A Predictive Nomogram for Post-Cesarean Infections: Risk Factors and Clinical Implications

Introduction

Cesarean section (CS) is one of the most frequently performed obstetric procedures worldwide, often performed to address maternal or fetal complications[1]. Despite advancements in surgical techniques, perioperative management, and infection control strategies, postoperative infections remain a major clinical and economic concern, contributing to increased maternal morbidity, prolonged hospital stays, additional medical interventions, and elevated healthcare costs [2]. The global burden of these infections also extends to long-term maternal health outcomes, with potential consequences such as chronic pelvic pain, impaired wound healing, and reduced quality of life [3]. Effective prevention strategies are essential to minimize these complications and optimize clinical outcomes [4]. A substantial body of research has explored risk factors for postoperative infections following cesarean delivery [5]. These risk factors include maternal obesity, diabetes mellitus, prolonged rupture of membranes, excessive intraoperative blood loss, and emergency procedures [6]. Over time, studies have refined our understanding of these risk factors, leading to improved clinical interventions such as standardized antibiotic prophylaxis and surgical site infection (SSI) prevention bundles. However, despite these efforts, postoperative infections continue to pose a significant challenge.

Current predictive models and clinical scoring systems often lack sufficient specificity, sensitivity, or applicability in real-world settings [7]. Additionally, there is limited consensus on the best approach to stratify infection risk at an individual level [8]. Some models focus primarily on single risk factors, failing to integrate the multifactorial nature of postoperative infections [9]. Furthermore, inconsistencies exist regarding the role of certain clinical and procedural factors, such as excessive vaginal examinations and complete cervical dilation, in influencing infection risk[10]. These knowledge gaps highlight the need for a more precise and clinically relevant predictive tool[11]. Nomogram models have emerged as effective tools for predicting patient-specific clinical outcomes, offering a user-friendly graphical approach to risk estimation. Unlike conventional scoring systems, nomograms integrate multiple variables into a single predictive framework, enhancing accuracy and individualized risk assessment[12]. In the context of cesarean section, an infection risk nomogram could facilitate early risk stratification, allowing clinicians to adjust antibiotic prophylaxis, enhance postoperative monitoring, and personalize infection prevention strategies[13]. However, few studies have developed a validated nomogram specifically for postoperative infections following cesarean delivery [14–16].

This study aims to fill this gap by identifying key risk factors for postoperative infections after cesarean section and developing a predictive nomogram model. To address this gap, we hypothesize that specific risk factors, including maternal comorbidities, obstetric interventions, and intraoperative variables, contribute significantly to the development of postoperative infections following cesarean sections. By leveraging multivariable logistic regression and internal validation techniques, we aim to provide a clinically applicable tool for real-time risk assessment. The findings of this study have the potential to inform clinical guidelines, improve early infection detection, and optimize perioperative care strategies to reduce maternal morbidity and healthcare burden.

Material and Methods

STUDY DESIGN:

A retrospective evaluation was conducted at our institution from January 2021 to December 2023 to analyze risk factors and assess the predictive value of a nomogram model for postoperative infections in patients undergoing cesarean section. Eligible participants included those who underwent elective or urgent cesarean delivery under standardized surgical and anesthetic protocols, were aged ≥18 years at the time of surgery, had complete intraoperative and postoperative records available, exhibited no active infection as confirmed by preoperative evaluation and laboratory tests, and provided written informed consent. Patients with incomplete medical records, significant comorbidities or immunocompromised conditions that could confound infection risk, or those who declined to participate or withdrew their consent were excluded. A total of 685 patients met the inclusion criteria, including 652 individuals without postoperative infections (control group) and 33 individuals with postoperative infections (infection group). The study methodology, objectives, and procedures adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [17] guidelines. Informed consent was obtained from all subjects and/or their legal guardian(s). The study’s methodology, intent, and protocols underwent rigorous review by our hospital’s ethics committee and adhered to relevant guidelines and the Declaration of Helsinki. All procedures were performed ethically, with strict confidentiality and anonymity maintained to protect participant privacy.

DIAGNOSTIC CRITERIA FOR POSTOPERATIVE INFECTIONS:

Microbiologically Confirmed Infection: A postoperative infection is diagnosed when a patient presents with fever (elevated body temperature above the normal postoperative range) accompanied by at least one of the following criteria [18]: 1) Positive microbial culture from one or more of the following sites [19]: cervical secretions, urine, blood, surgical incision exudate, or drainage fluid. 2) Radiological evidence of pneumonia, as demonstrated by a chest CT scan showing pulmonary infiltrates consistent with infectious pathology [20].

Clinically Suspected Infection Without Culture Confirmation: If no specific infection source can be identified and all culture results remain negative, a presumptive diagnosis of postoperative infection may be made based on persistent pyrexia. The diagnostic criteria are as follows: 1) Temperature monitoring: From 24 hours to 10 days postpartum, the patient’s temperature should be recorded 4 times daily at 4-hour intervals [21]. 2) Fever threshold: A diagnosis is considered if at least 2 recorded temperatures reach or exceed 38°C within this period, even in the absence of a clearly defined infectious focus or positive culture results [22].

DATA COLLECTION AND OUTCOME MEASURES:

Relevant clinical and demographic data were extracted from patients’ electronic medical records and supplemented by postoperative telephone follow-up interviews. The collected information encompassed 4 primary domains:

Maternal Demographic and Obstetric History: Key parameters included maternal age, body mass index (BMI), marital status, employment status (presence or absence of stable occupation), residential region, and prior cesarean deliveries.

Gestational and Pregnancy-Related Conditions: Factors assessed included preoperative vaginal infections, gestational diabetes, hypertensive disorders of pregnancy, and Group B Streptococcus (GBS) colonization.

Intrapartum and Perioperative Characteristics: Data concerning the mode and duration of labor and delivery were recorded, such as oxytocin induction, duration of membrane rupture, whether the cervix reached full dilation, and the use of a cervical balloon. Additionally, the number of vaginal examinations performed, operative duration, estimated intraoperative blood loss, and perioperative blood transfusion were documented.

Preoperative Laboratory Indices: Hematological parameters included leukocyte count, neutrophil-to-lymphocyte ratio (NLR), and hemoglobin levels.

STATISTICAL ANALYSIS:

Statistical analyses were conducted using SPSS version 27.0 and R version 4.3.1. Continuous variables with normal distributions are presented as mean±standard deviation (χ̄x±s) and were compared using the independent t-test. For non-normally distributed continuous variables, data are expressed as median (interquartile range) [M (QL, QU)] and compared using the Mann-Whitney U test. Categorical variables are reported as frequencies (percentages) and were analyzed by the χ² test. Frequencies and percentages were used to describe the distribution of postoperative infection types. Prior to modeling, multicollinearity analyses were performed to identify and exclude predictor variables demonstrating direct interdependence. Potential risk factors for postoperative infection following cesarean section were then identified using multivariable logistic regression. A nomogram model was constructed based on the significant predictors derived from this regression. Model discrimination was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC), and further evaluated using the concordance index (C-index), which quantifies the predictive ability of the model. Model calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit test and calibration plots, as well as the mean absolute error (MAE) between predicted and observed risks to quantify calibration accuracy. Clinical utility was examined via decision curve analysis (DCA), where the net benefit was calculated across different threshold probabilities to assess the clinical applicability of the model. Internal validation of the nomogram was performed through bootstrap resampling methods. A two-sided P-value of less than 0.05 was considered statistically significant.

Results

POSTOPERATIVE INFECTION DISTRIBUTION:

Among the 33 patients diagnosed with postoperative infections following cesarean delivery, the most frequently identified condition was endometritis (17 cases, 51.52%). This was followed by sepsis in 5 patients (15.15%), urinary tract infection in 3 patients (9.09%), and surgical site infection in 2 patients (6.06%). One patient (3.03%) was found to have pneumonia based on clinical and radiological evaluations. An additional 5 patients (15.15%) presented with persistent fever exceeding the defined postoperative threshold but did not meet the diagnostic criteria for a specific infection source despite comprehensive laboratory, imaging, and clinical assessments.

CLINICAL CHARACTERISTICS OF PREGNANT WOMEN WITH AND WITHOUT INFECTIONS:

A comparative analysis of clinical characteristics between the infection group (n=33) and the non-infection group (n=652) revealed several noteworthy findings. The mean age and BMI of participants were comparable across both groups, with no statistically significant differences observed (30.3±4.7 years vs 30.9±4.4 years, P=0.447; 29.4±3.8 kg/m2 vs 28.8±3.9 kg/m2, P=0.388). Employment status also did not significantly differ between the groups (χ2=2.977, P=0.085), nor did the history of cesarean deliveries (P=0.765) or the presence of preoperative vaginitis (P=0.681). However, the prevalence of diabetes mellitus exhibited a significant variation between the 2 cohorts. Specifically, a higher proportion of individuals in the infection group had diabetes compared to the non-infection group (39.4% vs 20.0%, P<0.001). Additionally, Group B Streptococcus (GBS) colonization was significantly more prevalent in the infection group than in the non-infection group (9.1% vs 2.4%, P=0.024). Gestational hypertension did not show a significant association with infection status (P=0.455) (Table 1).

ASSOCIATION BETWEEN OBSTETRIC INTERVENTIONS AND INFECTION OUTCOMES:

The comparison of obstetric interventions and surgical outcomes between the infection group (n=33) and the non-infection group (n=652) revealed significant differences in several key factors. The infection group experienced a higher rate of membrane rupture (57.6% vs 23.8%, P<0.001) and complete cervical dilation (6.1% vs 0.9%, P=0.007). Additionally, women in the infection group underwent more frequent vaginal examinations (>5 times) compared to those in the non-infection group (30.3% vs 10.0%, P<0.001). These findings suggest that membrane rupture, cervical dilation, and increased vaginal examinations are associated with a higher risk of postoperative infections. Conversely, there were no significant differences between the groups regarding oxytocin induction (P=0.626), cervical balloon placement (P=0.915), operation time (≥60 minutes, P=0.536), intraoperative blood loss (≥300 mL, P=0.453), or perioperative blood transfusion (P=0.362) (Table 2).

LOGISTIC REGRESSION ANALYSIS OF RISK FACTORS FOR POSTOPERATIVE CESAREAN INFECTIONS:

Logistic regression analysis identified several significant risk factors associated with postoperative infections in pregnant women undergoing cesarean sections. Women who underwent more than 5 vaginal examinations had a higher likelihood of infection (OR=1.57, 95% CI: 1.23–2.98, P=0.043). Membrane rupture was strongly associated with an increased risk of infection (OR=2.36, 95% CI: 1.32–4.20, P=0.004). Additionally, complete cervical dilation significantly elevated the risk (OR=2.46, 95% CI: 1.23–4.90, P=0.011). Comorbid diabetes mellitus was another important factor, with affected individuals showing a higher probability of developing infections (OR=2.47, 95% CI: 1.14–5.28, P=0.021). Furthermore, Group B Streptococcus (GBS) colonization was associated with a markedly increased risk of postoperative infections (OR=3.15, 95% CI: 1.12–8.85, P=0.036) (Table 3). Prior to logistic regression analysis, we conducted multicollinearity testing using the variance inflation factor (VIF) to ensure that predictor variables were not highly correlated. No variable exhibited a VIF exceeding the threshold of 10, indicating an absence of severe multicollinearity. Furthermore, potential confounding factors were adjusted in the multivariate regression model, including maternal age, BMI, and history of cesarean delivery.

DEVELOPMENT OF A NOMOGRAM FOR PREDICTING POSTOPERATIVE INFECTIONS FOLLOWING CESAREAN SECTION:

A nomogram risk prediction model for postoperative infections following cesarean section was developed using 5 significant risk factors identified through multivariate logistic regression analysis. Each of these factors was assigned a specific score based on its relative contribution to the risk of infection. By summing the scores of the individual variables, a total score was calculated for each patient. This total score corresponds to a probability value on the nomogram’s scale, representing the likelihood of developing a postoperative infection after cesarean delivery. Higher total scores indicate an increased risk of infection, thereby allowing clinicians to stratify patients based on their individual risk profiles effectively (Figure 1).

PERFORMANCE EVALUATION OF THE NOMOGRAM MODEL FOR PREDICTING POSTOPERATIVE INFECTIONS FOLLOWING CESAREAN SECTION:

The predictive accuracy of the nomogram model for postoperative infections following cesarean section was evaluated using the AUC. The model demonstrated a satisfactory performance with an AUC of 0.786 (95% CI: 0.681–0.856). At the optimal Youden index, the sensitivity of the model was 79.7%, and the specificity was 76.8%, indicating a balanced ability to correctly identify both infected and non-infected patients (Figure 2). Bootstrap resampling was utilized to internally validate the model and mitigate the potential for overfitting. By repeatedly drawing random samples (with replacement) from the dataset over 1,000 iterations, this method provides a more robust estimation of the model’s predictive performance. The corrected C-index obtained from bootstrap validation (0.716) reflects the model’s stability and reliability in differentiating between infected and non-infected patients. Additionally, the calibration curve showed that the mean absolute error between predicted and actual infection risks was 0.008, suggesting excellent calibration and high consistency between the predicted and observed outcomes.

CALIBRATION OF THE NOMOGRAM MODEL FOR POSTOPERATIVE INFECTION PREDICTION:

The calibration of the nomogram model for predicting postoperative infections following cesarean section was assessed using the Hosmer-Lemeshow goodness-of-fit test. The result showed a χ2 value of 2.915 with a P-value of 0.921, indicating excellent model calibration (Figure 3).

CLINICAL EFFECTIVENESS OF THE NOMOGRAM MODEL: DECISION CURVE ANALYSIS:

The clinical utility of the nomogram model for predicting postoperative infections was assessed through DCA. In the DCA curve, the horizontal line represents the scenario where no interventions are made, assuming no patients experience postoperative infections, resulting in a net benefit of zero. The diagonal line, on the other hand, represents the case where all patients develop postoperative infections and are treated, with a negative slope reflecting diminishing net benefit as interventions increase. The net benefit curve of the nomogram model clearly outperforms both of these extreme scenarios (Figure 4).

POST HOC POWER ANALYSIS:

To assess the adequacy of our sample size for detecting statistically significant differences in key risk factors, we performed a post hoc power analysis for the 5 identified risk factors: number of vaginal examinations, membrane rupture, complete cervical dilation, comorbid diabetes mellitus, and GBS colonization. A weighted average power calculation was conducted using equal weights for each variable. The analysis yielded a weighted average power of approximately 0.942. This result indicates robust statistical power, with a high probability of detecting significant effects for each factor. Based on the observed effect sizes and sample sizes, our study is adequately powered to validate the identified risk factors for postoperative infections following cesarean sections.

Discussion

Our study builds upon existing literature by identifying key risk factors for postoperative infections following cesarean sections and developing a predictive nomogram for individualized risk assessment. Islam et al [23] conducted a meta-analysis on global surgical site infection (SSI) incidence, highlighting diabetes and procedural factors as major contributors. While our findings align, we enhance clinical applicability by offering a validated, institution-specific risk stratification tool. Guo et al [24] analyzed SSI incidence in China, emphasizing obesity, diabetes, and membrane rupture. Our study corroborates these findings but further identifies excessive vaginal examinations and complete cervical dilation as critical risk factors, integrating them into a predictive model for real-time clinical decision-making. Erritty et al [25] found high BMI, smoking, and emergency cesarean sections to be major risk factors, whereas diabetes and prior C-sections were not significant. In contrast, our study establishes diabetes mellitus and GBS colonization as key contributors, reflecting potential population differences. The novelty of our study lies in the comprehensive integration of multiple risk factors into a predictive nomogram for postoperative infections following cesarean sections. Our model concurrently evaluates excessive vaginal examinations, membrane rupture, complete cervical dilation, diabetes mellitus, and GBS colonization. This multifactorial approach not only enhances risk stratification but also provides a practical, quantifiable tool for individualized clinical decision-making. The incorporation of decision curve analysis further demonstrates the clinical utility of our nomogram by quantifying net benefits over alternative intervention strategies. This innovative tool can be readily integrated into electronic health record systems, facilitating real-time risk assessment and guiding targeted preventive measures, thereby optimizing patient outcomes and resource allocation in the clinical setting.

A comparative analysis between the infection (n=33) and non-infection (n=652) groups revealed several significant clinical characteristics. Notably, a higher prevalence of diabetes mellitus (39.4% vs 20.0%, P<0.001) and GBS colonization (9.1% vs 2.4%, P=0.024) was observed in the infection group. Diabetes mellitus is a well-established risk factor for postoperative infections due to its impact on immune function, impaired wound healing, and increased susceptibility to bacterial colonization. Hyperglycemia can impair neutrophil function, reduce complement activity, and disrupt the integrity of the skin and mucosal barriers, thereby facilitating bacterial invasion and infection [26,27]. GBS colonization significantly increased the risk of postoperative infections, with an OR of 3.15 (95% CI: 1.12–8.85, P=0.036). GBS is a recognized pathogen in postpartum infections, capable of causing endometritis, sepsis, and UTIs. The higher prevalence of GBS colonization in the infection group underscores the importance of routine screening and appropriate antibiotic prophylaxis in GBS-positive patients to mitigate infection risks. Effective management of GBS colonization could potentially reduce the incidence of severe postoperative infections [28]. Obstetric interventions also played a critical role in infection outcomes. The infection group exhibited higher rates of membrane rupture (57.6% vs 23.8%, P < 0.001) and complete cervical dilation (6.1% vs 0.9%, P=0.007). Prolonged membrane rupture increases the exposure time for ascending pathogens from the vaginal flora to the sterile uterine environment, thereby heightening the risk of endometritis and other infections. Complete cervical dilation may necessitate more extensive surgical manipulation, increasing the potential for bacterial contamination and subsequent infections [29,30].

Additionally, women in the infection group underwent more frequent vaginal examinations (>5 times) compared to those in the non-infection group (30.3% vs 10.0%, P<0.001). Repeated vaginal examinations can disrupt the natural barriers of the cervix and vagina, facilitating the entry of microorganisms into the uterine cavity and other sterile sites. This finding suggests that minimizing unnecessary vaginal examinations during labor and delivery could be a crucial strategy in reducing postoperative infection risks. Interestingly, factors such as oxytocin induction, cervical balloon placement, operation time, intraoperative blood loss, and perioperative blood transfusion did not differ significantly between the groups. This suggests that these variables may not be primary drivers of postoperative infections within this cohort, possibly due to standardized surgical protocols and effective perioperative care practices that mitigate their potential impact. The logistic regression analysis reinforced the significance of the identified risk factors [31]. More than 5 vaginal examinations (OR=1.57, 95% CI: 1.23–2.98, P=0.043), membrane rupture (OR=2.36, 95% CI: 1.32–4.20, P=0.004), complete cervical dilation (OR=2.46, 95% CI: 1.23–4.90, P=0.011), comorbid diabetes mellitus (OR=2.47, 95% CI: 1.14–5.28, P=0.021), and GBS colonization (OR=3.15, 95% CI: 1.12–8.85, P=0.036) were independently associated with an increased risk of postoperative infections. These findings highlight the multifactorial nature of infection risks, encompassing both clinical and procedural elements.

The developed nomogram, incorporating these 5 risk factors, demonstrated robust predictive performance with an AUC of 0.786 (95% CI: 0.681–0.856). This indicates good discriminative ability to differentiate between patients at high and low risk for postoperative infections. The internal validation using bootstrap resampling confirmed the model’s reliability, with a corrected C-index of 0.716 and excellent calibration, as evidenced by a mean absolute error of 0.008 and a Hosmer-Lemeshow test result of χ2=2.915, P=0.921. These metrics suggest that the nomogram accurately predicts infection risk and aligns closely with observed outcomes. Decision Curve Analysis (DCA) further demonstrated the clinical utility of the nomogram model. The findings of this study have direct clinical implications, providing a practical risk stratification tool for postoperative infection prevention in cesarean section patients. The developed nomogram enables clinicians to categorize patients into different risk tiers, facilitating personalized interventions. High-risk patients may benefit from enhanced antibiotic prophylaxis, intensified postoperative monitoring, and early infection detection strategies, while low-risk patients may require less intensive surveillance, optimizing resource allocation. Key risk factors – excessive vaginal examinations, membrane rupture, complete cervical dilation, diabetes mellitus, and GBS colonization – inform targeted clinical interventions. Reducing unnecessary vaginal examinations, implementing standardized GBS screening with prophylactic antibiotics, and ensuring optimal glycemic control in diabetic patients are critical strategies. The nomogram’s integration into electronic health records (EHRs) allows for real-time risk assessment, guiding clinical decisions on infection control protocols and individualized postoperative care, ultimately improving patient outcomes and reducing healthcare burden.

However, this study has several limitations. As a retrospective single-center study, the findings may not be generalizable to other settings with different patient populations and clinical practices. Although the post hoc power analysis indicated that the sample size was sufficient for the 5 identified risk factors, the relatively small number of infection cases may limit the statistical power and the ability to detect additional potential risk factors. Additionally, the study did not account for all possible confounding variables, such as the use of prophylactic antibiotics or variations in surgical techniques, which could influence infection rates. Future research should focus on external validation of the nomogram in diverse populations and prospective studies to confirm the identified risk factors and further refine the predictive model. Additionally, exploring the role of other potential risk factors, such as antibiotic resistance patterns and specific surgical techniques, could enhance the model’s accuracy and applicability. Despite these limitations, this study successfully identifies key risk factors for postoperative infections in cesarean section patients and presents a validated nomogram with strong predictive performance and clinical utility. By integrating these risk factors into a user-friendly tool, the nomogram facilitates personalized risk assessment and targeted interventions, ultimately contributing to improved patient care and reduced infection-related morbidity.

Conclusions

This study identified key risk factors for postoperative infections following cesarean section, including excessive vaginal examinations, membrane rupture, complete cervical dilation, diabetes mellitus, and GBS colonization. The developed nomogram showed strong predictive accuracy and clinical utility, offering a valuable tool for clinicians to stratify infection risk and implement targeted preventive measures.