Background

Surgical site infections (SSIs) are postoperative infections encompassing the superficial, deep, and interstitial layers [1,2,3]. SSI is a common nosocomial infection, leading to extended patient hospitalization and imposing substantial burdens on patients [1, 4, 5]. According to a U.S. Centers for Disease Control and Prevention health care-associated infection (HAI) prevalence survey, nearly 600,000 cases of SSI occurred in the USA in 2011, making it the most common HAI [6]. It is estimated that approximately 5% of patients develop SSI during the perioperative period, which prolongs the average length of stay by more than 9 days and increases the risk of death by 11 times [1].

Notably, orthopedic patients have heightened susceptibility to SSI relative to other patients owing to the enduring presence of internal fixation and implant apparatus within the body [7, 8]. These components create conducive niches and substrates for pathogenic proliferation, consequently significantly elevating the risk of postoperative wound infections [9, 10]. When SSI occurs during joint implant surgery, the cost per treatment may exceed $90,000 [2, 11, 12]. However, approximately 55% of SSIs are preventable through proper implementation of evidence-based strategies, so timely preoperative detection of high-risk SSI patients is critical [13].

The National Nosocomial Infections Surveillance (NNIS) risk index [14, 15] is the prevailing clinical prognostic instrument for predicting overall SSI risk. The NNIS system employs three autonomous and equitably significant variables—the American Society of Anesthesiology (ASA) classification [16], surgical incision type, and operative duration—to predict SSI risk. However, the prognostic efficacy of the NNIS system remains uncertain with respect to the prediction of SSI risk in patients undergoing elective aseptic orthopedic procedures [17, 18]. Consequently, the formulation of a composite predictive model based on multiple preoperative clinical parameters is imperative to aid orthopedic practitioners in identifying candidates at high risk of SSI.

A nomogram is a straightforward instrument for clinical prognostication and is used to predict clinical outcomes [19]. Nomograms have extensive applications across domains, such as oncology [20], cardiovascular ailments [3A).

Fig. 2
figure 2

ROC curve analysis was used to compare the performance of the nomogram and the NNIS system for predicting surgical site infection in A the training cohort and B the validation cohort. ROC receiver operating characteristic, NNIS national nosocomial infections surveillance

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Fig. 3
figure 3

Calibration curves of the nomogram for predicting the risk of surgical site infection in A the training cohort and B the validation cohort

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Predictive value of the nomogram model for SSI in the validation cohort

The nomogram also showed higher performance in predicting SSI in the validation cohort, with a C-index of 0.732 (95% CI 0.603–0.861), compared with the C-index of 0.543 (95% CI 0.410–0.677) for the NNIS system (Fig. 2B). In addition, the calibration curve of the SSI forecast showed that the nomogram agreed well with the observed development of SSI. This demonstrates that our nomogram-based prediction model had good predictive performance for the occurrence of SSI in the validation cohort (Fig. 3B).

Comparison of the nomogram and NNIS system

In the training cohort, the nomogram showed favorable predictive performance for SSI detection, with an NPV, PPV, specificity, sensitivity, accuracy, precision, and recall of 0.946, 0.444, 0.906, 0.593, 0.871, 0.444, and 0.593, respectively (Table 3). The nomogram had a higher predictive performance for SSI than the NNIS system (Table 3). The nomogram also showed good performance for SSI in the validation cohort (Table 4). The NPV, PPV, specificity, sensitivity, accuracy, precision, and recall for the nomogram and NNIS system in the validation cohort are summarized in Table 4.

Table 3 Performance of the nomogram model and NNIS system for predicting surgical site infection in the training cohort
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Table 4 Performance of the nomogram model and NNIS system for predicting surgical site infection in the validation cohort
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DCA showed that the nomogram for predicting SSI was more valuable than the NNIS system in the training cohort (Fig. 4A) and in the validation cohort (Fig. 4B). Therefore, our nomogram outperforms the existing models.

Fig. 4
figure 4

Decision curve analysis for the nomogram and the NNIS system for surgical site infection in A the training cohort and B the validation cohort

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Discussion

Despite advances in the management of perioperative nosocomial infections in recent years, SSIs remain a common cause of increased mortality, length of stay, and cost in surgical patients [1, 2]. Our investigation devised a model aimed at predicting the incidence of SSI in individuals undergoing clean orthopedic surgery, thereby proficiently evaluating the risk of SSI among elective aseptic orthopedic patients. Using univariate and multivariate logistic regression analyses, we established that operation time, ASA class, and D-dimer level were independently correlated with a heightened risk of postoperative SSI. Subsequently, the logistic regression model was translated into a visual representation a nomogram. Our nomogram model not only exhibited robust predictive capability and impeccable calibration but also had substantial clinical utility in facilitating informed decision-making for patients within both the training and validation cohorts. Additionally, we extended our efforts to develop an easy-to-use and free-to-access online calculator based on the nomogram model (https://jitao.shinyapps.io/dynnomapp/), an accessible tool designed to enable clinicians and researchers to readily ascertain the probability of postoperative SSI in the special patient populations.

The American College of Surgeons incision grading system stratifies incisions into four distinct grades: grade I, grade II, grade III, and grade IV. Grade I incisions, characterized as clean surgeries, exhibit a propensity for swift and comprehensive healing within a condensed timeframe. Directives formulated by the US Centers for Disease Control and Prevention state that clean surgeries, including of drainage procedures, require no supplementary antibiotic prophylaxis after closure of the surgical incision [2]. Although, compared with other types of surgery, the risk of SSI in patients undergoing clean orthopedic surgery is relatively low, once SSI occurs, it may lead to serious clinical outcome [29, 30].

The NNIS grading system is currently the most widely used clinical tool for predicting the occurrence of SSI and includes three independent and equally important variables: ASA class, surgical incision type, and surgical duration. Through the qualitative classification of these variables, the NNIS system divides the surgical risk into four levels, namely, NNIS level 0, NNIS level 1, NNIS level 2, and NNIS level 3 [14, 15]. However, because all surgical incision types in clean surgery are the same, the NNIS system lacks specificity for clean surgery. Compared to the NNIS system, our nomogram model integrates qualitative and quantitative clinical variables. By assigning values to each clinical variable and intuitively obtaining the occurrence probability of SSI with a 95% CI, the nomogram is more convenient for orthopedic surgeons. More importantly, our predictive model had a higher predictive ability and is more suitable than traditional NNIS system for patients undergoing clean orthopedic surgery.

Similar to the NNIS system, our nomogram included the ASA class and operation time, as they were independent risk variables for SSI. ASA classification is a clinical tool used to assess the risk of develo** SSI and severity of potential disease in patients undergoing preoperative anesthesia. Many studies have confirmed that ASA classification can be used for SSI risk stratification [31,32,33,34]. A study of 310 patients who underwent general surgery and were classified as clean or clean-contaminated confirmed that the rate of SSI was significantly higher in patients with ASA class II-III than in patients with ASA class I (P = 0.003). An ASA class > 2 is independently associated with SSI [33]. The duration of surgery is another widely recognized clinical index closely related to the occurrence of SSI. In a study of 825 patients undergoing spinal surgery, operative time (P = 0.0019) and ASA class III (P = 0.0132) were independent risk factors for SSI [32]. Higher ASA classes are associated with more comorbidities and poorer immunity, whereas longer operation time usually indicates higher surgical difficulty and longer incision exposure time, all of which increase the risk of pathogen invasion [32, 35, 36]. Therefore, shortening the operation time, especially in patients with higher ASA classes, can effectively prevent SSI.

Our predictive model also incorporates another laboratory measure, the D-dimer level, which is not included in the NNIS system. Owing to the close relationship between the coagulation system, inflammation, and endothelial injury, an increase in D-dimer levels is also often observed in some non-thrombotic diseases, such as infection, surgery, trauma, heart failure, and malignant tumors [37,38,39]. A multicenter study of patients undergoing revision total joint arthroplasty examined elevated serum C-reactive protein (CRP > 1 mg/dL), D-dimer (> 860 ng/mL), and erythrocyte sedimentation rate (> 30 mm/h), which were assigned 2, 2, and 1 points, respectively, and jointly constructed a new standard for the diagnosis of periprosthetic infection (PJI) with other laboratory indicators; its sensitivity and specificity were significantly higher than those of the Musculoskeletal Infection Association and International Consensus Conference Definition [40]. Another study demonstrated that a serum D-dimer threshold of 0.75 mg/L predicted shoulder PJI with a sensitivity of 86%, specificity of 56%, and area under the curve of 0.74. When serum D-dimer and CRP above thresholds of 0.75 mg/L and 10 mg/L, respectively, were used to predict PJI, the sensitivity and specificity were 57% and 100%, respectively [41]. Therefore, it is necessary to maintain D-dimer levels in patients at normal or even slightly decreased levels to reduce the incidence of SSI [41,42,43,44].

This study had some limitations. First, owing to the retrospective nature of the study, it only included a small number of patients who did not develop SSI, and selection bias was inevitable. Second, some inflammatory indicators that may be related to SSI, such as C-reactive protein and procalcitonin, were missing from our study; the inclusion of these indicators may help improve the predictive accuracy of the model. Third, this was a single-center study. To verify the prediction model, we randomly divided the total cohort into training and internal validation cohorts; however, we still lacked an external validation cohort. In the future, another prospective multicenter study with a larger sample size is needed to further confirm the predictive performance of this model. Finally, models based on more advanced machine learning algorithms or radiomics may be more helpful in providing predictive model accuracy [45,46,47]. Further development of SSI models based on other artificial intelligence is still needed to further improve prediction capabilities.

Conclusions

In conclusion, operation time, ASA class, and D-dimer level are important clinical indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram prediction model based on these clinical characteristics showed strong SSI prediction performance, calibration, and clinical decision-making utility. In addition, we created an online calculator using the nomogram so that orthopedic surgeons and researchers can easily and quickly predict the risk of postoperative SSI and identify patients at high risk as early as possible to reduce the risk of infection.