ACS, which commonly occurs after fracture, can cause tissue ischemia owing to increased intracompartment pressure [13,14,15]. It often results in catastrophic complications such as infection, amputation, or muscle necrosis. In recent decades, increasing evidence has been reported regarding the incidence and predictors of ACS. Our meta-analysis found that being younger or male and injury mechanisms were related to ACS [1]. After reviewing the relevant articles, prior research focused on the characteristics of patients with ACS, but neglected the role of admission blood indicators. Therefore, we investigated their role in the development of ACS by using PSM to minimize selection bias and other confounding effects.

In our study, numerous laboratory indicators were found to be relevant to ACS on univariate analysis; however, MON, SII, and CKMB levels were risk factors for ACS on logistic regression analysis. ROC curve analysis indicated that 0.79 × 109/L, 1082.55, and 20.99 U/L were the cut-off values for predicting ACS. Then, we constructed a nomogram prediction model with an AUC of 0.869, with good consistency in correction curve and good clinical practicality by decision curve analysis.

It is well known that the hypoxic and ischemic microenvironment caused by continuously increasing intracompartmental pressure can lead to aseptic inflammation [11, 16,17,18]. Therefore, the presupposition of an inflammatory mechanisms in the pathogenesis of ACS is highly reliable. Blood biomarker research is the most commonly used method owing to its routine collection and low cost, which can enable large-scale practical and clinical applications. A complete blood count test, including NEU, MON, LYM, and platelets, which are inexpensive, can be easily obtained and are sensitive to inflammation in the organism [19].

NEU is the most abundant type of WBC in the body and contributes greatly to the innate immune system [20]. LYM is an adaptive immune system that contributes to immunological responses, including antibody generation and cell-mediated immunity [21]. MON is the most critical cell type for releasing proinflammatory and prooxidant cytokines during the innate immune response against pathogens [22]. PLT have the potential to modulate endothelial cell permeability, as well as the recruitment of NEU and macrophages, and has inflammatory roles in various pathophysiological conditions [23]. There has been increasing interest in inflammatory ratios, including NLR, MLR, and PLR, which are derived from a complete blood count, being a low-cost and routine clinical examination [24]. Inflammatory indicators derived from NEU, MON, LYM, and PLT, such as NLR, MLR, PLR, SII, and SIRI, are new markers that reflect immune response and inflammation [25, 26].

The SII, which combines NEU, LYM, and PLT, is becoming a popular biomarker for systemic inflammation [27] because it can more precisely represent the immunological and inflammatory conditions of the body than any of the aforementioned markers alone. Previous research has shown that SII plays a key role in determining the prognosis of many physical disorders, including malignancies, cerebral infarction, cardiovascular disease, and acute pancreatitis. SIRI is a novel inflammation-based biomarker that combines NUE, LYM, and MON peripheral counts [26]. Previous research has shown that SIRI can represent inflammation and has prognostic value in various malignancies, including cholangiocarcinoma and esophageal and gastric cancers [28]. To the best of our knowledge, the relationship between these indices and ACS has not been studied.

Univariate analysis showed that MON, NEU, NLR, MLR, SII, SIRI, and WBC count were associated with ACS. Furthermore, logistic regression analysis indicated that MON and SII levels were independent risk factors for ACS, and ROC analysis showed that the area under the curve of MON and SII was 0.632 and 0.678, respectively, indicating that SII more accurately reflected the immunological and inflammatory conditions of the body than MON. The above-mentioned findings imply that patients with ACS are in an inflammatory state compared with patients without ACS following tibial fracture. This further verifies our previous conclusions on the changes in the proportions of MON and its derived cell type, macrophages, in ACS patients from a study focusing on the deep fascia with single-cell RNA-seq analysis [11]. We will pay great attention to the study of inflammatory biomarkers of ACS using multiomics. Additionally, we identified the cutoff values of MON and SII to predict ACS, which assists orthopedists in auxiliary diagnosis of ACS in clinical practice.

Owing to their excellent sensitivity and specificity, CK and CKMB have frequently been used in clinical practice to diagnose acute myocardial damage [29]. They have also been used to monitor other injuries such as skeletal muscle injury [30], pulmonary embolism [31], and brain injury [32]. However, numerous studies have examined the effects of CK and CKMB on the diagnosis of ACS. In this study, we discovered that CK, CKMB, and CKMB% played a critical role in ACS, according to univariate analysis, while CKMB was an important predictor of ACS on the basis of logistic regression analysis. Furthermore, we identified 20.99 U/L as a cutoff value for CKMB to indicate ACS with the highest accuracy as a single indicator.

We established a prediction model to evaluate ACS in patients with tibial fracture. The ROC curve suggested good discrimination ability, and the calibration curve showed good calibration. Decision curve analysis showed that our nomogram prediction model had good clinical benefits.

This study provides some innovative findings; however, a few limitations should be noted. First, the single-center nature of the research and the limited samples make it difficult to perform subgroup analysis, which inevitably affects the reliability of the findings. Therefore, a multicenter, randomized controlled study with a larger sample size is required. Second, the inherent limitation on data collection owing to its retrospective nature results in the omission of some important inflammatory markers, such as C-reactive protein.

In conclusion, we used PSM to reduce the effects of potential confounding variables and investigate the role of admission blood indicators. Our findings showed that MON, SII, and CKMB levels were risk factors for ACS. We also identified their cutoff values. We established a nomogram prediction model that can efficiently predict ACS in patients with tibial fracture.