Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery

The usefulness of artificial intelligence in breast reconstruction: a systematic review

BACKGROUND

Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction.

METHODS

A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction.

RESULTS

A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification.

CONCLUSIONS

In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

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.

Can perioperative psychological interventions reduce chronic pain after surgery?

Chronic post-surgical pain is a relatively common adverse effect following surgery. Several prognostic factors for chronic post-surgical pain have been identified, including psychological states and traits. Psychological factors are modifiable, and perioperative psychological interventions may reduce the incidence of chronic post-surgical pain. A meta-analysis showed preliminary evidence for the benefits of such interventions for the prevention of chronic post-surgical pain. Further research must be conducted to better understand the specific type, intensity, duration and timing of interventions that are most effective. The number of studies in this area has recently increased, with additional randomised controlled trials currently being carried out, which may allow for the development of more robust conclusions in the coming years. In order to implement perioperative psychological care alongside routine surgical interventions, efficient and accessible interventions should be available. In addition, demonstration of cost-effectiveness may be a prerequisite for wider adoption of perioperative psychological interventions in regular healthcare. Offering psychological interventions selectively to patients at risk of chronic post-surgical pain could be a means to increase cost-effectiveness. Stepped-care approaches should also be considered, where the intensity of psychological support is adapted to the needs of the patient.

Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size

Selecting the k best features is a common task in machine learning. Typically, a few features have high importance, but many have low importance (right-skewed distribution). This report proposes a numerically precise method to address this skewed feature importance distribution in order to reduce a feature set to the informative minimum of items. Computed ABC analysis (cABC) is an item categorization method that aims to identify the most important items by partitioning a set of non-negative numerical items into subsets "A", "B", and "C" such that subset "A" contains the "few important" items based on specific properties of ABC curves defined by their relationship to Lorenz curves. In its recursive form, the cABC analysis can be applied again to subset "A". A generic image dataset and three biomedical datasets (lipidomics and two genomics datasets) with a large number of variables were used to perform the experiments. The experimental results show that the recursive cABC analysis limits the dimensions of the data projection to a minimum where the relevant information is still preserved and directs the feature selection in machine learning to the most important class-relevant information, including filtering feature sets for nonsense variables. Feature sets were reduced to 10% or less of the original variables and still provided accurate classification in data not used for feature selection. cABC analysis, in its recursive variant, provides a computationally precise means of reducing information to a minimum. The minimum is the result of a computation of the number of k most relevant items, rather than a decision to select the k best items from a list. In addition, there are precise criteria for stopping the reduction process. The reduction to the most important features can improve the human understanding of the properties of the data set. The cABC method is implemented in the Python package "cABCanalysis" available at https://pypi.org/project/cABCanalysis/ .

Using artificial intelligence to improve pain assessment and pain management: a scoping review

CONTEXT

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.

OBJECTIVES

This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.

METHODS

The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.

RESULTS

This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.

CONCLUSIONS

Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

Present and future of machine learning in breast surgery: systematic review

BACKGROUND

Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications.

METHODS

A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar.

RESULTS

The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation.

CONCLUSION

Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.

Profiles of Risk and Resilience in Chronic Pain: Loneliness, Social Support, Mindfulness, and Optimism Coming out of the First Pandemic Year

Objective

Individuals experience chronic pain differently, not only because of different clinical diagnoses, but also because of differing degrees of influence from biopsychosocial pain modulators. We aimed to cluster patients with chronic pain into distinct subgroups based on psychosocial characteristics and pain intensity, and we subsequently examined group differences in pain-related interference approximately 1 year later.

Methods

In this observational, longitudinal study, patients with chronic pain (n = 94) completed validated assessments of psychosocial characteristics and pain intensity at the beginning of COVID-19–related social distancing (April to June 2020). One year later (May to June 2021), patients completed a follow-up survey with assessments of pain interference, loneliness, social support, mindfulness, and optimism.

Results

A cluster analysis, using psychosocial factors and pain intensity, empirically produced three patient groups: 1) psychosocial predominant (PSP), characterized by high psychosocial distress and average pain intensity; 2) pain intensity predominant (PIP), characterized by average psychosocial distress and high pain intensity; and 3) less elevated symptoms (LES), characterized by low psychosocial distress and low pain intensity. At the 1-year follow-up, patients in the PSP and PIP clusters suffered greater pain interference than patients in the LES cluster, while patients in the PSP cluster also reported greater loneliness and lower mindfulness and optimism.

Conclusions

An empirical psychosocial-based clustering of patients identified three distinct groups that differed in pain interference. Patients with high psychosocial modulation of pain at the onset of social distancing (the PSP cluster) suffered not only greater pain interference but also greater loneliness and lower levels of mindfulness and optimism, which suggests some potential behavioral targets for this group in the future.

Intelligent monitoring of noxious stimulation during anaesthesia based on heart rate variability analysis

Research based on medical signals has received significant attention in recent years. If the patients' states can be accurately monitored based on medical signals, it greatly benefits both doctors and patients. This paper proposes a method to extract signal features from heart rate variability signals and classify patients' states using the long short-term memory network and enable effective monitoring of noxious stimulation. For data processing, the heart rate variability signal is decomposed and recombined by the empirical mode decomposition method, and the signal features of the noxious stimulation are extracted by the sliding time window method. Compared with the average accuracy of direct classifications, the classification accuracy based on the proposed method is proved more accurate. The model based on the extracted features proposed can realize the classification of consciousness and general anaesthesia with an accuracy rate of more than 90% and accurately estimate the occurrence of tracheal intubation stimulation. Furthermore, this study shows that combining the deep learning neural network with the extracted more effective signal features under different states and stresses can classify the states with high accuracy. Therefore, it is promising to apply the deep learning method in researching the autonomic nervous system.

Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study

BACKGROUND

Researching people with herpes simplex virus (HSV) is challenging because of poor data quality, low user engagement, and concerns around stigma and anonymity.

OBJECTIVE

This project aimed to improve data collection for a real-world HSV registry by identifying predictors of HSV infection and selecting a limited number of relevant questions to ask new registry users to determine their level of HSV infection risk.

METHODS

The US National Health and Nutrition Examination Survey (NHANES, 2015-2016) database includes the confirmed HSV type 1 and type 2 (HSV-1 and HSV-2, respectively) status of American participants (14-49 years) and a wealth of demographic and health-related data. The questionnaires and data sets from this survey were used to form two data sets: one for HSV-1 and one for HSV-2. These data sets were used to train and test a model that used a random forest algorithm (devised using Python) to minimize the number of anonymous lifestyle-based questions needed to identify risk groups for HSV.

RESULTS

The model selected a reduced number of questions from the NHANES questionnaire that predicted HSV infection risk with high accuracy scores of 0.91 and 0.96 and high recall scores of 0.88 and 0.98 for the HSV-1 and HSV-2 data sets, respectively. The number of questions was reduced from 150 to an average of 40, depending on age and gender. The model, therefore, provided high predictability of risk of infection with minimal required input.

CONCLUSIONS

This machine learning algorithm can be used in a real-world evidence registry to collect relevant lifestyle data and identify individuals' levels of risk of HSV infection. A limitation is the absence of real user data and integration with electronic medical records, which would enable model learning and improvement. Future work will explore model adjustments, anonymization options, explicit permissions, and a standardized data schema that meet the General Data Protection Regulation, Health Insurance Portability and Accountability Act, and third-party interface connectivity requirements.

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.

Prediction of Persistent Pain Severity and Impact 12 Months After Breast Surgery Using Comprehensive Preoperative Assessment of Biopsychosocial Pain Modulators

BACKGROUND

Persistent post-mastectomy pain (PPMP) is a significant negative outcome occurring after breast surgery, and understanding which individual women are most at risk is essential to targeting of preventive efforts. The biopsychosocial model of pain suggests that factors from many domains may importantly modulate pain processing and predict the progression to pain persistence.

METHODS

This prospective longitudinal observational cohort study used detailed and comprehensive psychosocial and psychophysical assessment to characterize individual pain-processing phenotypes in 259 women preoperatively. Pain severity and functional impact then were longitudinally assessed using both validated surgery-specific and general pain questionnaires to survey patients who underwent lumpectomy, mastectomy, or mastectomy with reconstruction in the first postsurgical year. An agnostic, multivariable modeling strategy identified consistent predictors of several pain outcomes at 12 months.

RESULTS

The preoperative characteristics most consistently associated with PPMP outcomes were preexisting surgical area pain, less education, increased somatization, and baseline sleep disturbance, with axillary dissection emerging as the only consistent surgical variable to predict worse pain. Greater pain catastrophizing, negative affect, younger age, higher body mass index (BMI), and chemotherapy also were independently predictive of pain impact, but not severity. Sensory disturbance in the surgical area was predicted by a slightly different subset of factors, including higher preoperative temporal summation of pain.

CONCLUSIONS

This comprehensive approach assessing consistent predictors of pain severity, functional impact, and sensory disturbance may inform personalized prevention of PPMP and also may allow stratification and enrichment in future preventive studies of women at higher risk of this outcome, including pharmacologic and behavioral interventions and regional anesthesia.

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.

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.

Pectoral nerve blocks and postoperative pain outcomes after mastectomy: a meta-analysis of randomized controlled trials

BACKGROUND AND OBJECTIVES

Several studies have evaluated the effect of pectoral nerve blocks to improve postoperative analgesia following breast cancer surgery resulting in contradictory findings. The aim of this study was to examine the effect of Pecs blocks on postoperative analgesia in women following mastectomies.

METHODS

We performed a quantitative systematic review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Articles of randomized controlled trials that compared Pecs block (types I and II) to a control group in patients undergoing mastectomy were included. The primary outcome was total opioid consumption 24 hours after surgery. Secondary outcomes included pain scores and side effects. Meta-analysis was performed using the random effect model.

RESULTS

7 randomized controlled trials with 458 patients were included in the analysis. The effect of pectoral nerve blocks on postoperative opioid consumption compared with control revealed a significant effect, weighted mean difference (WMD) (95% CI) of --4.99 (-7.90 to -2.08) mg intravenous morphine equivalents (p=0.001). In addition, postoperative pain compared with control was reduced at 6 hours after surgery: WMD (95% CI) of -0.72 (-1.37 to -0.07), p=0.03, and at 24 hours after surgery: WMD (95% CI) of -0.91 (-1.81 to -0.02), p=0.04.

DISCUSSION

This quantitative analysis of randomized controlled trials demonstrates that the Pecs block is effective for reducing postoperative opioid consumption and pain in patients undergoing mastectomy. The Pecs block should be considered as an effective strategy to improve analgesic outcomes in patients undergoing mastectomies for breast cancer treatment.

Does early depressive mood expire following radical retropubic prostatectomy in patients with localized prostate cancer?

In this study, the pattern of depressive mood in patients following radical prostatectomy (RP) for localized prostate cancer (PCa) was determined. A total of 30 patients (aged 68.03±6.1 years) who were diagnosed with localized PCa and underwent RP within 1 month entered the study. Evaluations included body mass index, prostate-specific antigen, testosterone, underlying disease, international prostate symptom score and quality of life (QoL), international index of erectile function as well as Beck depression inventory (BDI), both at the initial stage and 3 months later. Basic demographic data, laboratory results, and questionnaires were analyzed statistically. The BDI score significantly decreased 3 months after the surgery. In correlation analysis, BDI was related with the international prostate symptom score but not with the underlying disease, QoL or international index of erectile function. Body mass index was identified as one of the risk factors to decrease the probability of BDI score (≥3) significantly. Underlying disease increased the probability of BDI score. In the assessment of the correlation between BDI and each subscale, sadness, self-dislike, self-criticalness, and worth-lessness showed high correlation. In the early period, depressive mood was improved at the short-term follow-up in localized PCa patients after RP. Voiding symptoms were only related with the depressive mood, but not with other parameters, including sexual function. The depressive mood had no effect on the QoL in the early stage.

A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

Background

Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine‐learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain.

Methods

Based on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine‐learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase.

Results

Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research.

Conclusions

The present computational functional genomics‐based approach provided a computational systems‐biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine‐learned methods provide innovative approaches to knowledge discovery from previous evidence.

Significance

We show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain.