Fairness in the prediction of acute postoperative pain using machine learning models

Moving towards the use of artificial intelligence in pain management

BACKGROUND AND OBJECTIVE

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

DATABASES AND DATA TREATMENT

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

RESULTS

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

CONCLUSION

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

SIGNIFICANCE

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Machine learning approach to predict postoperative pain after spinal morphine administration during caesarean delivery

Background

A major barrier to optimal pain management is the difficulty in predicting and assessing patients at high risk for significant pain across multiple locations within the institution in a timely manner. This is compounded by the fragmented display of clinical information on enterprise clinical platform, which further hinders delay the reviews and hence the increased risk of untreated pain. We evaluated and compared the predictive performance of six modelling techniques in predicting significant pain, defined as the maximum pain score of 3 or more on movement at the 13th to 24th hour after spinal morphine administration during caesarean delivery.

Methods

We retrieved medical records from women who underwent caesarean delivery and received postoperative spinal morphine in a single specialist maternity hospital in Singapore between Aug 2019 and Aug 2022. We extracted 120 clinical variables from the medical records of eligible patients and further selected 23 to improve algorithm accuracies. The dataset was split randomly, with 80 % of patients (n = 5248) used for training the models, and 20 % (n = 1313) reserved for validation.

Results

The study cohort comprised 6561 patients with an incidence of significant postoperative pain of 7.9 %. Ridge regression demonstrated the best performance with both the full (AUC: 0.649) and selected (AUC: 0.719) feature sets. By reducing the number of features, Ridge regression, LASSO, Elastic net, and XGBoost showed similar in AUC (0.704-0.719), sensitivity (0.644-0.695), specificity (0.644-0.705), positive predictive value (0.155-0.179), and negative predictive value (0.949-0.955) in predicting significant postoperative pain. These were attributed to the top three variables, mainly the last recorded postoperative pain score (on movement) before the prediction point, mean and standard deviation of the hourly maximum postoperative pain score (at rest) at 0th to 12th hour.

Conclusions

Future research will aim to refine these models and explore their implementation in clinical settings to enhance real-time pain management and risk stratification for women after caesarean delivery.

Deep learning models for the prediction of acute postoperative pain in PACU for video-assisted thoracoscopic surgery

BACKGROUND

Postoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.

METHODS

Through a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.

RESULTS

A total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.

CONCLUSIONS

Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

Postoperative Pain at Discharge From the Post-anesthesia Care Unit: A Case-Control Study

INTRODUCTION

Despite advancements in postoperative pain management, approximately 20% of patients still experience severe pain within the first 24 hours post-surgery. Previous studies utilizing machine learning have shown promise in predicting postoperative pain with various models. This study investigates postoperative pain predictors using a machine learning approach based on physiological indicators and demographic factors in a Mexican cohort.

METHODS

We conducted a retrospective case-control study to assess pain determinants at Post-anesthesia Care Unit (PACU) discharge at Hospital Ángeles Lomas in Mexico City. Data were collected from 550 patients discharged from the PACU, including 292 cases and 258 controls, covering a range of surgical procedures and illnesses. Machine learning techniques were employed to develop a predictive model for postoperative pain. Physiological responses, such as blood pressure, heart rate, respiratory rate, and anesthesia type, were recorded prior to PACU admission.

RESULTS

Significant differences were found between cases and controls, with factors such as sex, anesthesia type, and physiological responses influencing postoperative pain. Visual analog scale (VAS) scores at PACU admission were predictive of pain at discharge.

CONCLUSIONS

Our findings reinforce existing literature by highlighting sex-based disparities in pain experiences and the influence of anesthesia type on pain levels. The logistic regression model developed, incorporating physiological responses and sex, shows potential for refining pain management strategies. Limitations include the lack of detailed surgical data and psychological factors, and validation in a prospective cohort. Future research should focus on more comprehensive predictive models and longitudinal studies to further improve postoperative pain management.

Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review

PURPOSE OF REVIEW

This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued investigation and research.

RECENT FINDINGS

Current ML models leverage diverse datasets, algorithmic techniques, and validation methods to identify predictive biomarkers, risk factors, and phenotypic signatures associated with increased acute and chronic postoperative pain and persistent opioid use. ML models demonstrate satisfactory performance to predict pain outcomes and their prognostic trajectories, identify modifiable risk factors and at-risk patients who benefit from targeted pain management strategies, and show promise in pain prevention applications. However, further evidence is needed to evaluate the reliability, generalizability, effectiveness, and safety of ML-driven approaches before their integration into perioperative pain management practices.

SUMMARY

Artificial intelligence (AI) has the potential to enhance perioperative pain management by providing more accurate predictive models and personalized interventions. By leveraging ML algorithms, clinicians can better identify at-risk patients and tailor treatment strategies accordingly. However, successful implementation needs to address challenges in data quality, algorithmic complexity, and ethical and practical considerations. Future research should focus on validating AI-driven interventions in clinical practice and fostering interdisciplinary collaboration to advance perioperative care.

Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation

Objective

This study aims to evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques.

Methods

This study, conducted at Dr. Irandoust's Health Center at Qazvin from August 2021 to August 2023, aimed to evaluate the effects of five lifestyle interventions on individuals with eating disorders, initially diagnosed using The Eating Disorder Diagnostic Scale (EDDS). The interventions were: (1) Counseling, exercise, and dietary regime, (2) Aerobic exercises with dietary regime, (3) Walking and dietary regime, (4) Exercise with a flexible diet, and (5) Exercises through online programs and applications. Out of 955 enrolled participants, 706 completed the study, which measured Body Fat Percentage (BFP), Waist-Hip Ratio (WHR), Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL) Cholesterol, Total Cholesterol (CHO), Weight, and Triglycerides (TG) at baseline, during, and at the end of the intervention. Random Forest and Gradient Boosting Regressors, following feature engineering, were used to analyze the data, focusing on the interventions' long-term effectiveness on health outcomes related to eating disorders.

Results

Feature engineering with Random Forest and Gradient Boosting Regressors, respectively, reached an accuracy of 85 and 89%, then 89 and 90% after dataset balancing. The interventions were ranked based on predicted effectiveness: counseling with exercise and dietary regime, aerobic exercises with dietary regime, walking with dietary regime, exercise with a flexible diet, and exercises through online programs.

Conclusion

The results show that Machine Learning (ML) models effectively predicted the long-term effectiveness of lifestyle interventions. The current study suggests a significant potential for tailored health strategies. This emphasizes the most effective interventions for individuals with eating disorders. According to the results, it can also be suggested to expand demographics and geographic locations of participants, longer study duration, exploring advanced machine learning techniques, and including psychological and social adherence factors. Ultimately, these results can guide healthcare providers and policymakers in creating targeted lifestyle intervention strategies, emphasizing personalized health plans, and leveraging machine learning for predictive healthcare solutions.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models

Objectives

Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.

Materials and Methods

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment.

Results

Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting.

Discussion

This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

Risk Factors for Acute Postsurgical Pain: A Narrative Review

Acute postsurgical pain (APSP) has received growing attention as a surgical outcome. When poorly controlled, APSP can affect short- and long-term outcomes in patients. Despite the steady increase in awareness about postoperative pain and standardization of pain prevention and treatment strategies, moderate-to-severe APSP is frequently reported in clinical practice. This is possibly because pain varies widely among individuals and is influenced by distinct factors, such as demographic, perioperative, psychological, and genetic factors. This review investigates the risk factors for APSP, including gender, age, obesity, smoking history, preoperative pain history, pain sensitivity, preoperative anxiety, depression, pain catastrophizing, expected postoperative pain, surgical fear, and genetic polymorphisms. By identifying patients having an increased risk of moderate-to-severe APSP at an early stage, clinicians can more effectively manage individualized analgesic treatment protocols with a combination of pharmacological and non-pharmacological interventions. This would alleviate the transition from APSP to chronic pain and reduce the severity of APSP-induced chronic physical disability and social psychological distress.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models

OBJECTIVES

Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.

MATERIALS AND METHODS

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment.

RESULTS

Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting.

DISCUSSION

This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.