Development of a screening instrument for risk factors of persistent pain after breast cancer surgery

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Perioperative Risk Factors for Persistent Postsurgical Pain After Inguinal Hernia Repair: Systematic Review and Meta-Analysis

Persistent postsurgical pain (PPSP) is one of the most bothersome and disabling long-term complications after inguinal hernia repair surgery. Understanding perioperative risk factors that contribute to PPSP can help identify high-risk patients and develop risk-mitigation approaches. The objective of this study was to systematically review and meta-analyze risk factors that contribute to PPSP after inguinal hernia repair. The literature search resulted in 303 papers included in this review, 140 of which were used for meta-analyses. Our results suggest that younger age, female sex, preoperative pain, recurrent hernia, postoperative complications, and postoperative pain are associated with a higher risk of PPSP. Laparoscopic techniques reduce the PPSP occurrence compared to anterior techniques such as Lichtenstein repair, and tissue-suture techniques such as Shouldice repair. The use of fibrin glue for mesh fixation was consistently associated with lower PPSP rates compared to tacks, staples, and sutures. Considerable variability was observed with PPSP assessment and reporting methodology in terms of study design, follow-up timing, clarity of pain definition, as well as pain intensity or interference threshold. High or moderate risk of bias in at least one domain was noted in >75% of studies. These may limit the generalizability of our results. Future studies should assess and report comprehensive preoperative and perioperative risk factors for PPSP adjusted for confounding factors, and develop risk-prediction models to drive stratified PPSP-mitigation trials and personalized clinical decision-making. PERSPECTIVE: This systematic review and meta-analysis summarizes the current evidence on risk factors for persistent pain after inguinal hernia repair. The findings can help identify patients at risk and test personalized risk-mitigation approaches to prevent pain. PROSPERO REGISTRATION: htttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=154663.

Use of artificial intelligence in breast surgery: a narrative review

Background and Objective

We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application.

Methods

Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded.

Key Content and Findings

AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence.

Conclusions

Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.

Pain associated with breast cancer: etiologies and therapies

Pain associated with breast cancer is a prevalent problem that negatively affects quality of life. Breast cancer pain is not limited to the disease course itself but is also induced by current therapeutic strategies. This, combined with the increasing number of patients living with breast cancer, make pain management for breast cancer patients an increasingly important area of research. This narrative review presents a summary of pain associated with breast cancer, including pain related to the cancer disease process itself and pain associated with current therapeutic modalities including radiation, chemotherapy, immunotherapy, and surgery. Current pain management techniques, their limitations, and novel analgesic strategies are also discussed.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Risk factors for long-term arm morbidities following breast cancer treatments: A systematic review

PURPOSE

To examine the risk factors for arm morbidity following breast cancer treatments, taking a broad view of all types of physical morbidity, including prolonged pain, lymphedema, decreased range of motion, and functional limitations.

METHODS

A systematic literature review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. Studies exploring the risk factors for prolonged arm morbidity following breast cancer surgery and treatments were included. The studies were assessed independently according to pre-eligibility criteria, following data extraction and methodological quality assessment.

RESULTS

1,242 articles were identified. After removing duplicates, the full texts of 1,153 articles were examined. Sixty-nine of these articles met the criteria and were included in the review. These 69 articles identified 29 risk factors for arm morbidity following treatments for breast cancer. The risk of bias was evaluated using NIH study quality assessment tools. The studies reviewed were published between 2001 and 2021 and included a total of 22,886 patients who were followed up for between three months and 10 years.

CONCLUSIONS

The main risk factors for long-term morbidity are removal of lymph nodes from the axilla, body mass index >30, having undergone a mastectomy, the stage of the disease, radiation therapy, chemotherapy, infection and trauma to the affected arm after surgery. An understanding of the risk factors for prolonged arm morbidity after surgery can help doctors and therapists in making personalized decisions about the need and timing of rehabilitation treatments.

Postmastectomy Pain Syndrome: A Narrative Review

Postmastectomy pain syndrome is a very common disorder in breast cancer survivors. The impact on the quality of patients' lives is significantly adverse. The precise pathophysiology has not been determined as yet though various risk factors have been identified that make the patient vulnerable. Required preoperative work includes the identification and possible elimination of risk factors. Treatment is multidisciplinary involving surgical and non-surgical modalities. There is a great scope of research in this field.

Prognostic models for chronic postsurgical pain-Current developments, trends, and challenges

PURPOSE OF REVIEW

Prognostic models for chronic postsurgical pain (CPSP) aim to predict the likelihood for development and severity of CPSP in individual patients undergoing surgical procedures. Such models might provide valuable information for healthcare providers, allowing them to identify patients at higher risk and implement targeted interventions to prevent or manage CPSP effectively. This review discusses the latest developments of prognostic models for CPSP, their challenges, limitations, and future directions.

RECENT FINDINGS

Numerous studies have been conducted aiming to develop prognostic models for CPSP using various perioperative factors. These include patient-related factors like demographic variables, preexisting pain conditions, psychosocial aspects, procedure-specific characteristics, perioperative analgesic strategies, postoperative complications and, as indicated most recently, biomarkers. Model generation, however, varies and performance and accuracy differ between prognostic models for several reasons and validation of models is rather scarce.

SUMMARY

Precise methodology of prognostic model development needs advancements in the field of CPSP. Development of more accurate, validated and refined models in large-scale cohorts is needed to improve reliability and applicability in clinical practice and validation studies are necessary to further refine and improve the performance of prognostic models for CPSP.

Predictors of postoperative pain six months after breast surgery

Breast cancer, with a high prevalence and survival rate, leads to long-term complications. A major sequel is acute or chronic postoperative pain, and we investigated the possible relationship with clinical and psychological variables. Patients undergoing breast surgery filled out the loneliness (ULS-8) and depression (HADS) questionnaires. Patients rated their pain intensity with the Numerical Rating Scale (0-10, NRS) two days, seven days, and six months after surgery. Of 124 patients, the mean age was 45.86 years old, and the pain scores on the second and seventh postoperative days were 5.33 and 3.57, respectively. Sixth-month pain was significantly correlated with the acute scores with a mean of 3.27; and in the multivariate analysis, it was significantly associated with preoperative pain (p-value = 0.007), self-reported loneliness (p-value = 0.010), and adjuvant radiotherapy (p-value = 0.004). In conclusion, loneliness may be a risk factor for postoperative pain in breast surgery.

Prevalence and Predictors of Chronic Postsurgical Pain After Video-Assisted Thoracoscopic Surgery: A Systematic Review and Meta-analysis

INTRODUCTION

Determining the prevalence of chronic postsurgical pain (CPSP) after video-assisted thoracoscopic surgery (VATS) and identifying CPSP predictors should improve the prognosis of patients undergoing VATS. Although several studies have investigated predictors of CPSP after VATS, there were significant dissimilarities in the findings due to the confounding of predictors.

METHODS

PubMed, Cochrane, MEDLINE, Web of Science, Chinese Biomedical Literature, and China National Knowledge Infrastructure databases were comprehensively searched using the Medical Subject Headings terms "pain, postoperative," "thoracic surgery, video-assisted," and all related free terms from inception until March 27, 2022. The Stata metaprop package was used to comprehensively analyze the incidence of CPSP following VATS. Furthermore, the pooled odds ratios (OR) or the standardized mean differences (SMD) and their corresponding 95% confidence intervals (95% CI) were calculated, and qualitative analyses were performed for predictors that could not be assessed quantitatively to evaluate the effects of the included risk factors on the occurrence of CPSP. Unadjusted odds ratios were utilized to consider the impact of non-significant estimates if the original study did not report them.

RESULTS

Of the 4302 studies, 183 were considered eligible, and 17 were finally included in this study. The overall incidence of CPSP after VATS was 35.3% (95% CI 27.1-43.5%). The qualitative synthesis results revealed that female sex, age, and acute postoperative pain were definite predictors of CPSP after VATS. The number of ports, operation time, duration of drainage, and insufficient analgesia were also considered predictors. Consistent, quantitative synthesis results also showed that the aforementioned predictors were closely related to the occurrence of CPSP after VATS. Only by quantitative analysis, postoperative chemotherapy and an educational level less than junior school were also risk factors for CPSP. Other predictors displayed no evidence or unclear evidence of association with CPSP after VATS.

CONCLUSION

This study preliminarily determined the incidence of CPSP after VATS based on the existing literature. Female sex, age, and acute pain were identified as risk factors for CPSP after VATS, and other potential risk factors were also identified and analyzed. However, as a result of the inclusion of retrospective studies and inevitable limitations in this systematic review and meta-analysis, the results of this study still need to be verified by large-scale prospective clinical studies.

TRIAL REGISTRATION

CRD42022323179.

Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches

Purpose

This study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance.

Methods

The study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%). Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared.

Results

1152 patients were finally included, of which 22.1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention.

Conclusions

Our study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify high-risk patients and improve patients' management after breast cancer.

"Persistent Post-Operative Pain in Children - An Argument for a Transitional Pain Service in Pediatrics"

BACKGROUND

Persistent post-operative pain continues to occur in pediatric patients. Despite the growing amount of literature on causes, there is little discussion on treatment and prevention with a majority of studies focusing on specific surgeries.

AIM

The aim of this retrospective chart study is to identify risk factors and clinical features of persistent post-operative pain after any surgery in a pediatric quaternary complex pain service, describe the pharmacologic and non-pharmacologic management in children, and explore options to improve outcomes, in particular, the introduction of a transitional pain service.

METHODS

A retrospective chart review recorded demographics, gender, age, pain location and surgery type, treatment, and outcomes of 31 children identified through relevant registers over a 2-year period.

RESULTS

A total of 31 children were positively identified as having persistent post-operative pain and were seen in the complex pain clinic. Consistent with the literature, most cases represented neuropathic pain and persistent post-operative pain was most commonly seen after orthopedic surgery. All but four children had resolution of their pain after implementing the pain management plan. Management was characterized by early intervention, emphasis on non-pharmacologic strategies, and conservative use of opioids.

CONCLUSIONS

Identifying risk factors is useful, however putting strategies into place for prevention is necessary. Early intervention and the implementation of strategies before and immediately after are best provided by a transitional pain service.

Consensus statement on smoking cessation in patients with pain

Smoking is closely associated with the development of various cancers and tobacco-related illnesses such as cardiovascular and respiratory disorders. However, data are scarce on the relationship between smoking and both acute and chronic pain. In addition to nicotine, tobacco smoke contains more than 4000 different compounds. Although nicotine is not the sole cause of smoking-induced diseases, it plays a critical role in pain-related pathophysiology. Despite the acute analgesic effects of nicotine, long-term exposure leads to tolerance and increased pain sensitivity due to nicotinic acetylcholine receptor desensitization and neuronal plastic changes. The purpose of smoking cessation interventions in smoking patients with pain is primarily not only to reduce their pain and associated limitations in activities of daily living, but also to improve the outcomes of underlying pain-causing conditions and reduce the risks of tobacco-related disorders. This statement aims to summarize the available evidence on the impact of smoking on pain and to inform medical professionals of the significance of smoking cessation in patients with pain.

Development and Validation of a Prediction Model for Chronic Post-Surgical Pain After Thoracic Surgery in Elderly Patients: A Retrospective Cohort Study

Purpose

Chronic post-surgical pain (CPSP) is one of the adverse outcomes after surgery, especially in thoracotomy. However, the prevalence of CPSP in elderly adults (≥65 years), is still limited. Therefore, the present study was undertaken to establish and validate the prediction model of CPSP in those patients after thoracic surgery, including thoracotomy and video-assisted thoracoscopic surgery.

Patients and Methods

This retrospective, observational single-center cohort study was conducted in Nanfang Hospital, Southern Medical University, which randomly and consecutively collected 577 elderly patients who underwent thoracic surgery between January 1, 2017, and December 31, 2020. According to the Akaike information criterion, the prediction model was built based on all the data and was validated by calibration with 500 bootstrap samples.

Results

The mean age of participants was 69.09±3.80 years old, and 63.1% were male. The prevalence of CPSP was 26.9%. Age more than 75 years, BMI, blood loss, longer length of hospital stays, and higher pre-operative neutrophil count were associated with CPSP. Except for these factors, we incorporated history of drinking to build up the prediction model. The areas under the curve (AUCs) of the prediction models were 0.66 (95% CI, 0.61–0.71) and 0.64 (95% CI, 0.59–0.69) in the observational and validation cohorts, respectively. And the calibration curve of the predictive model showed a good fit between the predicted risk of CPSP and observed outcomes in elderly patients.

Conclusion

The present developed model may help clinicians to find high-risk elderly patients with CPSP after thoracic surgery and take corresponding measures in advance to reduce the incidence of CPSP and improve their life quality.

Abstract No. : ABS2524 : Preventive strategies in chronic persistent postsurgical pain following breast cancer Surgery- A systematic review and meta-analysis,

Background and Aims:

Chronic persistent postsurgical pain(CPCP) following breast cancer surgery is associated with decreased health-related quality of life and additional psychological distress.This meta-analysis aimed to evaluate the effect of various preventive strategies used for reducing the chronic persistent post-surgical pain following breast cancer surgery.

Methods:

An extensive search was conducted on online databases like PubMed, Google Scholar, Science Direct, and Embase. All the relevant randomised controlled trials(RCT) that are published up to June 2021 were included in this review. Patients with breast cancer who have undergone lumpectomy with or without axillary lymph node dissection and modified radical mastectomy were included in the study. The primary outcome measure was pain intensity. Incidence of chronic pain at 3 months using a verbal rating scale (VRS), visual analog scale (VAS), and numerical rating scales (NRS) were compared with control groups. The association between the primary outcome and preventive strategies was assessed using relative risk.

Results:

Incidence of CPSP at 3-month was assessed by 21 RCTs. This meta-analysis included 1610 patients (804 in intervention groups and 806 controls). Intervention reduced the incidence of CPSP at 3-month by 37% as compared to the control group and this reduction was statistically significant [Relative risk: 0.64: 0.54-0.75; p<0.001; I²=24.2%]. There was no publication bias and low heterogeneity among the studies (figure 1).

Conclusion:

In patients undergoing breast cancer surgery, intervention in the form of block or drug reduces the incidences of CPSP at three months post-surgery

Risk factors for persistent pain after breast and thoracic surgeries: a systematic literature review and meta-analysis

ABSTRACT

Persistent postsurgical pain (PPSP) is common after breast and thoracic surgeries. Understanding which risk factors consistently contribute to PPSP will allow clinicians to apply preventive strategies, as they emerge, to high-risk patients. The objective of this work was to systematically review and meta-analyze the literature on risk factors of PPSP after breast and thoracic surgeries. A systematic literature search using Ovid Medline, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, and Scopus databases was conducted. Study screening with inclusion and exclusion criteria, data extraction, and risk of bias assessment was performed independently by 2 authors. The data for each surgical group were analyzed separately and meta-analyzed where possible. The literature search yielded 5584 articles, and data from 126 breast surgery and 143 thoracic surgery articles were considered for meta-analysis. In breast surgery, younger age, higher body mass index, anxiety, depression, diabetes, smoking, preoperative pain, moderate to severe acute postoperative pain, reoperation, radiotherapy, and axillary lymph node dissection were the main factors associated with higher risk of PPSP. In thoracic surgery, younger age, female sex, hypertension, preoperative pain, moderate to severe acute postoperative pain, surgical approach, major procedure, and wound complications were associated with PPSP. This systematic review demonstrated certain consistent risk factors of PPSP after breast and thoracic surgeries, as well as identified research gaps. Understanding the factors that increase susceptibility to PPSP can help selectively allocate resources to optimize perioperative care in high-risk patients and help develop targeted, risk-stratified interventions for PPSP prevention.

A comprehensive approach to risk factors for upper arm morbidities following breast cancer treatment: a prospective study

BACKGROUND

Breast cancer surgery frequently causes deficiencies in shoulder functioning. The study pourpode is to identify risk factors for prolonged pain, reduction in function, and decrease in range of motion (ROM) in BC patients.

METHODS

A prospective cohort study was designed in a private hospital; between October 2018 and April 2019 with a follow-up of 6 months. Patients following BC surgery, were divided by arm morbidities, and the different risk facrors were evaluated using univariate analysis and logistic regression.

RESULTS

A total of 157 patients were included in the study. Risk factors for functional disabilities included; pain levels during hospitalization NPRS 1.2 (±0.8) compared to patients with no disabilities 0.5 ± 0.7 (p = .006), the size of tumors more than 1.4 ± 0.8 cm. compared with no morbidities 0.8 ± 0.9 cm. (p = .046), and breast reconstructions (p = .030). Risk factors for prolonged pain includes mastectomy (p = .006), breast reconstruction (p = .011), more than three dissected lymph nodes (p = .002), the presence of preoperative pain (p < .001), in-hospital pain (p < .001), axillary web syndrome (p < .001) and lymphedema (p < .001). Risk factors for decreased ROM were more than three dissected lymph nodes (p = .027), radiation (p = .018), and the size of dissected tissue (p = .035). Postoperative physical therapy appears to reduce the incidence of prolonged pain (p = .013) and regular physical activity may reduce long term functional disabilities (p = .021).

CONCLUSIONS

Upper arm morbidity following BC treatments affect up to 70% of the patients. Identifying the different risk and beneficial factors may improve awareness among physicians to refer patients to early rehabilitation programs and thus avoid chronic morbidity and improve the course of recovery.

TRIAL REGISTRATION

The study was registered in Clinical trial with the ID number: NCT03389204 .

Prognostic prediction models for chronic postsurgical pain in adults: a systematic review

ABSTRACT

Chronic postsurgical pain (CPSP) affects an estimated 10% to 50% of adults depending on the type of surgical procedure. Clinical prediction models can help clinicians target preventive strategies towards patients at high risk for CPSP. Therefore, the objective of this systematic review was to identify and describe existing prediction models for CPSP in adults. A systematic search was performed in MEDLINE, Embase, PsychINFO, and the Cochrane Database of Systematic Reviews in March 2020 for English peer-reviewed studies that used data collected between 2000 and 2020. Studies that developed, validated, or updated a prediction model in adult patients who underwent any surgical procedure were included. Two reviewers independently screened titles, abstracts, and full texts for eligibility; extracted data; and assessed risk of bias using the Prediction model Risk of Bias Assessment Tool. The search identified 2037 records; 28 articles were reviewed in full text. Fifteen studies reporting on 19 prediction models were included; all were at high risk of bias. Model discrimination, measured by the area under receiver operating curves or c-statistic, ranged from 0.690 to 0.816. The most common predictors identified in final prediction models included preoperative pain in the surgical area, preoperative pain in other areas, age, sex or gender, and acute postsurgical pain. Clinical prediction models may support prevention and management of CPSP, but existing models are at high risk of bias that affects their reliability to inform practice and generalizability to wider populations. Adherence to standardized guidelines for clinical prediction model development is necessary to derive a prediction model of value to clinicians.

Pain in the Cancer Survivor

Recent decades have demonstrated significant strides in cancer screening, diagnostics and therapeutics. As such there have been dramatic changes in survival following a diagnosis of cancer.

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

Prevalence and predictors of chronic postsurgical pain after colorectal surgery: A prospective study

AIM

The aim was to investigate the incidence, characteristics and the potential predictors of chronic postsurgical pain (CPSP) after colorectal surgery.

METHOD

Patients who underwent colorectal surgery at our institution from July 2014 to December 2016 were prospectively enrolled in this study. Perioperative potential demographic, clinical and psychological predictors for CPSP were collected. The follow-up visits were conducted through telephone interviews at 3 and 6 months postoperatively. The interview questionnaire comprised items regarding pain intensity, frequency, site, analgesic administration and impact on activities of daily living.

RESULTS

A total of 624 patients completed the 6-month follow-up and were included in the analysis. CPSP was reported by 32.1% of these patients at 3 months and 21.8% at 6 months after colorectal surgery. The pain interfered with several activities of daily living in a considerable proportion of CPSP cases. At 3 months, the identified predictors of CPSP were young age, preoperative abdominal pain and pain elsewhere, preoperative anxiety and depression, longer duration of surgery, and high pain intensity on movement within 24 h after surgery. Predictors of CPSP at 6 months were young age, preoperative abdominal pain, preoperative anxiety, longer duration of surgery, and high pain intensity on movement within 24 h after surgery.

CONCLUSION

Chronic pain after colorectal surgery is a common complication associated with young age, preoperative abdominal pain and pain elsewhere, preoperative anxiety and depression, longer duration of surgery, and high pain intensity on movement within 24 h after surgery.

Breast reconstruction after breast cancer surgery - persistent pain and quality of life 1-8 years after breast reconstruction

OBJECTIVES

To assess the long-term outcome of breast reconstructions with special focus on chronic postsurgical pain (CPSP) in a larger cohort of breast cancer survivors.

METHODS

A cross-sectional study on 121 women with mastectomy and breast reconstruction after mean 2 years 4 months follow up. The mean time from breast reconstruction to the follow-up visit was 4 years 2 months. We studied surveys on pain (Brief Pain Inventory, BPI and Douleur Neuropathique 4, DN4), quality of life (RAND-36 health survey), sleep (insomnia severity questionnaire, ISI), mood (Beck's Depression Index, BDI; Hospital Anxiety and Depression Scale, HADS), and a detailed clinical sensory status. Patients were divided into three groups: abdominal flap (Deep inferior epigastric perforator flap, DIEP; Free transverse rectus abdominis flap, fTRAM, and Pedicled transverse rectus abdominis flap, pTRAM), dorsal flap (Latissimus dorsi flap, LD and Thoracodorsal artery perforator flap, TDAP), and other (Transverse myocutaneous gracilis flap, TMG; implant). Clinically meaningful pain was defined ≥ 4/10 on a numeric rating scale (NRS). We used patients' pain drawings to localize the pain. We assessed preoperative pain NRS from previous data.

RESULTS

106 (87.6%) of the patients did not have clinically meaningful persistent pain. We found no statistically significant difference between different reconstruction types with regards to persistent pain (p=0.40), mood (BDI-II, p=0.41 and HADS A, p=0.54) or sleep (p=0.14), respectively. Preoperative pain prior to breast reconstruction surgery correlated strongly with moderate or severe CPSP.

CONCLUSIONS

Moderate to severe CPSP intensity was present in 14% of patients. We found no significant difference in the prevalence of pain across different reconstruction types. Preoperative pain associated significantly with postoperative persistent pain.

Perioperative factors associated with persistent post-surgical pain after hysterectomy, cesarean section, prostatectomy, and donor nephrectomy: a systematic review and meta-analysis

ABSTRACT

Persistent postsurgical pain (PPSP) is a common, and often disabling postoperative morbidity, but many questions remain about factors associated with PPSP. This systematic review and meta-analysis aimed to identify preoperative, intraoperative and postoperative factors associated with PPSP after gynecological surgeries, namely hysterectomy and cesarean section (C-section), and urological surgeries, namely prostatectomy and donor nephrectomy. Overall, 18 gynecological surgery studies, 4 prostatectomy studies, and 2 donor nephrectomy studies met the review criteria providing data that could be meta-analyzed. Average (±SD) PPSP occurrence after gynecological surgery was 20±11%; factors associated with increased risk of PPSP included smoking, preoperative abdominal or pelvic pain, preoperative pain elsewhere in the body, longer duration of surgery, more intense acute postoperative pain, and surgical wound infection. The use of neuraxial anesthesia was associated with decreased PPSP risk. Average PPSP occurrence was 20±9% after prostatectomy and 15±2% after donor nephrectomy. For urological procedures, the existing data did not allow for identification of significant factors associated with PPSP, except for laparoscopic and hand assisted laparoscopic approaches that were associated with lower incidence of PPSP for donor nephrectomy, and the use of neuraxial anesthesia which was associated with lower incidence of PPSP after prostatectomy. PPSP after gynecological and urological surgeries is common. This systematic review identified important factors associated with C-section and hysterectomy that can help identify women who are at high risk of PPSP. More high-quality studies with consistent methodology are needed to understand the factors associated with PPSP risk, particularly for surgeries such as prostatectomy and nephrectomy.

Psychological Factors Are Associated with Pain at All Time Frames After Breast Cancer Surgery: A Systematic Review with Meta-Analyses

OBJECTIVE

This systematic review aimed to 1) assess associations between psychological factors and pain after breast cancer (BC) treatment and 2) determine which preoperative psychological factors predicted pain in the acute, subacute, and chronic time frames after BC surgery.

DESIGN

A systematic review with meta-analysis.

SUBJECTS

Women with early-stage BC.

METHODS

The Medline, EMBASE, CINAHL, and Web of Science databases were searched between 1990 and January 2019. Studies that evaluated psychological factors and pain after surgery for early-stage BC were included. Associations between psychological factors and pain, from early after surgery to >12 months after surgery, were extracted. Effect size correlations (r equivalents) were calculated and pooled by using random-effects meta-analysis models.

RESULTS

Of 4,137 studies, 47 were included (n = 15,987 participants; 26 studies ≤12 months after surgery and 22 studies >12 months after surgery). The majority of the studies had low to moderate risk of bias. Higher preoperative anxiety and depression were weak but significant predictors of pain at all time points up to 12 months (r equivalent: 0.15-0.22). Higher preoperative pain catastrophizing and distress were also weak but significant predictors of pain during the acute (0-7 days) and chronic (3-12 months) periods (r equivalent: 0.10-0.20). For the period >12 months after surgery, weak but significant cross-sectional associations with pain were identified for anxiety, depression, pain catastrophizing, and distress (r equivalents: 0.15, 0.17, 0.25, 0.14, respectively).

CONCLUSION

Significant pooled effect size correlations between psychological factors and pain were identified across all time frames. Though weak, these associations should encourage assessment of key psychological factors during preoperative screening and pain assessments at all postoperative time frames.

Data-science-based subgroup analysis of persistent pain during 3 years after breast cancer surgery: A prospective cohort study

BACKGROUND

Persistent pain extending beyond 6 months after breast cancer surgery when adjuvant therapies have ended is a recognised phenomenon. The evolution of postsurgery pain is therefore of interest for future patient management in terms of possible prognoses for distinct groups of patients to enable better patient information.

OBJECTIVE(S)

An analysis aimed to identify subgroups of patients who share similar time courses of postoperative persistent pain.

DESIGN

Prospective cohort study.

SETTING

Helsinki University Hospital, Finland, between 2006 and 2010.

PATIENTS

A total of 763 women treated for breast cancer at the Helsinki University Hospital.

INTERVENTIONS

Employing a data science approach in a nonredundant reanalysis of data published previously, pain ratings acquired at 6, 12, 24 and 36 months after breast cancer surgery, were analysed for a group structure of the temporal courses of pain. Unsupervised automated evolutionary (genetic) algorithms were used for patient cluster detection in the pain ratings and for Gaussian mixture modelling of the slopes of the linear relationship between pain ratings and acquisition times.

MAIN OUTCOME MEASURES

Clusters or groups of patients sharing patterns in the time courses of pain between 6 and 36 months after breast cancer surgery.

RESULTS

Three groups of patients with distinct time courses of pain were identified as the best solutions for both clustering of the pain ratings and multimodal modelling of the slopes of their temporal trends. In two clusters/groups, pain decreased or remained stable and the two approaches suggested/identified similar subgroups representing 80/763 and 86/763 of the patients, respectively, in whom rather high pain levels tended to further increase over time.

CONCLUSION

In the majority of patients, pain after breast cancer surgery decreased rapidly and disappeared or the intensity decreased over 3 years. However, in about a tenth of patients, moderate-to-severe pain tended to increase during the 3-year follow-up.

Chronic Breast Pain Prior to Breast Cancer Surgery Is Associated with Worse Acute Postoperative Pain Outcomes

Acute postoperative pain is associated with adverse short and long-term outcomes among women undergoing surgery for breast cancer. Previous studies identified preexisting pain as a predictor of postoperative pain, but rarely accounted for pain location or chronicity. This study leveraged a multinational pain registry, PAIN OUT, to: (1) characterize patient subgroups based on preexisting chronic breast pain status and (2) determine the association of preexisting chronic pain with acute postoperative pain-related patient-reported outcomes and opioid consumption following breast cancer surgery. The primary outcome was a composite score comprising the mean of pain intensity and pain interference items from the International Pain Outcomes Questionnaire. The secondary outcome was opioid consumption in the recovery room and ward. Among 1889 patients, we characterized three subgroups: no preexisting chronic pain (n = 1600); chronic preexisting pain elsewhere (n = 128) and; chronic preexisting pain in the breast with/without pain elsewhere (n = 161). Controlling for covariates, women with preexisting chronic breast pain experienced more severe acute postoperative pain and pain interference (β = 1.0, 95% CI = 0.7-1.3, p < 0.001), and required higher doses of opioids postoperatively (β = 2.7, 95% CI = 0.6–4.8, p = 0.013). Preexisting chronic breast pain may be an important risk factor for poor pain-related postoperative outcomes. Targeted intervention of this subgroup may improve recovery.

The incidence of chronic pain following Cesarean section and associated risk factors: A cohort of women followed up for three months

BACKGROUND

Chronic post-surgical pain (CPSP) is one of the post-surgical complications of a Cesarean section. Despite the high rates of Cesarean section worldwide, the incidence of CPSP and the risk factors for this condition remain relatively unknown. The objective of this study was to calculate the incidence of CPSP in women submitted to Cesarean section and to analyze the associated risk factors.

MATERIALS AND METHODS

A prospective cohort of 621 women undergoing Cesarean section was recruited preoperatively. Potential presurgical (sociodemographic, clinical and lifestyle-related characteristics) and post-surgical risk factors (the presence and intensity of pain) risk factors were analyzed. Pain was measured at 24 hours and 7, 30, 60 and 90 days after surgery. Following discharge from hospital, data were collected by telephone. The outcome measure was self-reported pain three months after a Cesarean section. The risk factors for chronic pain were analyzed using the log-binomial regression model (a generalized linear model).

RESULTS

A total of 462 women were successfully contacted 90 days following surgery. The incidence of CPSP was 25.5% (95%CI: 21.8-29.7). Risk factors included presurgical anxiety (adjusted relative risk [RR] 1.03; 95%CI: 1.01-1.05), smoking (adjusted RR 2.22; 95%CI: 1.27-3.88) and severe pain in the early postoperative period (adjusted RR 2.79; 95%CI: 1.29-6.00).

CONCLUSION

One in four women submitted to Cesarean section may develop CPSP; however, the risk factors identified here are modifiable and preventable. Preventive strategies directed towards controlling anxiety, reducing smoking during pregnancy and managing pain soon after hospital discharge are recommended.

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery

Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoing breast cancer surgery, assigned based on a 3-year follow-up to either a persistent or nonpersistent pain phenotype, next-generation sequencing was performed for 77 genes selected for known functional involvement in persistent pain. Applying machine-learning and item categorization techniques, 21 variants in 13 different genes were found to be relevant to the assignment of a patient to either the persistent pain or the nonpersistent pain phenotype group. In descending order of importance for correct group assignment, the relevant genes comprised DRD1, FAAH, GCH1, GPR132, OPRM1, DRD3, RELN, GABRA5, NF1, COMT, TRPA1, ABHD6, and DRD4, of which one in the DRD4 gene was a novel discovery. Particularly relevant variants were found in the DRD1 and GPR132 genes, or in a cis-eCTL position of the OPRM1 gene. Supervised machine-learning-based classifiers, trained with 2/3 of the data, identified the correct pain phenotype group in the remaining 1/3 of the patients at accuracies and areas under the receiver operator characteristic curves of 65% to 72%. When using conservative classical statistical approaches, none of the variants passed α-corrected testing. The present data analysis approach, using machine learning and training artificial intelligences, provided biologically plausible results and outperformed classical approaches to genotype-phenotype association.

Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning

INTRODUCTION

Neuropathic pain (NP) remains a major debilitating condition affecting more than 26% of breast cancer survivors worldwide. NP is diagnosed using a validated 10-items Douleur Neuropathique - 4 screening questionnaire which is administered 3 months after surgery and requires patient-doctor interaction. To develop an effective prognosis model admissible soon after surgery, without the need for patient-doctor interaction, we sought to [1] identify specific pain characteristics that can help determine which patients may be susceptible to NP after BC surgery, and 2) assess the utility of machine learning models developed in objective [1] as a knowledge discovery tool for downstream analysis.

METHODS

The dataset is from a prospective cohort study of female patients scheduled to undergo breast cancer surgery for the first time at the Jewish General Hospital, Montreal, Canada between November 2014 and March 2019. NP was assessed at 3 months after surgery using Douleur Neuropathique - 4 interview scores (in short, DN4-interview; range: 0-7). For the primary analysis, we constructed six ML algorithms (least square, ridge, elastic net, random forest, gradient boosting, and neural net) to identify the most relevant predictors for DN4-interview score; and compared model performance based on root mean square error (RMSE). For the secondary analysis, we built a logistic classification model for neuropathic pain (DN4-interview score ≥ 3 versus DN4-interview score < 3) using the relevant-consensus-predictors from the primary analysis.

RESULTS

Anxiety, type of surgery, preoperative baseline pain and acute pain on movement were identified as the most relevant predictors for DN4 - interview score. The least square regression model (RMSE = 1.43) is comparable in performance with random forest (RMSE = 1.39) and neural network model (RMSE = 1.50). The Gradient boosting model (RMSE = 1.16) outperformed the models compared including the penalized regression models (ridge regressions, RMSE = 1.28; and elastic net, RMSE = 1.31). In the secondary analysis, the preferred logistic regression classier for NP had an area under the curve (AUC) of 0.68 (95% CI = 0.57 to 0.79). Anxiety was significantly associated with the likelihood of NP (odds ratio = 2.18; 95% CI = 1.05-4.49). In comparison to their counterparts, the odds of NP were higher in participants with acute pain on movement or with present preoperative baseline pain or participants who performed total mastectomy surgery, but the differences were not statistically significant.

CONCLUSIONS

Modern machine learning models show improvements over traditional least square regression in predicting of DN4-interview score. Penalized regression methods and the Gradient boosting model out-perform other models. As a predictor discovery tool, machine learning algorithms identify relevant predictors for DN4-interview score that remain statistically significant indicators of neuropathic pain in the classification model. Anxiety, type of surgery and acute pain on movement remain the most useful predictors for neuropathic pain.

A comparative study of pain experienced during successive mammography examinations in patients with a family history of breast cancer and those who have had breast cancer surgery

INTRODUCTION

To measure mammography-related pain in two groups of women undergoing regular surveillance as a baseline for future care.

METHODS

Following ethical approval, two hundred and forty two women aged 32-84 years (mean 54), were invited by written invitation to participate in the study. Two hundred women accepted the invitation, 100 women had a family history (FH) of breast cancer, 100 had undergone conservative surgery (FU) for breast cancer and were currently asymptomatic. A validated pain scale was used to score the participants' perceived pain before compression based on memory, immediately after compression and one week later. A series of baseline parameters were also captured including compression force, breast size/density, menstrual history and any adverse events following mammography to allow the investigation of relationships.

RESULTS

There was a strong correlation (r = 0.79, p < 0.001) between previous pain scores and current pain scores, no significant correlations were found between breast size, breast density or total compression force and pain. Pain scores reduced between previous and current examinations and there was consistency in overall pain scores, despite variations in the compression forces applied.

CONCLUSION

Physical side effects from mammography can develop and extend beyond the examination period. Patients' prior experience of pain was the only significant predictor of current pain in this study.

IMPLICATIONS FOR PRACTICE

Data on past mammography experiences are essential to improve future pain outcomes. Post-mammography aftercare should be a routine feature of the examination.

Machine-learned analysis of global and glial/opioid intersection-related DNA methylation in patients with persistent pain after breast cancer surgery

BACKGROUND

Glial cells in the central nervous system play a key role in neuroinflammation and subsequent central sensitization to pain. They are therefore involved in the development of persistent pain. One of the main sites of interaction of the immune system with persistent pain has been identified as neuro-immune crosstalk at the glial-opioid interface. The present study examined a potential association between the DNA methylation of two key players of glial/opioid intersection and persistent postoperative pain.

METHODS

In a cohort of 140 women who had undergone breast cancer surgery, and were assigned based on a 3-year follow-up to either a persistent or non-persistent pain phenotype, the role of epigenetic regulation of key players in the glial-opioid interface was assessed. The methylation of genes coding for the Toll-like receptor 4 (TLR4) as a major mediator of glial contributions to persistent pain or for the μ-opioid receptor (OPRM1) was analyzed and its association with the pain phenotype was compared with that conferred by global genome-wide DNA methylation assessed via quantification of the methylation in the retrotransposon LINE1.

RESULTS

Training of machine learning algorithms indicated that the global DNA methylation provided a similar diagnostic accuracy for persistent pain as previously established non-genetic predictors. However, the diagnosis can be based on a single DNA based marker. By contrast, the methylation of TLR4 or OPRM1 genes could not contribute further to the allocation of the patients to the pain-related phenotype groups.

CONCLUSIONS

While clearly supporting a predictive utility of epigenetic testing, the present analysis cannot provide support for specific epigenetic modulation of persistent postoperative pain via methylation of two key genes of the glial-opioid interface.

Identification of Functioning Domains in the Presurgical Period and Their Relationships with Opioid Use and Pain Catastrophizing

OBJECTIVES

The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) is a multidimensional screening system evaluating biopsychosocial factors affecting pain and functioning. Using a military sample, the current study 1) examined the structure and domains of the PROMIS, the Defense and Veterans Pain Rating Scale 2.0 (DVPRS), and the Pain Catastrophizing Scale (PCS) within a presurgical setting and 2) examined the relationship of these variables to pre- and postsurgical opioid use.

METHODS

This cross-sectional study included 279 adult patients scheduled for surgery at the Walter Reed National Military Medical Center and a validation sample of 79 additional patients from the Naval Medical Center, San Diego. PROMIS, DVPRS, PCS, and opioid use data were collected before surgery. Exploratory factor analysis and confirmatory factor analysis identified the latent structure for the measures. A structural equation model (SEM) examined their relationship to pre- and postsurgical opioid use.

RESULTS

Two latent factors represented Psychosocial Functioning (PROMIS Depression, PROMIS Anxiety, and PROMIS Social Isolation) and Pain Impact (DVPRS, PROMIS Pain Interference, PROMIS Physical Functioning). The remaining PROMIS scales did not load onto a single factor. In the SEM, the two latent factors and PCS were significantly related to pre- and postsurgical opioid use.

CONCLUSIONS

This study highlights the utility and relative ease of using a convenient multidimensional assessment in presurgical settings. Using such an assessment can help provide targeted interventions for individuals who may be at greatest risk for negative postsurgical outcomes.

Development of a Simple Preoperative Risk Score for Persistent Pain After Breast Cancer Surgery: A Prospective Observational Cohort Study

OBJECTIVES

Persistent postoperative pain is reported by 30% to 50% of patients following breast cancer surgery. Studies testing preventive measures, however, have so far failed to produce consistent positive results. If preventive measures could be targeted to a subgroup of patients at high risk of persistent pain, positive results would be more likely. Our aim was to develop a simple risk score predicting persistent pain after breast cancer surgery.

MATERIALS AND METHODS

In a prospective observational cohort study, we tested the predictive ability of a 4 simple items score for persistent pain in 200 patients scheduled for breast cancer surgery. A multivariable logistic regression model was created for the outcome of clinically important pain at 4 months.

RESULTS

On the basis of literature review and univariable analysis of our data, 4 parameters were selected: preoperative pain at the surgical site, history of depression, age below 50 years and expected pain of high intensity (>6/10). Points for the score are based on the coefficients of the logistic regression model. A total score ≥2 points/5 predicts a risk of developing clinically important pain at 4 months >30%, with an area under the curve-receiver operating characteristic of 0.81.

DISCUSSION

We studied known risk factors for persistent pain in patients scheduled for breast cancer surgery and constructed a preoperative risk score simple enough to select high-risk patients in future prevention studies.

What makes surgical nerve injury painful? A 4-year to 9-year follow-up of patients with intercostobrachial nerve resection in women treated for breast cancer

Nerve injury during breast cancer surgery can cause neuropathic pain (NP). It is not known why some, but not all, patients develop chronic postsurgical neuropathic pain (CPSNP) after the same nerve injury. In this study, we examined 251 breast cancer survivors with surgeon-verified intercostobrachial nerve resection to identify factors that associate with CPSNP. The patients were recruited from a previous study of 1000 women treated for breast cancer in 2006 to 2010. This enabled us to analyze preoperative factors that associate with future CPSNP. The patients were re-examined in 2014 to 2016 to diagnose CPSNP using the revised NP diagnostic criteria. Preoperative assessments were pain in the area to be operated on, any chronic pain condition, depressive symptoms, anxiety, sleep, and experimental cold pain sensitivity using the cold pressor test (CPT). Follow-up assessments were CPT, psychological factors, sleep, any chronic pain, and basic laboratory tests. One hundred thirty-seven (55%) patients with intercostobrachial nerve resection fulfilled CPSNP diagnostic criteria after 4 to 9 years. Of them, 30 patients (22%) had moderate to severe pain in self-reports and 86 (63%) presented moderate to severe evoked pain at examination. Preoperative pain in the surgical area, other chronic pains, and breast-conserving surgery were associated with future CPSNP. Other chronic pains, increased psychological burden, and insomnia, both before surgery and at the follow-up, were associated with CPSNP. Preoperative CPT did not associate with future CPSNP. Patients with established CPSNP showed increased pain sensitivity in CPT and higher levels of inflammatory markers, suggesting that central sensitization and inflammation may associate with the maintenance of CPSNP.

Association between C-reactive protein and radiotherapy-related pain in a tri-racial/ethnic population of breast cancer patients: a prospective cohort study

BACKGROUND

Post-surgery adjuvant radiotherapy (RT) significantly improves clinical outcomes in breast cancer patients; however, some patients develop cancer or treatment-related pain that negatively impacts quality of life. This study examined an inflammatory biomarker, C-reactive protein (CRP), in RT-related pain in breast cancer.

METHODS

During 2008 and 2014, breast cancer patients who underwent RT were prospectively evaluated for pre- and post-RT pain. Pre- and post-RT plasma CRP levels were measured using a highly sensitive CRP ELISA kit. Pain score was assessed as the mean of four pain severity items (i.e., pain at its worst, least, average, and now) from the Brief Pain Inventory. Pain scores of 4-10 were classified as clinically relevant pain. Multivariable logistic regression analyses were applied to ascertain the associations between CRP and RT-related pain.

RESULTS

In 366 breast cancer patients (235 Hispanic whites, 73 black/African Americans, and 58 non-Hispanic whites), 17% and 30% of patients reported pre- and post-RT pain, while 23% of patients had RT-related pain. Both pre- and post-RT pain scores differed significantly by race/ethnicity. In multivariable logistic regression analysis, RT-related pain was significantly associated with elevated pre-RT CRP (≥ 10 mg/L) alone (odds ratio (OR) = 2.44; 95% confidence interval (CI) = 1.02, 5.85); or combined with obesity (OR = 4.73; 95% CI = 1.41, 15.81) after adjustment for age and race/ethnicity.

CONCLUSIONS

This is the first pilot study of CRP in RT-related pain, particularly in obese breast cancer patients. Future larger studies are warranted to validate our findings and help guide RT decision-making processes and targeted interventions.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis

Background

To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain.

Methods

We analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2-4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain.

Results

Whether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the "persistent pain" group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate.

Conclusions

Results provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent.

AAPT Diagnostic Criteria for Peripheral Neuropathic Pain: Focal and Segmental Disorders

Peripheral neuropathic pain is among the most prevalent types of neuropathic pain. No comprehensive peripheral neuropathic pain classification system that incorporates contemporary clinical, diagnostic, biological, and psychological information exists. To address this need, this article covers the taxonomy for 4 focal or segmental peripheral neuropathic pain disorders, as part of the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership and the American Pain Society (APS) collaborative to develop a standardized, evidence-based taxonomy initiative: the ACTTION-APS Pain Taxonomy (AAPT). The disorders-postherpetic neuralgia, persistent posttraumatic neuropathic pain, complex regional pain disorder, and trigeminal neuralgia-were selected because of their clinical and clinical research relevance. The multidimensional features of the taxonomy are suitable for clinical trials and can also facilitate hypothesis-driven case-control and cohort epidemiologic studies. PERSPECTIVE: The AAPT peripheral neuropathic pain taxonomy subdivides the peripheral neuropathic pain disorders into those that are generalized and symmetric and those that are focal or segmental and asymmetric. In this article, we cover the focal and segmental disorders: postherpetic neuralgia, persistent posttraumatic neuropathic pain, complex regional pain disorder, and trigeminal neuralgia. The taxonomy is evidence-based and multidimensional, with the following dimensions: 1) core diagnostic criteria; 2) common features; 3) common medical and psychiatric comorbidities; 4) neurobiological, psychosocial, and functional consequences; and 5) putative neurobiological and psychosocial mechanisms, risk factors, and protective factors.

C-reactive Protein Level on Postoperative Day One is Associated with Chronic Postsurgical Pain After Mastectomy

Background

C-reactive protein (CRP) is an acute phase reactant released in response to inflammation or tissue injury. Inflammation is one of the pathogenic factors related to transition from acute postsurgical pain (APSP) to chronic postsurgical pain (CPSP). Although several risk factors are reportedly associated with CPSP, the effects of CRP levels on CPSP have not been examined.

Objectives

The present study investigated the relationship between perioperative risk factors, including CRP levels on postoperative day one and CPSP, in patients undergoing mastectomy.

Methods

Preoperative anxiety and depression levels were evaluated in female patients undergoing mastectomy under general anesthesia, with or without peripheral nerve block. Patients with chronic preoperative pain and/or preoperative breast pain were excluded. The intensity of postoperative pain was prospectively examined one and six days, and three and twelve months after surgery using a numerical rating scale (NRS).

Results

The current researchers conducted univariate and multivariate linear regression analyses to explore risk factors for CPSP in 36 patients. Patient demographics, preoperative psychological states, and anesthetic managements showed no relationship with CPSP. On the other hand, pain intensity of APSP and CRP levels on postoperative day one was significantly associated with the pain intensity of CPSP.

Conclusions

Postoperative CRP level is likely to be associated with the development of CPSP after mastectomy.

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy

Background

Prevention 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.

Methods

Over 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.

Results

A 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%.

Conclusions

The 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 material

The online version of this article (10.1007/s10549-018-4841-8) contains supplementary material, which is available to authorized users.

Living with chronic pain: perceptions of breast cancer survivors

PURPOSE

Breast cancer treatments may lead to chronic pain. For some breast cancer survivors (BCS), this experience can develop into the perception of living with chronic pain. The majority of BCS are postmenopausal and have hormone receptor-positive (HR+) breast cancer requiring aromatase inhibitors (AIs). Neither the prevalence nor risk factors associated with the perception of living with chronic pain among this population are well defined.

METHODS

We conducted a cross-sectional survey among postmenopausal, HR+ BCS who previously took or were currently taking AIs. The primary outcome was patients' perception of living with chronic pain over the past 6 months. We measured pain and demographic and clinical variables. Multivariable logistic regression analysis was performed to evaluate risk factors associated with the perception of chronic pain.

RESULTS

Among 1280 participants, 167 (13%) reported having the perception of living with chronic pain before their breast cancer diagnosis; 426 (34%) reported this perception after completion of non-hormonal cancer treatment. Seventy-eight percent of BCSs reported experiencing at least one type of treatment-related pain within the past 7 days, with 23% experiencing at least three types. The most common types of pain were AI-induced musculoskeletal pain (49%) and pain at the surgery or radiation site (31%). Younger age (< 56), BMI > 25, and the perception of living with chronic pain before diagnosis were risk factors associated with the perception of living with chronic pain.

CONCLUSIONS

One in three postmenopausal, HR+ BCS considered themselves to be living with chronic pain. Effective interventions to reduce chronic pain are needed.

Does expecting more pain make it more intense? Factors associated with the first week pain trajectories after breast cancer surgery

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

The aim of this study was to identify clinical risk factors for unfavorable pain trajectories after breast cancer surgery, to better understand the association between pain expectation, psychological distress, and acute postoperative pain. This prospective study included 563 women treated for breast cancer. Psychological data included questionnaires for depressive symptoms and anxiety. Experimental pain tests for heat and cold were performed before surgery. The amount of oxycodone needed for satisfactory pain relief after surgery was recorded. Pain intensity in the area of operation before surgery and during the first postoperative week and expected intensity of postoperative pain were recorded using the Numerical Rating Scale (NRS 0-10). Pain trajectories were formed to describe both initial intensity (the intercept) and the direction of the pain path (the slope). Factors associated with higher initial pain intensity (the intercept) were the amount of oxycodone needed for adequate analgesia, psychological distress, type of axillary surgery, preoperative pain in the area of the operation, and expectation of postoperative pain. The higher the pain initially was, the faster it resolved over the week. Expectation of severe postoperative pain was associated with higher scores of both experimental and clinical pain intensity and psychological factors. The results confirm that acute pain after breast cancer surgery is a multidimensional phenomenon. Psychological distress, pain expectation, and the patients' report of preoperative pain in the area to be operated should be recognized before surgery. Patients having axillary clearance need more efficient analgesic approaches.

Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

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.

Characterization of risk factors for adjuvant radiotherapy-associated pain in a tri-racial/ethnic breast cancer population

Pain related to cancer or treatment is a critical quality of life issue for breast cancer survivors. In a prospective study of 375 patients with breast cancer (enrolled during 2008-2014), we characterized the risk factors for adjuvant radiotherapy (RT)-associated pain. Pain score was assessed at pre-RT and post-RT as the mean of 4 pain severity items (ie, pain at its worst, least, average, and now) from the Brief Pain Inventory with 11-point numeric rating scale (0-10). Pain scores of 4 to 10 were considered clinically relevant pain. The study consists of 58 non-Hispanic whites (15%), 78 black or African Americans (AA; 21%), and 239 Hispanic whites (HW; 64%). Overall, the prevalence of pre-RT, post-RT, and RT-associated clinically relevant pain was 16%, 31% and 20%, respectively. In univariate analysis, AA and HW had significantly higher pre-RT and post-RT pain than non-Hispanic whites. In multivariable logistic regression analysis, pre-RT pain was significantly associated with HW and obesity; post-RT pain was significantly associated with AA, HW, younger age, ≥2 comorbid conditions, above-median hotspot volume receiving >105% prescribed dose, and pre-RT pain score ≥4. Radiotherapy-associated pain was significantly associated with AA (odds ratio [OR] = 3.27; 95% confidence interval [CI] = 1.09-9.82), younger age (OR = 2.44, 95% CI = 1.24-4.79), and 2 or ≥3 comorbid conditions (OR = 3.06, 95% CI = 1.32-7.08; OR = 4.61, 95% CI = 1.49-14.25, respectively). These risk factors may help to guide RT decision-making process, such as hypofractionated RT schedule. Furthermore, effective pain management strategies are needed to improve quality of life in patients with breast cancer with clinically relevant pain.

Audit of postoperative pain management after open thoracotomy and the incidence of chronic postthoracotomy pain in more than 500 patients at a tertiary center

STUDY OBJECTIVE

To evaluate the quality of postoperative pain relief during the first 3 days after surgery and to evaluate with the incidence of persistent pain at 6 months after surgery.

DESIGN

Retrospective single-center audit.

SETTING

University hospital.

PATIENTS

Five hundred four patients who underwent thoracotomy.

INTERVENTIONS

Review of patient records, questionnaire, and telephone review.

RESULTS

Of the 364 survivors, 306 were contacted. Five or more episodes of severe pain (numerical rating scale >6/10 at rest or movement) during the first 72 hours after surgery occurred in 133 patients. Persistent postsurgical pain at 6 months was present in 82% (109/133) of these patients. Patient satisfaction with acute postoperative pain management was excellent (36%), good (43%), and fair or poor (21%).The incidence of postthoracotomy pain was 56% (mild 32%, moderate 18%, and severe 6%).

CONCLUSIONS

Poorly controlled acute postoperative pain correlated with persistent postsurgical pain at 6 months. In view of such a high incidence in thoracotomy patients, preventative strategies assume great significance.

Antidepressant Drugs for Postsurgical Pain: Current Status and Future Directions

Current treatments for postsurgical pain are often inadequate and adverse effects are substantial such that residual pain and/or side effects impair recovery. The recognition of analgesic efficacy with antidepressant drugs for chronic pain suggests the potential for efficacy in acute postsurgical pain. As reviewed here, current evidence suggests that approximately half of previous trials suggest efficacy of various antidepressants for acute postoperative pain. However, most trials are older with deficiencies including: lack of designation of a primary outcome, no assessment of movement-evoked pain, small size and limited safety assessment. Only one of three trials addressing prevention of chronic postsurgical pain suggested any efficacy; however, the evidence base for this indication is limited. Thus, current evidence does not yet support routine use of any one specific antidepressant for treatment of acute, or prevention of chronic, postsurgical pain. However, limitations in available trials are such that one cannot yet rule out the possibility that one or more antidepressant drugs may provide benefit in specific populations. Therefore, future larger trials should explore optimal dosing and duration of antidepressant treatment, procedure specificity, safety evaluation, and assessment of movement-evoked pain.