AAPT Diagnostic Criteria for Chronic Abdominal, Pelvic, and Urogenital Pain: Irritable Bowel Syndrome

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

Vulvodynia

Vulvodynia is a condition that occurs in 8-10% of women of all ages and is characterized by pain at the vulva that is present during sexual and/or non-sexual situations. Diagnosis is established through careful medical history and pelvic examination, including the cotton-swab test. The onset and maintenance of vulvodynia involves a complex interplay of peripheral and central pain mechanisms, pelvic floor muscle and autonomic dysfunction, anxiety, depression and childhood maltreatment as well as cognitive-affective, behavioural and interpersonal factors. Given the absence of empirically supported treatment guidelines, a stepwise approach of pelvic floor physical therapy and cognitive behavioural therapy as well as medical management is suggested, with surgery as the last option. Vulvodynia has a negative effect on the quality of life of women and their partners, and imposes a profound personal and societal economic burden. In addition, women with vulvodynia are more likely to report other chronic pain conditions, which further alters their quality of life. Future efforts should aim to increase girls', women's and healthcare professionals' education and awareness of vulvodynia, phenotype different subgroups of women based on biopsychosocial characteristics among more diverse samples, conduct longitudinal studies and improve clinical trial designs.

Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions

Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.

Evidence for Somatic Hypersensitivity in Veterans With Gulf War Illness and Gastrointestinal Symptoms

INTRODUCTION

Over 25% of Persian Gulf War (PGW) veterans with Gulf War Illness (GWI) (chronic health symptoms of undetermined etiology) developed gastrointestinal (GI) (diarrhea and abdominal pain) and other somatic symptoms.

OBJECTIVES

Our study objective was to determine if veterans with GWI and GI symptoms exhibit heightened patterns of somatic pain perception (hypersensitivity) across nociceptive stimuli modalities.

METHODS

Participants were previously deployed GW Veterans with GWI and GI symptoms (n=53); veterans with GWI without GI symptom (n=47); and veteran controls (n=38). We determined pain thresholds for contact thermal, cold pressor, and ischemic stimuli.

RESULTS

Veterans with GWI and GI symptoms showed lower pain thresholds (P<0.001) for each stimulus. There was also overlap of somatic hypersensitivities among veterans with GI symptoms with 20% having hypersensitivity to all 3 somatic stimuli. Veterans with GWI and GI symptoms also showed a significant correlation between mechanical visual analog scale abdominal pain ratings and heat pain threshold, cold pressor threshold, and ischemic pain threshold/tolerance.

DISCUSSION

Our findings show that there is widespread somatic hypersensitivity in veterans with GWI/GI symptoms that is positively correlated with abdominal pain ratings. In addition, veterans with somatic hypersensitivity that overlap have the greatest number of extraintestinal symptoms. These findings may have a translational benefit: strategies for developing more effective therapeutic agents that can reduce and/or prevent somatic and GI symptoms in veterans deployed to future military conflicts.