, representing no pain to the worst possible pain. While easy to administer and understand, the NRS is limited by respondent anchoring bias, expectation bias, and variable interpretation of individual values4,5; these also limit between-participant comparisons. The VAS, a continuous scale from 0-100, may reduce the impact of anchoring but can still face similar limitations as those of NRS4. Several studies have demonstrated a high degree of agreement between the NRS and VAS for chronic lower back pain severity6,7 and clinical practice5, but consensus guidelines highlight the many shortcomings of relying on similar scales in clinical pain trial design or interpretation8,9. The short-form McGill pain questionnaire 2 (MPQ) further dissects the somatosensory and affective dimensions of pain using ratings of verbal descriptors10, to aid in distinguishing between sensory and affective pain dimension11. Although these pain rating scales are commonly used to track pain intensity12,13, they fail to capture detailed topographic information such as pain location, or intensity variation across body regions.
Pain body diagrams (PBDs) are an open-ended, free-form pain assessment tool allowing respondents to illustrate a visual representation of pain location and intensity on a schematic human body outline14,15. PBDs are an effective communication tool between participants and medical providers which help track pain symptoms longitudinally16. PBD&#39;s free-form graphical format may decrease anchoring bias. Recent modifications to PBDs, such as the introduction of sex-specific body diagrams, have increased their effectiveness as a communication tool by aligning the visually represented body form with the respondent&#39;s anatomy, thereby increasing self-identification and response accuracy17. Furthermore, the use of color to signify intensity has been shown to allow effective communication of pain symptoms overcoming cultural and language barriers. For instance, the colors white and red were most commonly selected to indicate no pain and severe pain, respectively, in a Hmong patient population18. While PBDs are an effective tool19,20, they have been limited by their qualitative nature.
The use of PBDs on digital tablets has substantially expanded the tools available for quantifying pain location and intensity. Barbero et al. quantified the pain extent or the number of pixels drawn within a PBD of patients with chronic lower back and neck pain and showed good test-retest reliability and significant correlation with VAS measures21. Body diagrams have also been analyzed to create pain frequency maps to show the most to least frequently painful areas of the body21,22. While these methods quantify spatial pain information, so far, no method has incorporated both pain intensity and location into composite metrics.
The following protocol demonstrates a method for obtaining novel, visually intuitive, colored PBDs and extracting three quantitative metrics that together reflect a composite of pain intensity and location information. To do this, five participants undergoing a research trial of deep brain stimulation (DBS) for refractory chronic neuropathic pain were selected to test the current approach, using a N-of-1 study design23. Participants were instructed to report the intensity of their momentary pain symptoms by applying varying levels of pen pressure on a tablet illustration application to produce color hues that corresponded to varying pain intensities at different body locations. PBD-derived metrics of coverage, sum intensity, and mean intensity were compared to more common validated pain metrics (i.e., NRS, VAS and MPQ) using statistical and mutual information (MI) analyses.
Over a 10-day inpatient hospital stay, patients undergoing evaluation completed PBDs (mean &#177; standard deviation (SD) = 121.8 &#177; 34.3 PBDs per patient; range 84-177; 609 PBDs total) in addition to validated pain scales such as the NRS, VAS, and MPQ multiple times daily. PBDs were collected via a tablet application and uploaded as time-stamped files to secured research servers when completed. Pain intensity NRS, VAS and MPQ were acquired using REDCap survey tools, a secure web application. Both surveys and PBDs were administered in person by research assistants to ensure patients received the needed assistance to complete their evaluations accurately. The following steps detail PBD setup, participant instruction, data collection, and PBD analysis used to reliably quantify pain (Figure 1).

Author Spotlight: Quantifying Pain Experience &amp;#8211; An Illustrative Approach Using the Pain Body Diagram 

Multimodal Ağrı Vücut Diyagramları ile Ağrı Yerini ve Yoğunluğunu Ölçme

Watch this Scientific Journal Video about Setting up Pain Body Diagram for Pain Assessment at JoVE.com

Setting up Pain Body Diagram for Pain Assessment  

Ağrı Değerlendirmesi için Ağrı Vücut Diyagramını Ayarlama

Başlamak için, görüntülenen hasta dahil etme kriterlerini kullanarak hastaları tarayın. Ardından, hem ön hem de arka vücut yüzeylerini gösteren cinsiyete uygun bir PBD şablonunu, basınca duyarlı bir çizim aracıyla donatılmış, dokunmaya duyarlı bir dijital tabletteki bir illüstrasyon uygulamasına aktarın. Şablonu tabletin fotoğraf kitaplığına indirip içe aktardıktan sonra, katmanlar simgesine ve ardından artı düğmesine tıklayarak katılımcı için PBD şablonunun üzerinde yeni bir katman oluşturun.Bu, biri PBD ile, diğeri ise ağrıyı gösteren renklerle çizim yapan katılımcılar için olmak üzere iki katmanla sonuçlanacaktır. Ardından, fırça stüdyosunu açmak için fırça kitaplığı simgesine ve artı düğmesine tıklayarak X, renk dönüştürme eğrisine Y basıncına eşit olan yeni bir fırça oluşturun. Renk dinamikleri düğmesini tıklatın ve renk basıncı bölümüne gidin.Ton kaydırıcısı için, basınç dönüştürme grafiğinde 45 derecelik düz bir çizgi elde etmek için sayısal yüzdeyi tıklatın. Yeşilden maviye ve kırmızıya ton gradyanı aralığını tanımlamak için, listelenen yüzde sayısını tıklatıp %81 sayısal değerini girerek renk basıncı bölümündeki ton kaydırıcısını ayarlayın Çalışma katılımcılarının ihtiyaçlarına uygun bir boyut seçmek için kalem aracı kaydırıcısını ayarlayın.

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Neuroscience

Setting up Pain Body Diagram for Pain Assessment

291 Görüntüleme.  University of California, San Francisco. Başlamak için, görüntülenen hasta dahil etme kriterlerini kullanarak hastaları tarayın. Ardından, hem ön hem de arka vücut yüzeylerini gösteren cinsiyete uygun bir PBD şablonunu, basınca duyarlı bir çizim aracıyla donatılmış, dokunmaya duyarlı bir dijital tabletteki bir illüstrasyon uygulamasına aktarın. Şablonu tabletin fotoğraf kitaplığına indirip içe aktardıktan sonra, katmanlar simgesine ve ardından artı düğmesine tıklayarak katılımcı için PBD şabl...

Video: Setting up Pain Body Diagram for Pain Assessment  

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

To quantify an individual&#39;s subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale (VAS), or McGill pain questionnaire (MPQ) are commonly used to assess pain on a numerical scale. However, these scales are often biased and fail to capture the complexity of pain experiences. In contrast, clinical practice often requires patients to report areas of pain by drawing on a body diagram, which is an effective but qualitative tool. The method presented here extracts quantifiable metrics from pain body diagrams (PBDs) which are validated against the NRS, VAS, and MPQ pain scales. By using a novel pressure-hue transformation on a digital tablet, different drawing pressures applied with a digital stylus can be represented as different hues on a PBD. This produces a visually intuitive diagram of hues ranging from green to blue to red, representing mild to moderate to most painful regions, respectively. To quantify each PBD, novel pain metrics were defined: (1) PBD mean intensity, which equals the sum of each pixel&#39;s hue value divided by the number of colored pixels, (2) PBD coverage, which equals the number of colored pixels divided by the total number of pixels on the body, and (3) PBD sum intensity, which equals the sum of all pixels&#39; hue values. Using correlation and information theory analyses, these PBD metrics were shown to have high concordance with standardized pain metrics, including NRS, VAS and MPQ. In conclusion, PBDs can provide novel spatial and quantitative information that can be repeatedly measured and tracked over time to comprehensively characterize a participant&#39;s pain experience.

Current pain scales used to quantify pain severity, such as visual analog scales, fail to capture the complexity of subjective pain experiences. Pain body diagrams are qualitative but may be more informative. The goal of this method is to extract quantitative metrics from pain body diagrams using novel pressure-hue transformation.

To quantify an individual&#39;s subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale ...

Nörobilim

To begin, screen the patients using the displayed patient inclusion criteria. Then, import a gender appropriate PBD template showing both front and back body surfaces into an illustration application on a touch sensitive digital tablet, equipped with a pressure sensitive drawing tool. After downloading the template to the tablet's photo library and importing it, create a new layer on top of the PBD template for the participant by clicking the layers icon and then the plus button.This will result in two layers, one with the PBD, and one for the participants drawing with colors indicating pain. Next, create a new brush with X equals Y pressure to hue transformation curve by clicking the brush library icon and the plus button to open the brush studio. Click the color dynamics button and scroll to the color pressure section.For the hue slider, click the numeric percentage to achieve a straight 45 degree line in the pressure transformation graph. To define the hue gradient range from green to blue to red, adjust the hue slider in the color pressure section by clicking the percentage number listed and entering a numeric value of 81%Adjust the pen tool slider to select a size suitable for the study participants'needs.

Measuring Pain Location and Intensity Through Pain Body Diagrams

Explain the pain body diagram or PBD anatomy and orientation of body templates in landscape mode. Describe the pressure to hue linear transformation by showing that increased stylus pressure shifts hues from green to blue to red, representing mild, moderate, and severe pain intensity respectively. Then explain the drawing and erasing tools and pinch to zoom and panning functionalities.Confirm the participant's understanding of the pain body diagram task using the teach back method and have them explain the process in their own words. Allocate 15 minutes for participants to practice drawing pain body diagrams on a flat surface, ensuring an accurate representation of pain location and intensity. Review the PBD with each participant to verify consistency and correct use of colors.Request participants to finish PBDs during baseline or at different time points following treatment or intervention, allowing ample time for PBD completion. After completion, save PBDs with a standardized file name, including the patient ID, date and time the PBD was finished. Select the desired images for bulk export using the select icon.Click on the share button to access a list of available image formats. Choose a portable or Photoshop file format that retains image layers and click on the preferred format. Download the desired PBD files and open them in a raster based image editor.Now, isolate the colored pixels of interest from the top layer of the PBD file by adding a completely black layer below the colored in layer and a black mask layer above it to exclude pixels outside the template body outline, creating a modified PBD with only the colored pixels within the body outline on a black background. To export the processed PBDs as portable network graphic files, click on file, select export, and choose export as, select PNG format, then click export. Convert each pixel value within the PBD from RGB to HSV color space using the open CV2 Python package.Extract the hue value for every pixel by executing the Python scripts titled, measure underscore SA noblur and RGBA2HSV. Run the quantify pain Python script to calculate the three PBD metrics. First, calculate the PBD coverage by dividing the number of colored pixels by the total number of pixels within the body diagram.The range for females is one to 820, 452 pixels, and for males it is one to 724, 608 pixels. Multiply the results by 100 to normalize data from a zero to 100 scale. Next, determine the PBD sum intensity by adding the hue values for all pixels In the body diagram, which is done by running RGBA2HSV.The female range is zero to 114, 453, 054, and the male range is zero to 101, 082, 816. Divide the result by the maximum PBD sum intensity and multiply by 100. The maximum PBD sum intensity depends on the total number of pixels in the body diagram multiplied by 139.5.Compute the PBD mean intensity by dividing the sum of all hue values by the total number of colored pixels. Finally, process each PBD file and compile the outputs in a spreadsheet for further analysis. The PBD metrics were correlated with the numerical rating intensity, or NRS, visual analog scale, or VAS, and McGill Pain Questionnaire, or MPQ for most patients.In four of five patients, PBD metrics significantly shared mutual information, or MI, with the NRS-VAS intensity, VAS unpleasantness and MPQ. In all patients, the NRS shared significant MI with VAS intensity, VAS unpleasantness and MPQ, while the PBD sum shared MI with PBD coverage and PBD mean.