Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study

Abstract

Background

Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain management protocols often fail to accommodate this variability, necessitating more tailored approaches.

Objective

This study aims to improve postoperative pain management following cesarean sections by developing personalized protocols using machine learning (ML) models.

Method

The study analyzed the efficacy of eight ML models, including XGBoost, Random Forest, and Neural Networks, using data from two distinct hospital cohorts. Performance metrics such as Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) were evaluated through internal and external validations. SHAP value analysis was used to identify key predictors influencing pain management outcomes.

Results

The XGBoost model demonstrated superior performance, achieving the lowest RMSE and highest R². Key factors impacting pain management included esketamine use, anesthesia method, and anesthetic drug type, with esketamine significantly delaying the first activation of patient-controlled intravenous analgesia (PCIA).

Conclusions

The study highlights the potential of machine learning to refine postoperative pain management strategies in obstetric care, suggesting that personalized approaches, particularly incorporating esketamine and specific anesthesia methods, could enhance patient outcomes.

Trial registration

Not applicable.

Introduction

Effective management of postoperative analgesia following cesarean sections (C-sections) is essential in obstetric care [1, 2], especially given the rising global rates of cesarean deliveries [3]. Optimal analgesic strategies are crucial for improving maternal health outcomes as inadequate pain management can hinder maternal recuperation, disrupt breastfeeding success, and impair early mother-infant bonding, potentially affecting long-term outcomes for both mother and neonate [4, 5].

Current pain management protocols, including multimodal analgesia, regional anesthesia, and patient-controlled analgesia (PCA), often struggle to accommodate significant variability in pain perception and response [6, 7]. This variability, influenced by factors such as body mass index, comorbid conditions, anesthesia type, and psychosocial elements, challenges the standardization of pain management protocols and underscores the necessity for more personalized therapeutic approaches [8, 9].

Machine learning (ML) presents a promising solution to address these complexities [10]. ML techniques are adept at modeling intricate nonlinear interactions and processing multidimensional data, which facilitates the identification of nuanced patterns in patient responses [11]. The integration of ML into healthcare has demonstrated potential to enhance clinical decision-making and effectively predict patient outcomes [12]. By employing ML to analyze pain patterns post-cesarean section, this study aims to develop more precise, personalized pain management protocols.

This research evaluates eight machine learning models using data from two distinct hospital cohorts to advance precision medicine in obstetrics, focusing on the creation of tailored post-cesarean pain management strategies.

Methods

Study design and population

A retrospective cohort study was conducted across two medical facilities, Jinan Second maternal and Child Health Hospital (Hospital A) and Provincial Hospital Affiliated to Shandong First Medical University (Hospital B), from January to December 2023. The study targeted women undergoing elective cesarean sections. Data from Hospital A were used for training and internal validation of the machine learning models, while data from Hospital B enabled external validation and an assessment of model generalizability across diverse clinical settings.

Women aged 20–35 years with singleton pregnancies at 37–40 weeks gestation were enrolled. Exclusion criteria included significant cardiovascular (e.g., New York Heart Association Class III/IV heart failure), respiratory (e.g., forced expiratory volume < 50% predicted), hepatic (e.g., liver cirrhosis with Child-Pugh score ≥ 9), or renal diseases (e.g., estimated glomerular filtration rate < 30 mL/min/1.73 m²); preoperative use of sedatives or analgesics; existing psychiatric conditions; and any disorders likely to affect postoperative pain perception or recovery.

Data collection

Data were systematically collected at both sites by experienced clinical researchers. The comprehensive dataset incorporated three primary domains of clinical variables:

• Preoperative Variables: Patient demographics, physical parameters, body mass index, comprehensive medical histories with emphasis on cardiovascular and metabolic disorders, substance use, and obstetric history.

• Intraoperative Variables: Surgical approach (intraperitoneal or extraperitoneal), anesthetic techniques (epidural or combined spinal-epidural), specific pharmacological interventions (notably esketamine), duration of surgery, and measured blood loss.

• Primary Outcome Measure: The analgesic requirement, specifically the time from surgical completion to the first use of the patient-controlled intravenous analgesia system, indicating immediate postoperative pain.

Data processing and analysis

Data preprocessing involved outlier detection, multiple imputation for missing data, and transformation of variables. Recursive Feature Elimination (RFE) was utilized to refine the feature set for optimal predictive capability in model development. Missing data were addressed through multiple imputation by chained equations (MICE) using the ‘mice’ package in R, creating 10 imputed datasets with 20 iterations each. Convergence was assessed using trace plots, and pooled results were combined using Rubin’s rules.

Model development and validation

In this study, we applied a variety of machine learning algorithms to assess their predictive performance on postoperative pain following cesarean sections. The models evaluated included Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Stochastic Gradient Boosting (SGBT), Neural Network, and Extreme Gradient Boosting (XGBoost) [13]. This diversity allows us to evaluate which approach best captures the complex relationships in our dataset, from simple linear associations to highly nonlinear interactions.

To ensure a robust evaluation of these models and to prevent overfitting, we implemented 5-fold cross-validation coupled with grid search techniques for meticulous hyperparameter tuning. This approach enabled us to systematically explore and identify the most effective parameter settings for each model. The performance of each model was primarily measured by Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) during the internal validation phase.

Furthermore, the models underwent external validation using data from Hospital B to assess their generalizability across different clinical environments. The XGBoost model, which demonstrated superior performance, was further analyzed using SHAP (Shapley Additive exPlanations) to assess feature importance and enhance interpretability of the results. The flowchart is shown in Fig. 1.

Fig. 1

Flow chart of the study design. This flow chart illustrates the overall study design, including the recruitment of participants from two medical facilities, the division of data into training and validation cohorts, the machine learning model development process, and the subsequent internal and external validation step

Statistical analysis

Statistical analyses were performed using R software. Continuous variables were presented as mean ± standard deviation or median with interquartile range, and categorical variables as frequencies and percentages. The threshold for statistical significance was set at p < 0.05.

Ethical considerations

The study protocol was approved by the Ethics Committees of both Jinan Second Maternal and Child Health Hospital (Approval No. 2022-lw-301) and Provincial Hospital Affiliated to Shandong First Medical University (Approval No. SDPH-158), ensuring compliance with the principles of the Declaration of Helsinki. All patient data were anonymized to maintain confidentiality and protect patient privacy.

Results

Baseline characteristics of study population

In this multicenter study, 477 parturients were enrolled—300 from Hospital A for model training and internal validation, and 177 from Hospital B for external validation. The participants’ baseline characteristics are detailed in Table 1, which includes demographic and clinical data such as age, BMI, hypertension, diabetes, smoking, and alcohol consumption status.

Intraoperatively, 80% of the cesareans were intraperitoneal, while 20% were extraperitoneal. The average duration of surgery was approximately 60 min, with an estimated blood loss of 149.60 ± 23.50 ml. Anesthesia was administered via epidural in 56% of cases and combined spinal-epidural in 44%. The proportion of intravenous esketamine administration was 43%, associated with a mean time to the first activation of PCIA of 113.75 ± 17.26 min. (See Table 1 for details)

Table 1 Baseline characteristics of the study population

Model training and internal validation

To enhance model performance and interpretability, Recursive Feature Elimination (RFE) was utilized. Employing a 10-fold cross-validation resampling method, RFE identified the optimal subset of features at 15 variables, where the model achieved an RMSE of 12.36, R² of 0.4996, and MAE of 9.889, indicating the most predictive balance of accuracy and complexity. The top five variables, which included anesthesia method, anesthetic drug type, diabetes status, esketamine use, and alcohol consumption, were particularly influential in model predictions.

Eight machine learning models were subsequently developed to predict the time until the first activation of PCIA. Figure 2 details the internal validation performance of these models at Hospital A, with specific focus on the RMSE and R² values to highlight their predictive efficacy. The XGBoost model exhibited superior performance, achieving the lowest RMSE of 14.08 and the highest R² of 0.39, which substantiates its robust predictive power concerning the timing of the first PCIA activation. Additional performance metrics including precision (0.78) and recall (0.76) for the XGBoost model further support its robust predictive power. Both the Neural Network and Random Forest models also demonstrated commendable accuracy and consistency. See Table 2 for details.

Fig. 2

Model Performance during Internal Validation (Hospital A). This bar chart compares the performance of eight machine learning models during internal validation at Hospital A. The models evaluated include Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Regression, Stochastic Gradient Boosting, Neural Network, and XGBoost

Table 2 Performance comparison of eight machine learning models

Model hyperparameter tuning and performance improvement

The XGBoost model’s hyperparameters were finely tuned using a grid search, resulting in a marked performance enhancement. The optimization process led to a reduction in RMSE to 12.35 and an increase in R² to 0.45, showcasing substantial improvements in both predictive accuracy and model reliability. The optimal hyperparameters established were: nrounds = 150, max_depth = 7, eta = 0.3, gamma = 0.1, colsample_bytree = 1, min_child_weight = 1, and subsample = 0.8.

These settings significantly contributed to the model’s enhanced performance. Figure 3 displays a scatter plot comparing predicted versus actual times to the first activation of PCIA, demonstrating improved model alignment post-tuning. The refined model showed tighter data point clustering around the line of equality, signifying a substantial increase in predictive accuracy and a reduction in prediction error.

Fig. 3

XGBoost Model Predictions and Performance Post-Tuning. This scatter plot illustrates the predicted versus actual times to the first activation of patient-controlled intravenous analgesia (PCIA) for the optimized XGBoost model. The plot demonstrates the improved alignment of predicted and actual values after hyperparameter tuning, with data points clustering tightly around the line of equality

External validation

Our external validation process tested the optimized XGBoost model using a distinct cohort from Provincial Hospital Affiliated to Shandong First Medical University, which included patients with varied demographic and clinical profiles compared to the training set at Jinan Second Maternal and Child Health Hospital. This method allowed us to evaluate the model’s efficacy and generalizability in diverse clinical environments. The model achieved an RMSE of 13.87 and an R² of 0.41 during external validation. Although these metrics are slightly lower than those recorded during internal validation (RMSE of 12.35 and R² of 0.45), they affirm the model’s strong predictive capability and its robustness for broader clinical application.

Feature importance analysis

Figure 4 provides a detailed analysis of feature importance using SHAP values for the optimized XGBoost model, designed to predict postoperative pain outcomes following cesarean sections. Figure 4A illustrates how various predictors affect the model’s output. Notably, esketamine use is identified as the most influential factor, significantly delaying the first activation of patient-controlled intravenous analgesia (PCIA). This underscores its vital role in effective postoperative pain management.

Additionally, anesthesia methods emerge as the second most significant predictor, with variations in these methods substantially affecting the timing of PCIA activation. Anesthetic drugs also demonstrate a significant impact, with both positive and negative effects on the predictions, reflecting their complex role in pain management. Furthermore, while less pronounced, the effects of diabetes and alcohol use are also discernible. Diabetes is associated with prolonged delays in PCIA activation, suggesting its influence on pain sensitivity and management, whereas alcohol use has a minimal impact, indicating its limited relevance in this model’s context.

Figure 4B presents a bar chart that quantifies the average absolute SHAP values, summarizing the impact of each feature on the model’s performance. This visualization confirms the dominant influences of esketamine use, anesthesia methods, and anesthetic drug types in determining the model’s predictions.

Fig. 4

SHAP Value Analysis for XGBoost Model. This figure provides a comprehensive analysis of feature importance using SHAP values for the optimized XGBoost model predicting postoperative pain outcomes following cesarean sections. A: This panel illustrates how various predictors influence the model’s output. Esketamine use is identified as the most influential factor, significantly delaying the first activation of PCIA. Anesthesia methods and anesthetic drug types also demonstrate substantial impacts on the timing of PCIA activation. B: This bar chart quantifies the average absolute SHAP values, summarizing the impact of each feature on the model's performance. The visualization confirms the dominant influences of esketamine use, anesthesia methods, and anesthetic drug types in determining the model's predictions

Discussion

This study confirms the XGBoost model’s superior efficacy in predicting postoperative pain following cesarean sections, as demonstrated by its lowest RMSE and highest R² values among the tested models. These metrics not only demonstrate the model’s robustness but also its adeptness at navigating the complex, non-linear dynamics common in healthcare datasets. The success of the XGBoost model corroborates existing literature on the effectiveness of ensemble learning techniques, which capture intricate patterns often missed by simpler models [14]. This model’s performance surpasses traditional models like K-Nearest Neighbors, Random Forest, and Neural Networks, highlighting its reliability in clinical settings.

Our study’s SHAP value analysis revealed key factors—such as esketamine use, anesthesia methods, and types of anesthetic drugs—that significantly influence postoperative pain management outcomes. Particularly, esketamine’s role in delaying the first PCIA activation aligns with its known neuroprotective properties and its ability to mitigate central sensitization, as recent studies have shown [15, 16]. Integrating esketamine into current pain management protocols could significantly enhance recovery post-surgery [17].

Advocating for the integration of esketamine into multimodal analgesia regimens represents a proactive approach to pain management, especially in reducing opioid dependency. Supported by recent guidelines, this strategy highlights esketamine’s benefits as part of a comprehensive analgesic plan, particularly for patients at high risk for opioid dependency or with complex pain management needs [18]. Recent evidence suggests that the choice of adjuvant medications in spinal anesthesia significantly influences post-cesarean analgesic outcomes. Comparisons between α-2 agonists and fentanyl have shown distinct profiles in terms of analgesic efficacy and side effect profiles [19]. These findings highlight the importance of considering not only anesthesia techniques but also specific pharmacological adjuncts when developing personalized pain management protocols.

The SHAP analysis also indicated that the choice of anesthesia technique can profoundly impact clinical outcomes. Techniques like combined spinal-epidural anesthesia, associated with better analgesic profiles and reduced opioid use in recent trials [20, 21], advocate for a more personalized application of anesthesia based on patient-specific factors.

The necessity for personalized pain management strategies is clear, as our findings demonstrate significant variations in pain perception and management based on individual patient profiles, including conditions like diabetes and hypertension [22,23,24]. Tailoring pain management to address these conditions can lead to more effective and satisfying outcomes.

Our study has several limitations that warrant consideration. First, potential selection bias may exist, as participants were recruited from two specific hospital settings, which might not fully represent all cesarean delivery populations. Second, generalizability to different populations could be limited by demographic variations, practice patterns, and healthcare system differences. Future multi-center studies across diverse geographic regions and healthcare settings would strengthen the applicability of these findings.

While our study focused on clinical and anesthetic predictors of post-cesarean pain, socioeconomic factors and individual pain tolerance thresholds likely play important roles in postoperative outcomes. Previous research has demonstrated that socioeconomic status can influence access to preoperative care, nutritional status, and recovery environments, all of which may affect pain perception and management needs [25]. Additionally, psychosocial factors, such as mental health, should be incorporated in future studies to enrich our understanding of postoperative pain determinants [26, [27]. Future research that accounts for these broader contextual factors could further refine personalized pain management protocols and improve postoperative care for diverse patient populations.

To maximize the clinical utility of our machine learning models, seamless integration into existing healthcare infrastructures is essential. We propose the development of software plugins compatible with electronic health records (EHRs) that can automatically import patient data, run predictive models, and provide real-time pain management recommendations to clinicians. However, implementing machine learning models in clinical settings presents several practical challenges. First, integration requires a robust data infrastructure for real-time data collection and processing. Second, workflow redesign is necessary to incorporate model predictions effectively. Third, clinician training is essential to enable accurate interpretation and appropriate action based on model outputs. Additionally, ongoing validation and updating of the models will be necessary as clinical practices and patient populations evolve over time.

Conclusion

In conclusion, this study validates the effectiveness of the XGBoost model in predicting postoperative pain following a C-section. The critical roles of esketamine use and anesthesia methods in managing pain are highlighted by the model’s findings, suggesting that their integration into clinical practices could significantly refine pain management strategies, thereby improving recovery outcomes and enhancing patient satisfaction postoperatively.