Validation of the 'Pelvic Pain Map': a new self-assessment tool for chronic pelvic pain localisation.

OBJECTIVE: To develop and validate the Pelvic Pain Map to fill a gap in the need for a localised body map of the pelvic region. PATIENTS AND METHODS: The Pelvic Pain Map incorporated input from 12 chronic pelvic pain experts across the United States, as well as patient feedback to assess face validity. Finalised diagrams are single, front-facing images of the male and female pelvis that incorporate both abdominal and perineal views. Assessment of test-retest reliability and construct (convergent and discriminant) validity was carried out on a retrospective cohort of patients with chronic pelvic pain syndrome (CPPS) who completed the maps from January 2022 to May 2022. Other measures used in the validation process consisted of the male and female forms of the Genitourinary Pain Index (GUPI) and the short form (six item) of the Pain Catastrophising Scale (PCS-6). RESULTS: Test-retest for individual map zones demonstrated moderate to excellent reliability (Cohen's kappa coefficients ranging from 0.28 to 0.64) and for total map zones demonstrated excellent reliability (intraclass correlation coefficient = 0.90). Convergent validity for individual map zones with location descriptors from the GUPI was strong (phi coefficients ranging from 0.26 to 0.79) and for total map zones was moderate (Spearman's correlation coefficient = 0.56). Discriminant validity for total map zones with separate, but related constructs from the GUPI and PCS-6 was weakly positive (Spearman's correlation coefficients ranging from 0.27 to 0.32). CONCLUSION: This study suggests that the Pelvic Pain Map is a valid and reliable tool for assessing location of pain in patients with CPPS. Our findings highlight the potential utility of the Pelvic Pain Map in guiding treatment selection and monitoring therapeutic response in patients with chronic pelvic pain.

Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps.

BACKGROUND: Pain is an unpleasant sensation that signals potential or actual bodily injury. The locations of bodily pain can be communicated and recorded by freehand drawing on 2D or 3D (manikin) surface maps. Freehand pain drawings are often part of validated pain questionnaires (eg, the Brief Pain Inventory) and use 2D templates with undemarcated body outlines. The simultaneous analysis of drawings allows the generation of pain frequency maps that are clinically useful for identifying areas of common pain in a disease. The grid-based approach (dividing a template into cells) allows easy generation of pain frequency maps, but the grid's granularity influences data capture accuracy and end-user usability. The grid-free templates circumvent the problem related to grid creation and selection and provide an unbiased basis for drawings that most resemble paper drawings. However, the precise capture of drawn areas poses considerable challenges in producing pain frequency maps. While web-based applications and mobile-based apps for freehand digital drawings are widely available, tools for generating pain frequency maps from grid-free drawings are lacking. OBJECTIVE: We sought to provide an algorithm that can process any number of freehand drawings on any grid-free 2D body template to generate a pain frequency map. We envisage the use of the algorithm in clinical or research settings to facilitate fine-grain comparisons of human pain anatomy between disease diagnosis or disorders or as an outcome metric to guide monitoring or discovery of treatments. METHODS: We designed a web-based tool to capture freehand pain drawings using a grid-free 2D body template. Each drawing consisted of overlapping rectangles (Scalable Vector Graphics elements) created by scribbling in the same area of the body template. An algorithm was developed and implemented in Python to compute the overlap of rectangles and generate a pain frequency map. The utility of the algorithm was demonstrated on drawings obtained from 2 clinical data sets, one of which was a clinical drug trial (ISRCTN68734605). We also used simulated data sets of overlapping rectangles to evaluate the performance of the algorithm. RESULTS: The algorithm produced nonoverlapping rectangles representing unique locations on the body template. Each rectangle carries an overlap frequency that denotes the number of participants with pain at that location. When transformed into an HTML file, the output is feasibly rendered as a pain frequency map on web browsers. The layout (vertical-horizontal) of the output rectangles can be specified based on the dimensions of the body regions. The output can also be exported to a CSV file for further analysis. CONCLUSIONS: Although further validation in much larger clinical data sets is required, the algorithm in its current form allows for the generation of pain frequency maps from any number of freehand drawings on any 2D body template.