Development and Validation of a Machine Learning-Based Risk Prediction Model for Recurrence After Percutaneous Balloon Compression in Trigeminal Neuralgia Patients: A Retrospective Cohort Study
Conditions: Trigeminal Neuralgia
Sponsor: Second Xiangya Hospital of Central South University

Risk Factors for Recurrence of Trigeminal Neuralgia 
After Percutaneous Balloon Compression 
--------Development and Validation of a Machine Learning-Based 
Risk Prediction Model for Recurrence After Percutaneous Balloon 
Compression in Trigeminal Neuralgia Patients: A Retrospective 
Cohort Study 
Version 1.0    Version date 2025.07.30 
1. Research Background and Rationale 
Trigeminal Neuralgia (TN) is a disorder characterized by recurrent paroxysmal severe pain 
within the distribution area of the trigeminal nerve, with diagnosis confirmed in accordance 
with the criteria of the International Classification of Headache Disorders, 3rd edition (ICHD-
3) [1]. Epidemiological studies have shown that the annual incidence of TN ranges from 4.3 
to 28.9 per 100,000 population, and the prevalence can reach 10 to 30 per 100,000 
population. Middle-aged and elderly individuals are the main affected group, with a slightly 
higher incidence in women. This disease causes severe impairment to patients' quality of 
life, mental health, and social functioning [2, 3]. 
For TN patients who are unresponsive to drug therapy or unable to tolerate drug adverse 
reactions, surgical intervention becomes a necessary treatment option. Among various 
interventional treatment methods, Percutaneous Balloon Compression (PBC), first reported 
by Mullan and Lichtor in 1983 [4], has become the preferred minimally invasive treatment for 
elderly patients, those with multiple comorbidities, or those who refuse craniotomy, due to its 
advantages such as relatively simple operation, short anesthesia time, and avoidance of 
craniotomy-related risks [5]. Studies have confirmed that the immediate pain relief rate (BNI 
Grade I-III) after PBC can exceed 90% [6]. 
However, the long-term efficacy of PBC is limited by pain recurrence. Long-term follow-up 
studies have revealed that pain recurrence after PBC exhibits a significant time-dependent 
pattern: the 1-year recurrence rate is 8%-15%, the 3-year recurrence rate increases to 22%-
30%, and the 5-year cumulative recurrence rate reaches 20%-35% [7, 8]. The mechanism of 
recurrence involves the combined action of multiple factors, mainly including: (1) Baseline 
patient characteristics: Comorbid multiple sclerosis (HR = 3.21) [9], long-term hypertension 
(HR = 1.87) [10], long disease duration (>5 years), and non-classical TN are all important 
risk factors [11]; (2) Surgical operation parameters: The formation of an "ideal pear-shaped" 
balloon [12] and appropriate balloon compression parameters (volume ratio 1.5-1.8, 
pressure 138-154 kPa, duration 60-120 seconds) [13] are critical for prognosis; (3) 
Anatomical factors: Narrow foramen ovale (long diameter <7 mm) and abnormal Meckel's 
cave volume also significantly affect treatment outcomes [14]. 

Despite numerous studies investigating the influencing factors of recurrence after PBC, 
existing studies have obvious limitations: Firstly, the sample size is generally small. Most 
single-center studies have a sample size of less than 200 cases, lacking statistical power to 
examine complex relationships in high-dimensional data [17, 18]; Secondly, the construction 
of prediction models is incomplete. Traditional statistical methods such as Cox regression 
have limitations in handling complex non-linear relationships and interaction effects between 
variables [19]; Thirdly, external validation is insufficient. Most existing prediction models only 
undergo internal validation in the development cohort, lacking external validation in 
independent cohorts, which affects the generalization ability of the models [20]; Finally, the 
integration of predictors is inadequate. Most studies fail to systematically integrate multi-
dimensional information such as clinical characteristics, imaging parameters, and surgical 
operation variables [21]. 
Compared with traditional statistical methods, machine learning algorithms can better handle 
high-dimensional data, automatically capture complex relationships between predictors and 
outcomes, and show significant advantages in large-sample data [22]. Ensemble learning 
methods such as Random Forest and Gradient Boosting Machine are particularly suitable for 
addressing non-linear relationships and interaction effects in clinical data [23]. 
The research center where the authors are located has performed nearly 700 cases of 
balloon compression surgery for trigeminal neuralgia and possesses rich clinical data. 
Therefore, based on a large-sample cohort of 700 cases, this study aims to systematically 
develop and validate a prediction model for recurrence after PBC using machine learning 
methods, so as to make up for the deficiencies of existing studies, provide a personalized 
risk assessment tool for clinical practice, and promote the development of PBC treatment 
towards precision medicine. 
References 
[1] Headache Classification Committee of the International Headache Society. The 
International Classification of Headache Disorders, 3rd edition. Cephalalgia. 2018;38(1):1-
211. 
[2] Hall GC, et al. Epidemiology and surgery for trigeminal neuralgia in England. Neurology. 
2021;97(5):e505-e516. 
[3] Maarbjerg S, et al. Trigeminal neuralgia - diagnosis and treatment. Cephalalgia. 
2017;37(7):648-657. 
[4] Mullan S, Lichtor T. Percutaneous microcompression of the trigeminal ganglion for 
trigeminal neuralgia. J Neurosurg. 1983;59(6):1007-1012. 
[5] Brown JA, et al. Percutaneous balloon compression of the trigeminal ganglion for 
trigeminal neuralgia. J Neurosurg. 2023;139(2):381-389. 
[6] Torres CV, et al. Long-term outcomes of percutaneous balloon compression for 
trigeminal neuralgia. Acta Neurochir (Wien). 2024;166(1):25. 
[7] Zhao Y, et al. A prospective randomized controlled trial comparing two percutaneous 
balloon compression protocols. Neurosurgery. 2025;96(2):345-354. 

[8] Zheng X, et al. Ten-year follow-up of percutaneous balloon compression for trigeminal 
neuralgia. J Neurosurg. 2022;137(4):1009-1016. 
[9] Xu S, et al. Efficacy and safety of percutaneous balloon compression for trigeminal 
neuralgia secondary to multiple sclerosis. Neurosurg Rev. 2023;46(1):45. 
[10] Lv J, et al. Association between hypertension and long-term outcomes after 
percutaneous balloon compression. Clin Neurol Neurosurg. 2022;222:107448. 
[11] Ding Y, et al. Impact of symptom duration and classical vs non-classical presentation on 
outcomes. World Neurosurg. 2023;170:e898-e905. 
[12] Lee ST, et al. The significance of pear-shaped balloon in percutaneous balloon 
compression. Acta Neurochir (Wien). 2021;163(8):2279-2286. 
[13] Chen J, et al. Optimizing compression parameters in percutaneous balloon 
compression. Neurosurg Focus. 2024;56(3):E8. 
[14] Wang Y, et al. Preoperative evaluation of foramen ovale morphology and size using CT. 
Clin Anat. 2022;35(3):327-334. 
[15] Song Y, et al. Patient-reported outcome measures and satisfaction after percutaneous 
balloon compression. Pain Med. 2023;24(7):825-833. 
[16] Texakalidis P, et al. Comparative safety and efficacy of surgical interventions for 
trigeminal neuralgia. Neurosurg Rev. 2024;47(1):45. 
[17] Smith J, et al. Limitations of small sample sizes in predicting outcomes of percutaneous 
procedures. J Clin Epidemiol. 2022;145:176-185. 
[18] Johnson MA, et al. Statistical power considerations in neurosurgical prediction models. 
Neurosurgery. 2023;92(4):789-797. 
[19] Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, 
Validation, and Updating. 2nd ed. Springer; 2019. 
[20] Collins GS, et al. Transparency in reporting prediction models. JAMA. 
2023;330(10):963-964. 
[21] Anderson CD, et al. Integrating multimodal data for improved prediction of surgical 
outcomes. Nat Med. 2024;30(2):456-467. 
[22] Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical 
introduction. BMC Med Res Methodol. 2019;19(1):64. 
[23] Liu Y, et al. Machine learning for predicting outcomes of surgical treatment for trigeminal 
neuralgia. J Neurosurg. 2024;140(2):495-505. 
2. Research Objectives 
2.1 Primary Objective 

To develop a clinical prediction model based on machine learning algorithms for 
individualized prediction of pain recurrence risk in patients with primary trigeminal neuralgia 
after PBC. 
2.2 Secondary Objectives 
(1) Identify and evaluate key predictors influencing recurrence after PBC. 
(2) Compare the predictive performance of different machine learning algorithms with the 
traditional Cox proportional hazards regression model. 
(3) Transform the final model into a clinically applicable Nomogram or online risk calculator. 
3. Study Design 
Study Type: Single-center, retrospective, observational cohort study. 
Guideline Compliance: This study will adhere to the Transparent Reporting of a 
multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. 
Study Population: All patients with primary TN who met the inclusion criteria and underwent 
their first PBC treatment in the Department of Pain Medicine, the Second Xiangya Hospital 
of Central South University, from January 1, 2018, to June 30, 2025. 
Sample Size: A total of 700 patients are expected to be included. With recurrence as the 
endpoint event, the expected number of events will be more than 175, which meets the 
requirement for the number of events in machine learning model development. 
4. Study Subjects 
4.1 Inclusion Criteria 
(1) Aged ≥ 18 years. 
(2) Meet the diagnostic criteria for primary trigeminal neuralgia according to the International 
Classification of Headache Disorders, 3rd edition (ICHD-3). 
(3) Undergo the first PBC treatment. 
(4) Have complete preoperative clinical data, imaging data, and intraoperative records. 
(5) Have at least one postoperative follow-up record available for determining the recurrence 
status. 
4.2 Exclusion Criteria 
(1) Secondary trigeminal neuralgia (e.g., caused by cerebellopontine angle tumors, multiple 
sclerosis, etc.). 
(2) Missing rate of key predictor variables (e.g., balloon shape) or outcome variables > 15%. 
(3) Postoperative loss to follow-up (defined as no follow-up records available). 

5. Study Variables and Definitions 
5.1 Outcome Variable (Dependent Variable) 
Definition: Pain recurrence (dichotomous variable: Yes/No). 
Recurrence Criteria: Postoperative Barrow Neurological Institute (BNI) pain intensity score 
returns to Grade IV ("persistent pain, controllable with medication") or Grade V ("severe 
pain, uncontrollable with medication"), lasting for ≥ 1 week, and requiring an increase in 
medication dose or reoperation. 
Time Indicator: From the date of surgery to the date of recurrence or the date of the last 
follow-up. For patients without recurrence, data will be censored at the date of their last 
follow-up. 
Recurrence-Free Survival (RFS): Recurrence-free survival will be calculated. 
5.2 Predictor Variables (Independent Variables) 
Data will be extracted from the electronic medical record system, covering the following 
dimensions: 
(1) Demographic Data: Age, gender. 
(2) Baseline Clinical Characteristics: Disease duration, affected side of pain, involved 
branches, preoperative BNI score, presence of trigger points, presence of persistent 
background pain, comorbidities (diabetes, hypertension, etc.), preoperative biochemical 
indicators (inflammatory factors), preoperative sleep score, and anxiety scale score. 
(3) Imaging Anatomical Parameters: Measured based on preoperative images (MRI/CT), 
such as foramen ovale size (long diameter, area), Meckel's cave volume and shape, and 
presence of neurovascular compression. 
(4) Surgical Operation Parameters: Balloon shape (standard pear-shaped/non-standard 
pear-shaped), balloon inflation volume, balloon compression time, number of punctures, and 
guidance method (C-arm fluoroscopy/3D-CT navigation). 
(5) Postoperative Management: Postoperative medication tapering strategy. 
6. Data Collection and Management 
(1) Data Sources: Hospital Information System (HIS), Picture Archiving and Communication 
System (PACS), surgical anesthesia system, and outpatient follow-up system. 
(2) Data Extraction: Trained researchers will extract data using a standardized Case Report 
Form (CRF). 
(3) Data Anonymization: All patient identifiers will be removed and replaced with unique 
study numbers. 
(4) Data Management: Data entry and storage will be conducted using data collection 
systems such as Epidata, with logical verification functions set to ensure data quality. 

7. Statistical Analysis Plan 
7.1 Descriptive Statistical Analysis 
For continuous variables, those conforming to a normal distribution will be described using 
mean ± standard deviation, while those not conforming to a normal distribution will be 
described using median (interquartile range). For categorical variables, frequency 
(percentage) will be used for description. Patients will be grouped by recurrence status, and 
inter-group differences will be compared. For continuous variables, t-test or Mann-Whitney U 
test will be used based on data distribution; for categorical variables, chi-square test or 
Fisher's exact test will be used. 
7.2 Data Processing 
Missing Value Handling: For continuous variables, if the missing rate is low (<5%), median 
or mean will be used for imputation; if the missing rate is high (≥5%), multiple imputation will 
be used. For categorical variables, the mode category will be used for imputation, or 
"missing" will be set as a separate category. 
Data Splitting: The total dataset will be randomly divided into a training set and a test set at 
a ratio of 7:3. The training set will be used for model development and tuning, and the test 
set will be used for verifying the final model performance. 
7.3 Model Development and Comparison 
Baseline Model: The Cox proportional hazards regression model will first be used as the 
baseline model. Variable selection will be based on clinical importance and univariate 
analysis results, and stepwise regression (forward/backward) will be used to construct the 
multivariable model. 
Machine Learning Models: The following machine learning survival analysis models will be 
trained on the training set: 
Random Survival Forest 
Gradient Boosting Machine (using Cox proportional hazards loss function) 
CoxBoost 
Survival Support Vector Machine 
Model Training and Tuning: 5-fold or 10-fold cross-validation will be used to adjust the 
hyperparameters (e.g., learning rate, tree depth, node size) of each machine learning model 
to optimize model performance and prevent overfitting. 
7.4 Model Performance Evaluation 
The performance of all models will be evaluated on the independent test set. 
Discrimination: The main evaluation indicator is the Concordance Index (C-index). For 
time-dependent discrimination, the Time-dependent Area Under the Receiver Operating 

Characteristic Curve (Time-dependent AUC) at 1 year, 2 years, and 3 years will be 
calculated. 
Calibration: Calibration curves will be plotted to compare the consistency between the 
predicted recurrence probability and the actually observed recurrence probability. The Brier 
score will be calculated as a comprehensive evaluation indicator. 
Clinical Utility: Decision Curve Analysis (DCA) plots will be drawn to evaluate the clinical 
net benefit of the model at different threshold probabilities and determine its clinical 
applicability. 
7.5 Model Interpretation and Visualization 
For the model with the best performance, SHapley Additive exPlanations (SHAP) values will 
be used for interpretation to assess the direction and magnitude of the contribution of each 
predictor to the individual prediction result. SHAP summary plots and SHAP dependence 
plots will be drawn to visualize the global and local effects of key predictors. Finally, a 
Nomogram will be plotted based on the results of the optimal model (or Cox model) for direct 
clinical use. 
7.6 Software 
All statistical analyses will be performed using R language (version 4.3.0 or higher). The 
main R packages used will include: survival, randomForestSRC, gbm, CoxBoost, rms, 
timeROC, ggDCA, shap, etc. 
8. Ethics and Confidentiality 
This study has been approved by the Ethics Review Committee of the Second Xiangya 
Hospital of Central South University (Approval No. LYF2022139). As a retrospective study, 
no additional intervention will be performed on patients, and a waiver of informed consent 
has been applied for. All study data will undergo strict de-identification, and only analyzed in 
the form of serial numbers. Data access rights will be restricted to members of the research 
team, and all data will be stored on an institutional encrypted server. 
9. Research Plan 
October 2025 - November 2025: Data extraction and cleaning. 
November 2025 - December 2025: Descriptive analysis, model development and training, 
model validation and performance evaluation. 
January 2026 - February 2026: Result interpretation, manuscript writing and submission. 
10. Expected Outcomes and Dissemination 
The research results are expected to be published in international peer-reviewed journals 
and presented at relevant academic conferences. The developed prediction model will be 
transformed into an online clinical calculator for use and validation by peers. 

Protocol Developer: Professor Yaping Wang 
Principal Investigator: Professor Yaping Wang