Clinical prediction model for inguinal hernia recurrence risk after transabdominal preperitoneal hernia repair surgery in elderly patients

Abstract

BACKGROUND

Inguinal hernia is a common surgical condition in elderly individuals. Postoperative recurrence after laparoscopic transabdominal preperitoneal hernia repair (TAPP) remains an important clinical challenge. However, reliable predictive tools to assess the risk of recurrence are limited.

AIM

To establish and validate a clinical prediction model for the risk of recurrence after TAPP in elderly patients with inguinal hernia.

METHODS

Data from 300 elderly patients with inguinal hernia admitted to the Affiliated Hospital of Jiujiang University between January 2023 and January 2025 were retrospectively collected. All patients underwent TAPP in the hospital and were followed up for 24-72 months postoperatively. Inguinal hernia recurrence was observed; patients were subsequently divided into recurrence and non-recurrence groups. Differences in the general data between the two groups were compared. Logistic regression was used to identify factors associated with postoperative recurrence, and these multivariate factors were incorporated into R software to establish a visualized nomogram model. The patients were randomly divided into training and validation sets at a ratio of 7:3. Receiver operating characteristic curve, clinical calibration curve, and decision curve analysis were used to evaluate the diagnostic efficacy and clinical net benefit of the prediction model.

RESULTS

Among the 300 patients, 40 (13.33%) developed postoperative recurrence. Multivariate logistic regression analysis identified male sex, higher preoperative body mass index, smoking history, chronic constipation, incarcerated hernia, and polypropylene mesh use as independent risk factors for recurrence (P < 0.05). The nomogram prediction model demonstrated a good predictive performance, with area under the curve values of 0.895 and 0.933 in the training and validation sets, respectively.

CONCLUSION

Male sex, high preoperative body mass index, smoking history, chronic constipation, incarcerated hernia, and intraoperative use of polypropylene mesh are the major risk factors for inguinal hernia recurrence after TAPP in elderly patients. The constructed recurrence risk prediction model demonstrates high clinical efficacy, practicability, and net benefit, providing a reference for clinicians to screen high-risk patients.

Key Words: Elderly patients; Inguinal hernia; Laparoscopic transabdominal preperitoneal hernia repair; Recurrence; Clinical prediction model

Core Tip: Postoperative recurrence after transabdominal preperitoneal hernia repair remains a major concern in elderly patients with inguinal hernia. This study identified six independent risk factors associated with recurrence and developed a nomogram prediction model with favorable discrimination and calibration performance. This model may facilitate individualized perioperative risk assessment and early intervention strategies in elderly patients undergoing transabdominal preperitoneal hernia repair.

INTRODUCTION

Inguinal hernia is a common surgical condition that mainly occurs when the intra-abdominal contents gradually protrude through weak points in the inguinal region[1]. Surgery is the first-line treatment for inguinal hernias. With the development of minimally invasive techniques, laparoscopic transabdominal preperitoneal hernia repair (TAPP) has become the main surgical approach for inguinal hernias, with advantages such as minimal trauma, fewer complications, and rapid postoperative recovery[2]. However, elderly patients are at an increased risk of postoperative recurrence owing to multiple comorbidities, physiological characteristics, and a weak abdominal wall. Postoperative recurrence remains a challenge for both patients and clinicians. A previous study[3], reported that 3.6% of patients experienced inguinal hernia recurrence with a mean follow-up of 5.76 years after TAPP, requiring reoperation. In addition, no consensus has been reached on the etiology of postoperative inguinal hernia recurrence, which is generally considered to be associated with advanced age, smoking, obesity, and other factors[4,5].

Previous studies have explored the factors associated with postoperative recurrence after inguinal hernia repair, including obesity, smoking, mesh selection, and hernia characteristics. However, most studies have focused primarily on single risk factors or conventional regression analyses, and few have established individualized prediction tools for elderly patients undergoing TAPP. Additionally, elderly patients often present with reduced tissue healing capacity, multiple comorbidities, and increased intra-abdominal pressure, which may further increase the risk of recurrence. Therefore, developing a clinically applicable prediction model for this population is of substantial clinical importance.

MATERIALS AND METHODS

General data

Relevant data from 300 elderly patients with inguinal hernia admitted to the Affiliated Hospital of Jiujiang University between January 2023 and January 2025 were retrospectively collected.

Inclusion criteria: Patients were included if they: (1) Were diagnosed with inguinal hernia[6] based on clinical manifestations, ultrasound and other comprehensive examinations; (2) Were aged ≥ 60 years; (3) Met the surgical indications for TAPP; and (4) Had complete clinical and follow-up data.

Exclusion criteria: Patients were excluded if they had: (1) Severe multiple organ failure preventing surgery; (2) Severe coagulation dysfunction; (3) Active abdominal infection or severe abdominal adhesions affecting laparoscopic surgery; (4) Known allergy to mesh materials; and (5) Malignant tumors with an expected survival of less than 1 year.

Collection of clinical data

Researchers collected patients’ clinical data on admission, including: (1) Demographic data included age, sex, preoperative body mass index (BMI), smoking history, and drinking history; (2) Comorbidities such as hypertension, diabetes mellitus, coronary heart disease, hyperlipidemia, constipation, and hernia type; and (3) Surgical conditions such as emergency surgery, anesthesia method, operation time, patch type, and patch size.

Follow-up outcomes

All patients were followed up for 24-72 months after surgery through outpatient visits, telephone interviews, and community-based follow-ups. Community follow-up refers to postoperative health monitoring performed by community healthcare providers or local healthcare service centers, including symptom assessment, physical examination, and postoperative recovery evaluation.

The follow-up duration varied because the patients underwent surgery at different time points during the enrollment period (January 2023 to January 2025). Patients enrolled earlier had longer follow-up durations, whereas those enrolled later had shorter follow-up periods.

The overall postoperative inguinal hernia recurrence rate was defined as the proportion of patients who developed recurrent inguinal hernias during the follow-up period. Recurrence was diagnosed based on the presence of inguinal bulging confirmed by physical and/or imaging examinations, accompanied by reducible or irreducible masses, local discomfort, or pain during increased intra-abdominal pressure.

Observation indicators

Postoperative recurrence rate: The recurrence rate of inguinal hernia was calculated during 24-72 months of postoperative follow-up. The clinical data between recurrence and non-recurrence groups was compared, and a clinical prediction model was developed. The diagnostic efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, clinical calibration curves, and decision-making curve analyses (DCA).

Statistical analysis

Data processing and analysis were performed using IBM SPSS statistics for Windows software, version 26.0. Measurement data conforming to a normal distribution were expressed as mean ± SD and compared using the t-test. Enumeration data were expressed as n (%) or frequencies and compared using the χ² test. The R software package was used to draw the nomograms, ROC curves, and DCA curves. Multivariate logistic regression analysis was performed. Statistical significance was set at P < 0.05.

RESULTS

Baseline data of patients

A total of 300 patients were followed up for 24-72 months postoperatively, and 40 patients (13.33%) experienced postoperative recurrence. Significant differences were observed between the two groups in terms of sex, preoperative BMI, smoking history, chronic constipation, and incarcerated hernia (P < 0.05), whereas other baseline characteristics showed no statistically significant differences (P > 0.05; Table 1).

Table 1 Comparison of demographic characteristics and baseline disease data between the two groups, n (%)/mean ± SD.

Characteristic	Recurrence group (n = 40)	Nonrecurrence group (n = 260)	χ2/t	P value
Sex			6.909	0.006
Male	38 (95.00)	200 (76.92)
Female	2 (5.00)	60 (23.08)
Age			0.934	0.627
60-70 years	12 (30.00)	61 (23.46)
71-80 years	24 (60.00)	165 (63.46)
> 80 years	4 (10.00)	34 (13.08)
Preoperative BMI (kg/m2)	24.84 ± 2.04	22.07 ± 2.16	7.605	< 0.001
Smoking history	18 (45.00)	26 (10.00)	23.276	< 0.001
Drinking history	2 (5.00)	21 (8.08)	0.389	0.533
Hypertension	17 (42.50)	119 (45.77)	0.150	0.699
Diabetes mellitus	6 (15.00)	32 (12.31)	0.227	0.634
Coronary heart disease	3 (7.50)	20 (7.69)	0.077	0.782
Hyperlipidemia	20 (50.00)	136 (52.31)	0.074	0.786
Unilateral hernia	34 (85.00)	206 (79.23)	0.721	0.396
Bilateral hernia	6 (15.00)	54 (20.77)
Chronic constipation	12 (30.00)	25 (9.62)	11.504	0.001
Hernia type			0.366	0.833
Indirect hernia	31 (77.50)	201 (77.31)
Direct hernia	6 (15.00)	33 (12.69)
Combined hernia	3 (7.50)	26 (10.00)
Hernia sac adhesion			0.752	0.386
Mild	35 (87.50)	213 (81.92)
Moderate-to-severe	5 (12.50)	47 (18.08)
Incarcerated hernia	14 (35.00)	18 (6.92)	25.808	< 0.001

BMI: Body mass index.

Intraoperative conditions

The proportion of patients who used a polypropylene mesh was significantly higher in the recurrence group than in the non-recurrence group [11/40 (27.50%) vs 18/260 (6.92%), P < 0.001], indicating that intraoperative polypropylene mesh application may be associated with an increased risk of postoperative recurrence. There were no significant differences in the other intraoperative variables between the two groups (P > 0.05; Table 2).

Table 2 Comparison of surgical outcomes between the two groups, n (%)/mean ± SD.

General data	Recurrence group (n = 40)	Nonrecurrence group (n = 260)	χ2/t	P value
Emergency surgery	18 (45.00)	118 (45.38)	0.002	0.964
Anesthesia method			0.140	0.708
Intraspinal anesthesia	26 (65.00)	161 (61.92)
Local infiltration anesthesia	14 (35.00)	99 (38.08)
Operation time (minutes)	78.81 ± 12.14	80.06 ± 14.75	0.510	0.611
Mesh type			14.535	< 0.001
Polypropylene mesh	11 (27.50)	18 (6.92)
Polytetrafluoroethylene mesh	29 (72.50)	242 (93.08)
Mesh size			0.038	0.845
8 cm × 12 cm	6 (15.00)	36 (13.85)
10 cm × 15 cm	8 (20.00)	58 (22.31)
12 cm × 15 cm	18 (45.00)	121 (46.54)
15 cm × 15 cm	8 (20.00)	45 (17.31)
Chronic postoperative pain	3 (7.50)	28 (10.77)	0.400	0.527
Postoperative hematoma	4 (10.00)	30 (11.54)	0.003	0.986
Postoperative infection	1 (2.50)	3 (1.15)	0.002	0.961

Multivariate analysis

Indicators with statistically significant differences were used for further analysis. Postoperative recurrence was set as the dependent variable, and other indicators as independent variables, with “N = 1” representing “yes” and “N = 0” representing “no”; detailed assignments are shown in Table 3.

Table 3 Assignment analysis.

Variable	Assignment
Sex	Male = 1, female = 0
Preoperative BMI	Continuous variable
Smoking history	Yes = 1, no = 0
Chronic constipation	Yes = 1, no = 0
Incarcerated hernia	Yes = 1, no = 0
Polypropylene mesh patch	Yes = 1, no = 0

In the variable assignment process, “yes” was coded as 1 and “no” was coded as 0 for binary variables included in logistic regression analysis. BMI: Body mass index.

Logistic regression analysis revealed that male sex, preoperative BMI, smoking history, chronic constipation, incarcerated hernia, and intraoperative use of a polypropylene mesh were factors that influenced recurrence after TAPP (P < 0.05), as shown in Table 4.

Table 4 Multivariate logistic regression analysis.

Risk factor	β	SE	Wald	P value	OR	95%CI
Male	0.953	0.305	9.763	0.001	2.593	1.846-3.641
Preoperative BMI	0.588	0.236	6.208	0.008	1.800	1.147-2.826
Smoking history	0.739	0.282	6.867	0.006	2.093	1.392-3.147
Chronic constipation	1.150	0.406	8.023	0.002	3.159	2.104-4.742
Incarcerated hernia	0.856	0.305	7.877	0.004	2.353	1.748-3.168
Polypropylene mesh patch	0.709	0.281	6.366	0.008	2.031	1.500-2.751

BMI: Body mass index; OR: Odds ratio; CI: Confidence interval.

Construction of the nomogram prediction model

An interactive nomogram was constructed using the R software based on multivariate factors (male sex, preoperative BMI, smoking history, chronic constipation, incarcerated hernia, and polypropylene mesh). Total points = linear predictor × standardized coefficient + score offset. A mapping axis of “variable value - points” was established for each independent variable. When the total nomogram score exceeded 80 points, the corresponding recurrence probability was > 50% (Figure 1).

Figure 1 Column diagram of clinical prediction model. BMI: Body mass index.

Clinical performance of the risk prediction model

The patients were randomly divided into training and validation sets at a ratio of 7:3. ROC curve analysis showed that the training set had an area under the curve of 0.895, sensitivity of 75.90%, and specificity of 88.50%, whereas the validation set had an area under the curve of 0.933, sensitivity of 81.80%, and specificity of 83.10%, as shown in Table 5 and Figure 2.

Figure 2 The receiver operating characteristic curve of model predictions for training set and validation set. TPR: True positive rate; FPR: False rejection rate; AUC: Area under the curve; CI: Confidence interval.

Table 5 Clinical efficacy of the postoperative recurrence risk prediction model.

Item	AUC	95%CI	Sensitivity	Specificity
Training set	0.895	0.831-0.959	75.90%	88.50%
Validation set	0.933	0.828-1.000	81.80%	83.10%

Area under the curve refers to the area under the curve. AUC: Area under the curve; CI: Confidence interval.

Calibration ability

The calibration ability of the prediction model was evaluated using clinical calibration curves. For the training set, the C-index was 0.905, and the Hosmer-Lemeshow (HL) goodness-of-fit test showed χ² = 13.854, degrees of freedom = 8, P = 0.087. For the validation set, the C-index was 0.964, and the HL test showed χ² = 13.764, degrees of freedom = 8, P = 0.088. The model exhibited an excellent calibration performance (Figure 3).

Figure 3  The clinical calibration curves for training set and validation set.

Clinical net benefit

The clinical prediction model was evaluated using DCA. The risk threshold ranged from 0-0.8 in the training set and from 0-0.9 in the validation set. The net benefit was consistent between the training and validation sets, indicating high clinical practical value (Figure 4).

Figure 4  The decision-making curve analyses curves for training set and validation set.

DISCUSSION

Inguinal hernia is a common clinical disorder mainly caused by weakened abdominal muscles and elevated intra-abdominal pressure, leading to the formation of a mass protruding into the body surface in the inguinal region. It is particularly prevalent in the elderly population. Muscle atrophy and increased abdominal wall weakness in elderly individuals result in a significantly higher incidence of inguinal hernia in this group[7]. Currently, TAPP is one of the primary surgical procedures for the treatment of inguinal hernias. The unique anatomical structure of the inguinal region does not interfere with intraoperative manipulation, and the procedure can improve the detection rate of special hernias, such as composite and occult hernias, thereby facilitating surgery and enhancing surgical safety and accuracy[8]. However, postoperative recurrence remains a major factor that affects postoperative recovery in elderly patients. The present study found that among 300 patients followed-up for 24-72 months postoperatively, the postoperative recurrence rate was 13.33%. Chaoui et al[9] reported a recurrence rate of 21.7% in 89 patients after mesh removal. Jung et al[10] noted a postoperative recurrence rate of 7.0% in patients with inguinal hernias. Collectively, these findings indicate that postoperative recurrence of inguinal hernia cannot be overlooked. As this study enrolled only elderly patients, their loose peritoneal structure, persistently elevated intra-abdominal pressure, and multiple comorbidities may have further increased the risk of postoperative recurrence. Therefore, identifying the risk of recurrence and the factors influencing inguinal hernia after TAPP in elderly patients is conducive to early postoperative prevention, evaluation, and intervention, thereby reducing the postoperative recurrence rate.

The results of this study demonstrated that male sex, preoperative BMI, smoking history, chronic constipation, incarcerated hernia, and intraoperative use of polypropylene mesh were independent influencing factors for recurrence after TAPP (P < 0.05), suggesting that multiple factors are associated with the postoperative recurrence of inguinal hernia in elderly patients. The underlying mechanisms were analyzed as follows: (1) Male sex: Compared to females, males have a larger internal inguinal ring and wider inguinal canal due to anatomical differences, which increases surgical difficulty. Moreover, most male patients present with a direct hernia; under external stressors such as obesity and manual labor, abdominal wall injury or weakness may occur, thereby elevating the risk of postoperative recurrence[11]; (2) High preoperative BMI index: Obesity is a well-established risk factor for the postoperative recurrence of inguinal hernias. Bhardwaj et al[12] reported that a higher BMI is closely associated with postoperative recurrence. Obesity increases intra-abdominal pressure and tension at the repaired abdominal wall defect, which may lead to loosening or tearing of the prosthetic material, thus raising the risk of recurrence. Elderly obese patients exhibit reduced strength and elasticity of the abdominal wall tissues and impaired tissue healing capacity, which compromises postoperative recovery and further increases the likelihood of recurrence[13]; (3) Smoking: Smoking is a widely recognized risk factor for postoperative hernia recurrence. da Silveira et al[14] found that smokers had a higher recurrence rate of inguinal hernia (10.4% vs 9.1%; relative risk = 1.48; 95% confidence interval: 1.15-1.90; P < 0.01) and a significantly higher incidence of postoperative complications. Long-term smoking reduces collagen deposition, disrupts the balance between systemic proteases and antiproteases, and damages the connective tissues of the abdominal wall. In addition, chronic smoking increases coughing and expectoration, which further elevates intra-abdominal pressure and reduces the tensile strength of the abdominal wall incision, thereby increasing the risk[15]; (4) Chronic constipation: Wang et al[16] identified chronic constipation as an influencing factor for postoperative recurrence of inguinal hernia. Patients with chronic constipation exert excessive force during defecation, which leads to increased intra-abdominal pressure. Prolonged elevated pressure may damage the abdominal wall tissue and enlarge the hernia defects, thereby increasing the risk of recurrence. Moreover, the accumulation of large amounts of feces in the intestinal tract increases intestinal weight and volume, which increases mechanical stress and tension on the abdominal wall, impairs healing of its tissues, and ultimately promotes postoperative recurrence[17]; (5) Incarcerated hernia: Anwar et al[18] reported that patients with incarcerated hernias had higher postoperative recurrence and complication rates. Incarcerated hernias are often accompanied by altered pathology and anatomy, including an enlarged hernia sac and wide hernia neck. These changes disrupt the structure and strength of the posterior inguinal canal wall, reducing implanted mesh stability, and predisposing patients to mesh displacement, thereby increasing the recurrence risk[19]. In addition, hernial contents may involve intra-abdominal organs such as the intestine, ovary, or bladder, which leads to edema, ischemia, or necrosis of the incarcerated organs, thereby impairing the abdominal wall healing and increasing recurrence. Furthermore, peritoneal injury is common during surgery for incarcerated hernia, often requiring suture repair with silk thread, and reduced strength of the repaired peritoneum may lead to tissue entrapment and subsequent recurrence[20,21]; and (6) Polypropylene mesh: Paasch et al[22] observed a 1.6% recurrence rate with intraoperative polypropylene mesh placement, which was non-inferior to steel wire placement, and identified it as a major factor associated with postoperative hernia recurrence. Polypropylene mesh is widely used in TAPP because of its favorable anti-infective properties; however, contact with visceral organs may induce intra-abdominal adhesion. Meanwhile, the large pores of the mesh cannot block microorganisms, allowing free migration of polymorphonuclear leukocytes, which increases complications such as foreign body sensation and postoperative pain, thereby increasing the risk of hernia recurrence[23,24]. In conclusion, multiple factors contribute to the postoperative recurrence of inguinal hernias. Clinicians should pay close attention to these risk factors and implement targeted preventive strategies to reduce postoperative recurrence rates.

With the rapid development of machine learning algorithms and their wide application in medicine, the construction of risk prediction models using these techniques have attracted considerable clinical attention. The interactive nomogram prediction model is an intuitive and easy-to-understand visualization tool. When presented in graphical form, the model effectively characterizes the relationships among variables to provide clinicians with personalized risk assessments and predictive recommendations. By improving the transparency of medical decision-making, clearly displaying disease risk and the prediction process, this tool accurately estimates the event probability to support evidence-based perioperative management and interventions[25].

This nomogram may have a practical clinical value for perioperative management. For patients identified as high-risk before surgery, clinicians should strengthen smoking cessation education, optimize body weight management, improve constipation control, and carefully select mesh materials and fixation strategies. Furthermore, elderly patients with incarcerated hernias may benefit from intensified postoperative follow-up and imaging surveillance to facilitate the early detection of recurrence. Compared to conventional statistical analyses, this nomogram model enables individualized recurrence risk estimation and improves clinical decision-making.

The results of this study showed that after constructing a multivariate nomogram, ROC curve analysis revealed an area under the curve of 0.895, sensitivity of 75.90%, and specificity of 88.50% in the training set, and the corresponding values in the validation set were 0.933, 81.80%, and 83.10%, respectively. Assessment using clinical calibration curves demonstrated a Cindex of 0.905 and a HL goodnessoffit statistics of χ² = 13.854 in the training set, and a Cindex of 0.964 and χ² = 13.764 in the validation set, indicating excellent model calibration. DCA showed a risk threshold range of 0-0.8 in the training set and 0-0.9 in the validation set.

These findings indicate that the postoperative risk prediction model based on factors such as male sex, preoperative BMI, smoking history, chronic constipation, incarcerated hernia, and intraoperative use of polypropylene mesh yields a high diagnostic performance in predicting hernia recurrence after TAPP in elderly patients. This model aligns closely with real-world clinical conditions, provides a high net clinical benefit, and offers a reliable scientific basis for optimizing perioperative management.

This study had several limitations. First, this was a retrospective, single-center study, which may have introduced a selection bias. Second, the sample size of the recurrence group was relatively small compared to that of the non-recurrence group. Third, an external validation was not performed. Future multicenter prospective studies with larger sample sizes are warranted to validate and optimize the prediction model.

CONCLUSION

Multiple factors are associated with recurrence after TAPP for inguinal hernias in elderly patients, including male sex, high BMI, smoking, chronic constipation, incarcerated hernia, and intraoperative use of a polypropylene mesh. Incorporating these factors into the R software to construct a visualized nomogram model for recurrence risk after TAPP resulted in high diagnostic efficacy and a favorable net clinical benefit. It can serve as a useful reference for clinicians to screen high-risk patients, and has strong clinical practicability.