Machine learning suggests sleep as a core factor in chronic pain

Abstract

Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. Unsupervised machine learning analysis of parameters "pain intensity", "number of pain areas", "pain duration", "activity pain interference" and "affective pain interference", implemented as emergent selforganizing maps, identified three patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the two extreme pain phenotype clusters. Also important were the parameters "fear of pain", "self-rated poor health", and "systolic blood pressure". Decision trees trained with this information assigned patients to the extreme pain-phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain.In this study, we used a data-driven approach in a cohort of patients with persistent pain of various causes to identify the most important attributes or problems specific to different pain phenotypes. In addition to pain phenotype-related factors, 59 further variables, representing factors recognized to be, and others possibly, associated with pain phenotypes were identified, including demographic factors, pain etiology, comorbidities, lifestyle factors, psychological variables, and treatment-related factors. These data were analyzed using unsupervised and supervised machine learning methods with the aim of (i) identifying and interpreting patterns arising from different pain phenotypic parameters and (ii) selecting among the further parameters those that were informative in associating a patient with a particular pain phenotype. Methods Study setting and designThis was an observational cohort study in chronic pain patients treated in three multidisciplinary tertiary pain clinics in Finland. A multi-center "KROKIETA" study was used to collect patient data on a broad scale, including socioeconomic factors, lifestyle factors, psychological variables, previous treatments, and biochemical indicators [98]. The study adheres to the STROBE guidelines

The Effect of Sleep Quality on Pain in Chilean Individuals with Musculoskeletal Disorders

“…For instance, total and partial sleep deprivation has been shown to interfere with pain processing, inducing hyperalgesia in pain-free subjects and individuals with MSD [ 5 , 7 ]. Furthermore, poor SQ can contribute to acute pain continuing, acting as a risk factor for developing chronic pain [ 8 , 9 ].…”

Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

“…Bayes [17,19,20,22] Estimates the probability of data patterns belonging to a specific class Boosting: functional data boosting (FDboost) [13]; gradient boosting (GB) [24,28]; extreme gradient boosting regression (XGBoost) [27,31] Merges weak classifiers into strong ones Deep learning neural network (DLNN) [10,11,14,16,18,34,35] Similarly to multiple linear regression it contains layers of interconnected nodes. A subclass of NN is the convolutional neural network (CNN)…”

“…Decision trees (DT) [14,22,29,34] Gradually reject classes assigned into multistage decision systems to accept a final class. In pain medicine, decision trees algorithms such as classification and regression trees have been used…”

“…k-means clustering [14] Divides a number of data points into a number of clusters based on the nearest mean k-nearest neighbors (kNN) [10,22,24,27,29,32] Assigns data patterns to a class on the basis of the distance to the training patterns of a certain class Multilayer perceptron (MLP) [9,22] Trains on a set of input data patterns to predict/classify the output class Random forest (RF) [14,15,28,29,31,32] Builds and merges multiple decision trees to provide a more accurate prediction Regression: kernel ridge regression (KRR), [30]; elastic net (EN) [23,28]; generalized linear mixed-models (GLMMs) based on repeated data points, Lasso [15,24]; least square (LS) [28]; linear regression (LiR) [27,33]; logistic regression (LoR) [10,15,29,31,32]; ridge regression (RR) [28] Predicts the probability of agreement using continuous data points Support vector machine (SVM) [9, 12, 15, 21, 22, 25-27, 29, 32, 34] Creates a hyperplane to separate two classes. The hyperplane is found by optimizing a cost function Multi-subject dictionary learning (MSDL) [16] It is a feature learning method where a training example is represented as a linear combination of basic functions, and is assumed to be a sparse matrix medical hypotheses, etc.…”

Insomnia Symptoms and Chronic Pain among Patients Participating in a Pain Rehabilitation Program—A Registry Study

“…The bidirectional relationship between sleep and pain is of special interest. Poor sleep is common in chronic pain patients and recent data identifies sleep problems as key factors in the patients with severe pain presentations [ 6 , 7 ]. In a large, recent cross sectional study investigating insomnia in Norwegian adults, the prevalence of insomnia in the complete cohort was 14% [ 8 ].…”