Persistent pain following breast cancer treatments remains a significant clinical problem despite improved treatment strategies. 1 Data on factors associated with persistent pain are needed to develop prevention and treatment strategies and to improve the quality of life for breast cancer patients. This prospective study examined the prevalence and severity of and the factors associated with chronic pain after breast cancer surgery and adjuvant treatments. Methods | Consecutive patients younger than 75 years with unilateral nonmetastasized breast cancer treated at the Helsinki University Central Hospital in 2006-2010 with either breastconserving surgery or mastectomy with axillary surgery were eligible. Patients receiving neoadjuvant treatment or immediate or delayed breast reconstruction or who had no breast cancer in the final histology were excluded. All women gave informed written consent. The ethics committee of the Helsinki University Central Hospital approved the study. Preoperatively, medical history, demographic data, Beck Depression Inventory, 2 and Spielberger State-Trait Anxiety Inventory 3 were obtained. Preoperative pain in the operative area (breast, axilla, arm) during the previous week was assessed with a numerical rating scale of 0 to 10 (0 = no pain; 1-3 = mild; 4-6 = moderate; ≥7 = severe). 4 Perioperative analgesia was standardized. All patients received acetaminophen and patient-controlled analgesia with intravenous oxycodone. Postoperatively, data were acquired on tumor and lymph node characteristics, complications of surgery, reoperations, and the prognostic risk category. 5 Adjuvant treatments were given according to international guidelines. 5 A questionnaire was sent to patients 12 months after surgery, with identical assessments of presence and intensity of pain. A bivariable analysis was conducted to determine factors related to pain at 12 months after surgery. The worst pain in any area was used for statistical analysis, in which pain was considered an ordinal variable. Variables with P < .10 in bivariable analyses were entered into an ordinal logistic regression analysis. All statistical tests were 2-sided with P < .05 considered statistically significant. SPSS Statistics version 20 (SPSS Inc) software was used.

Purpose Persistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort. Results Moderate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area ( P < .001), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

Supplemental Digital Content is Available in the Text.Longitudinal study of 251 patients with surgical intercostobrachial nerve resection. Other chronic pains, psychological burden, inflammatory markers, and central sensitization characterize chronic postsurgical neuropathic pain.

BackgroundPrevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.MethodsOver 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28–75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either “persisting pain” or “non-persisting pain” groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.ResultsA symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with “yes/no” items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.ConclusionsThe present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.Electronic supplementary materialThe online version of this article (10.1007/s10549-018-4841-8) contains supplementary material, which is available to authorized users.

We studied postoperative pain trajectories and associated factors. Expectation of severe postoperative pain was associated with higher intensity of experimental and postoperative pain.

Contact heat and cold pressure tests identify variability in pain sensitivity which is modified by factors such as anxiety, chronic pain, previous surgery, and smoking. High levels of anxiety are connected to increased pain sensitivity in experimental and acute postoperative pain.

No abstract

Background: Persistent pain in breast cancer survivors is common. Psychological and sleep-related factors modulate perception, interpretation and coping with pain and may contribute to the clinical phenotype. The present analysis pursued the hypothesis that breast cancer survivors form subgroups, based on psychological and sleep-related parameters that are relevant to the impact of pain on the patients' life. Methods: We analysed 337 women treated for breast cancer, in whom psychological and sleep-related parameters as well as parameters related to pain intensity and interference had been acquired. Data were analysed by using supervised and unsupervised machine-learning techniques (i) to detect patient subgroups based on the pattern of psychological or sleep-related parameters, (ii) to interpret the detected cluster structure and (iii) to relate this data structure to pain interference and impact on life. Results: Artificial intelligence-based detection of data structure, implemented as self-organizing neuronal maps, identified two different clusters of patients. A smaller cluster (11.5% of the patients) had comparatively lower resilience, more depressive symptoms and lower extraversion than the other patients. In these patients, life-satisfaction, mood, and life in general were comparatively more impeded by persistent pain. Conclusions: The results support the initial hypothesis that psychological and sleep-related parameter patterns are meaningful for subgrouping patients with respect to how persistent pain after breast cancer treatments interferes with their life. This indicates that management of pain should address more complex features than just pain intensity. Artificial intelligence is a useful tool in the identification of subgroups of patients based on psychological factors.