Fracture-related infection (FRI) represents a formidable challenge in modern trauma surgery (1), with significant global implications. The 2019 Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated an annual incidence of 178 million new bone fractures, totaling 2,296 cases per 100,000 population (2). The prevalence of FRI is expected to rise in the coming decade due to factors such as increased life expectancy, a growing incidence of osteoporotic and age-related fragility fractures, and a rise in surgical fracture repair procedures globally (3). FRIs invariably necessitate long-term follow-up and high pain tolerance, multiple surgical interventions, extended antimicrobial therapy, the achievement of bony union, and the restoration of diminished bone or joint functionality and can even escalate to the point of necessitating amputation or posing life-threatening risks (4). Hence, early identification and diagnosis are crucial for timely decision-making regarding surgical intervention or alternative therapeutic strategies. Notably, unlike prosthetic joint infections (PJI), which have more established diagnosis and treatment guidelines, FRIs present unique challenges due to associated bone and soft tissue damage and the option of implant removal after fracture healing.

In clinical practice, orthopedic surgeons typically classify FRIs into two primary groups: the first includes patients with clear clinical indicators of infection, such as fistula, sinus, wound breakdown, or purulent drainage from the wound. The second group, lacking clear clinical confirmatory criteria (5), poses a diagnostic challenge, where laboratory findings serve as suggestive yet relatively ambiguous criteria (6).

In this context, the use of imaging tools becomes invaluable for surgeons, aiding in the selection of the optimal therapeutic approach. These tools not only help identify signs of infection but also play a crucial role in assessing implant stability and bone regeneration. However, each imaging technique has its limitations. For instance, plain radiography falls short in evaluating soft tissue and exhibits limited sensitivity and specificity for FRI diagnosis (7). Ultrasound faces constraints in bone assessment due to metallic implants. Computed tomography (CT) struggles with soft tissue contrast and the identification of early signs of infection like bone marrow edema (8). Magnetic resonance imaging (MRI) is relatively ineffective in post-traumatic conditions and fails to distinguish reactive bone marrow edema (9). Besides, both CT and MRI are impeded by metallic implants.

In recent years, several nuclear imaging techniques have been introduced to enhance infection diagnosis. Systematic reviews have indicated high diagnostic accuracy for nuclear imaging (10), but these techniques generally have low anatomical resolution. Three-phase bone scintigraphy, despite widespread use, has limited specificity and diagnostic value in violated bone (<1–2 years after trauma/fracture/osteosynthesis, <5 years after arthroplasty), making it unsuitable as a primary diagnostic tool for FRI (7, 11, 12). Gallium scans are time-consuming, requiring approximately 48–72 h. White blood cell scintigraphy (WBCS) is also time-consuming, and there is no standardized protocol (9).

In contrast, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT), though expensive, offers a relatively rapid turnaround time (60 min post-tracer injection) and is gaining popularity in infection/inflammatory diseases. The imaging interpretation is presented in two forms: visual image analysis, providing ¹⁸F-FDG uptake patterns and anatomical position, and semiquantitative analysis using standardized uptake value (SUV), total lesion glycolysis (TLG), etc. Multiple studies have validated its superior sensitivity compared to other nuclear medicine techniques in chronic osteomyelitis of the extremities (7, 8, 11). Furthermore, systematic reviews have confirmed its high diagnostic accuracy in detecting spinal infections (13). Nonetheless, both ¹⁸F-FDG PET/CT and laboratory findings exhibit a limited ability to isolate FRI, and research or guidelines about FRI diagnosis and treatment remain relatively scarce (14). Nevertheless, the potential of ¹⁸F-FDG PET/CT in detecting infections appears promising.

Neither laboratory findings nor imaging alone can diagnose FRI. It is widely thought that the diagnostic value could be enhanced by combining the two in diagnosing FRI. Hyunkwang et al. (15) combined SUV_max (maximum standardized uptake value) and blood inflammatory markers, building a model to assess treatment response in pyogenic vertebral osteomyelitis. The results demonstrated that the combined performance of SUV_max and blood inflammatory markers was superior to the blood inflammatory markers alone.

Nomograms are frequently used tools for prognostic estimations in both oncology and general medicine. They can amalgamate determinant variables, making them increasingly prevalent in tumor prognosis (16, 17). However, their utility extends to diagnostics as well (18). Given their characteristics as an integrated model, they significantly contribute to the advancement of personalized medicine (19).

In our study, we aimed to develop a novel nomogram based on ¹⁸F-FDG PET/CT and laboratory findings to refine early FRI diagnosis ability. Considering that the diagnosis of FRI in patients with clear clinical confirmatory criteria is relatively straightforward and does not require comprehensive tests, the primary aim of our study was to provide insight into the diagnosis and management of a subpopulation of FRI patients presenting without clinical confirmatory criteria.

We conducted an extensive search for patients who underwent ¹⁸F-FDG PET/CT scans and other laboratory examinations due to post-fracture pain at Shanghai Sixth People’s Hospital between January 2021 and December 2022.

The inclusion criteria were as follows:

The exclusion criteria were as follows:

The participant recruitment process is illustrated in the flowchart in Figure 1.

In this retrospective study, we developed and evaluated a novel diagnostic nomogram based on SUV_max, SII, IL-6 and ESR to diagnose FRI in patients without clinical confirmatory criteria. The nomogram exhibited high performance and proved beneficial for diagnosing FRI.

Despite advancements in orthopedic surgical techniques, the persistent challenge of post-operative infections remains, exacerbated by the insidious nature of the disease. Additionally, the emergence of culture-negative FRIs, often linked to antibiotic overuse, poses diagnostic challenges. Indeed, swift identification and treatment are crucial to prevent adverse outcomes, necessitating effective and simplified diagnostics.

Originally employed for oncology, ¹⁸F-FDG PET/CT has gained increasing recognition in inflammatory diseases (12). As early as 1998, scholars explored its utility in diagnosing chronic osteomyelitis (24). However, it should be borne in mind that technological advancements over time limit direct comparisons with historical outcomes due to evolving techniques. Recent studies have increasingly validated the significant role of ¹⁸F-FDG PET/CT in diagnosing and differentiating osteomyelitis (25–27). Van Vliet et al. (26) conducted a retrospective assessment to evaluate the efficacy and optimal diagnostic accuracy of ¹⁸F-FDG PET/CT in distinguishing delayed unions in 30 patients. Their findings revealed a significant distinction in SUV_max values between aseptic delayed unions and septic delayed unions, yielding an AUC of 0.747. This suggests that SUV_max could be a useful differentiation test in bone infection or aseptic pain.

Martina Sollini et al. (28) retrospectively tested the efficacy and optimal diagnostic accuracy of ¹⁸F-FDG PET/CT in distinguishing infection in non-union in 47 patients. Their results substantiated that ¹⁸F-FDG PET/CT offers heightened utility when clinical presentations are atypical in suspected FRIs. Notably, the study observed that ¹⁸F-FDG PET/CT exhibited good performance in ruling out infection among patients with normal CRP levels, contrasting with its efficacy in confirming infection in those with elevated CRP. The authors also found that a combination of visual approach and semi-quantitative analysis achieved a balanced compromise between sensitivity and specificity (80 and 77%), aligning with findings from other notable studies (27). As previously reported (26), a balanced sensitivity and specificity can help avoid unnecessary surgeries and reduce the risk of under treatment in septic delayed unions. Using SUV_max for interpreting ¹⁸F-FDG PET/CT imaging seems promising for FRI, especially in patients without obvious clinical presentations, compensating for the limitations of blood tests.

A complete blood count, coagulation function tests, and routine inflammation indicators (e.g., ESR, CRP) are commonly ordered when FRI is suspected, with blood routine and coagulation indicators being essential prerequisites for pre-operative assessment.

Inflammatory markers (e.g., ESR, WBC, CRP) are recognized as pivotal in clinical guidelines, serving as important suggestive criteria (6). CBC ratio parameters studied more in recent years in infectious diseases (29), were included in our study. Strony et al. (30) used the platelet count to mean platelet volume ratio and reported an AUC of 0.814 for the diagnosis of FRI, outperforming other inflammation indicators. With the in-depth development of research, scholars have found that new indicators composed of multiple types of cells, such as SII and SIRI, can further integrate the combined effects of various inflammatory cells to evaluate the inflammatory level of diseases. Telang et al. (31) research and other studies have shown that patients with elevated SII are prone to developing PJI, and it is an independent risk factor for postoperative infection. These indicators are capable of providing a more comprehensive assessment of the relationship between diseases, immune cells, and the inflammatory response. Therefore, our study also sought to assess the value of the derived indicators based on the existing results.

In this study, we combined ¹⁸F-FDG PET/CT imaging with laboratory tests to construct a multivariable logistic regression model, including ¹⁸F-FDG PET/CT semi-quantitative analysis and ESR, SII, IL-6. The model exhibited good discrimination in the training cohort (AUC = 0.823) and the validation cohort (AUC = 0.811). For the training set, the model demonstrated a sensitivity of 76.4%, specificity of 77.0%, and accuracy of 76.7%. There are relatively few diagnostic models for FRI. To assess the clinical utility of the model, we extended our evaluation beyond traditional performance measures like AUC. DCA was employed to estimate the net benefit of the model across a range of risk thresholds, allowing us to evaluate the potential impact of different risk thresholds (32, 33). The research results of Xu et al. (34) on the diagnosis of lower limb FRI using inflammatory indicators showed that the area under the curve (AUC) of the combination of C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR) could reach 0.873, which is higher than that of the model in this study. However, their research lacks data from a validation group. Therefore, further research is still needed.

Several limitations need to be acknowledged in the current study. Firstly, a notable limitation of our study is the relatively low sensitivity (62.6% in the validation cohort) of the FRID - PI model, risking under diagnosis. FRI’s complexity is a key factor. Our sample size may be too small. A larger one could improve the model’s ability to identify FRI patterns and increase sensitivity. Despite this, the model shows good overall discriminative power. Future research should aim to enhance sensitivity by exploring specific biomarkers or features, using advanced algorithms, and conducting large - scale multicenter studies. Secondly, the analysis was limited to a semi-quantitative assessment of PET/CT data. While visual analysis could potentially offer important diagnostic insights, it is more susceptible to subjective influence. Thirdly, the lack of standardized scan reconstruction resulted in semi-quantitative analysis results specific to the employed camera system, making it challenging to extrapolate findings to other hospitals and camera systems. Given the influence of various factors on SUV_max (35), the adoption of standardized criteria such as European Association Research Ltd. (EARL) (36) and Quantitative Imaging Biomarker Alliance (QIBA) (37) for PET/CT scans is recommended. Unfortunately, these reconstruction protocols are not yet integrated into clinical routines in China. And ¹⁸F-FDG PET/CT is indeed more expensive, and its large-scale clinical application is limited. Fourth, because of lasso regression decreases variability in the parameter estimates and reduces overfitting, leading to fewer parameters in nomogram model. Therefore, expect more machine learning algorithms to be used for diagnosing FRI in the future. Fourth, our study was a single-center, retrospective analysis, underscoring the necessity for multicenter clinical and prospective trials to validate the external applicability of our model. Finally, our study did not follow up with patients, and more attention should be paid in future studies.

In conclusion, this study makes the first attempt to integrate ¹⁸F-FDG PET/CT semi-quantitative analysis with laboratory examinations for FRI diagnosis. We developed and validated the FRID-PI, incorporating ESR, SII, IL-6 and SUV_max into the diagnostic model. The nomogram, serving as a non-invasive predictive tool, enhances diagnostic accuracy, specificity, and sensitivity of FRI staging when compared to SUV_max and ESR, SII, IL-6 alone. This tool holds promise for assisting clinicians in identifying and diagnosing FRI in patients lacking clinical confirmatory criteria, thereby aiding in timely and appropriate treatment decisions.