Abstract
Objective
This study aims to assess the clinical utility of composite immune–inflammatory indices formed from differential peripheral blood cell counts (NLR, PLR, SIRI, SII, PIV) for the early identification and risk stratification of chronic osteomyelitis, and to investigate potential clinical applications.
Methods
A retrospective unmatched case-control study was conducted at Zhongnan Hospital of Wuhan University. A total of 212 patients with confirmed chronic osteomyelitis and 440 non-infected trauma controls during the same period were enrolled using convenience sampling. Routine complete blood count data and main clinical information at admission were collected. The five composite indices were calculated and compared between groups. Receiver operating characteristic (ROC) curves and area under the curve (AUC) with 95% confidence intervals (CI) were used to evaluate diagnostic performance, with pairwise AUC comparisons by DeLong’s test. Multivariate logistic regression was performed to adjust for potential confounders (age, sex, diabetes, drinking history) and assess independent diagnostic value.
Results
Although total white blood cell count did not differ significantly between groups, NLR, PLR, SIRI, SII, and PIV were all significantly elevated in the osteomyelitis group (all P < 0.001). SII and PIV showed higher AUC, sensitivity, and specificity (SII AUC = 0.800, 95% CI: 0.764–0.835; PIV AUC = 0.781, 95% CI: 0.744–0.818). After adjustment for confounders, SII (OR = 1.002, 95% CI: 1.001–1.002) and PIV (OR = 1.002, 95% CI: 1.002–1.003) remained independent predictors. DeLong’s test indicated that SII had significantly better diagnostic utility than NLR, PLR, and SIRI, but not significantly different from PIV.
Conclusion
Composite immune‑inflammatory indices based on peripheral blood counts are clinically accessible and cost‑effective. SII and PIV may serve as potential auxiliary tools for chronic osteomyelitis diagnosis, complementing the limitations of traditional WBC, pending further prospective validation.
Keywords: chronic osteomyelitis, immune-inflammatory indices, diagnostic utility, peripheral blood
Introduction
Chronic osteomyelitis is a destructive bone tissue disease driven by persistent or recurrent invasion of pathogenic microorganisms. Epidemiological data indicate an annual incidence of 21.8 per 10,000 people in the United States, with a higher prevalence in males than females.1,2 Due to its insidious onset, complex clinical course, and high recurrence rate, chronic osteomyelitis remains a challenging condition in clinical orthopedics. Clinical diagnosis typically relies on combined imaging and laboratory evaluation; however, a definitive diagnosis often requires invasive bone biopsy and microbial culture. Although histopathology with bone culture is considered the “gold standard”, this approach is invasive, time-consuming, and costly, limiting its suitability for early screening. Moreover, the relatively high culture-negative rate in bone specimens further diminishes the practical utility of this gold standard for early diagnosis.2 Consequently, there is an urgent need for noninvasive, convenient, and sensitive diagnostic methods to enable early identification and intervention, thereby improving treatment efficacy and patient prognosis.
As an economical, convenient, and rapid laboratory test, the complete blood count (CBC) can yield several composite indices reflecting immune–inflammatory status, including the pan-immune-inflammatory value (PIV), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). A substantial body of literature demonstrates that these peripheral-blood–derived indices have diagnostic and prognostic value in tumors, osteoarthritis, ankylosing spondylitis, acute myocardial infarction, acute appendicitis, and other infectious diseases.3–7,8 However, research on their relationship with chronic osteomyelitis remains limited.
This study aims to systematically evaluate the associations between commonly used immune–inflammatory indices (including PIV, SII, SIRI, etc.) and chronic osteomyelitis, explore their potential in early screening, and provide a noninvasive, accessible adjunct diagnostic tool to promote early diagnosis, timely treatment, and improved patient outcomes.
Materials and Methods
Study Design and Participants
This was a single-center retrospective, unmatched case-control study. We retrospectively collected data from 212 patients with chronic osteomyelitis who were diagnosed and treated in the Department of Orthopedics Trauma and Microsurgery at Zhongnan Hospital of Wuhan University from January 2020 to August 2025, according to the inclusion and exclusion criteria, as the case group. The control group consisted of non-infected trauma patients who visited our hospital during the same period. Considering the availability of medical records and research resource constraints, convenience sampling was used to enroll eligible non-infected trauma patients as controls. No individual matching or frequency matching was performed. The study protocol was approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (No. 2026004K) and complied with the Declaration of Helsinki. Controls were defined as patients admitted for trauma during the same period with no evidence of active infection based on medical records. The exclusion of active infection was determined by integrating clinical diagnosis, temperature and symptom records, laboratory tests, imaging, microbiological examinations, and discharge diagnosis during hospitalization. Participant screening and enrolment procedures are presented in Figure 1.
Figure 1.
STARD-compliant flow diagram of participant screening, enrolment and analysis.
Diagnostic Criteria
The definitive diagnosis of chronic osteomyelitis was based on microbiological culture results of surgical patients and intraoperative findings by orthopedic trauma surgeons. According to the AO Foundation/European Bone and Joint Infection Society consensus definition, a positive infection result was defined as at least two positive cultures of the same microorganism from deep tissue specimens obtained during surgery from suspected infected sites. Clinical validation criteria included wound breakdown, purulent discharge, sinus tract formation, or progression during follow-up.9–11 All microbiological results were comprehensively evaluated and adjudicated by experienced orthopedic trauma surgeons with expertise in infection interpretation. It should be noted that the culture-negative rate for chronic osteomyelitis bone specimens is relatively high; a negative culture result does not exclude the clinical diagnosis. In the presence of typical clinical and/or imaging evidence, a clinical diagnosis was made based on composite judgment.
Exclusion Criteria
Common exclusion criteria for both groups were: age < 18 years, active infection at other sites (eg, pneumonia, deep abscess, urinary tract infection), recent use of immunosuppressants or glucocorticoids, recent antibiotic administration, past or current malignancy, rheumatic immune diseases, hematological diseases, hypersplenism, or history of splenectomy.
Data Collection
Demographic and clinical characteristics including age, sex, body mass index (BMI), smoking history, drinking history, diabetes history, hypertension history, and cardiovascular disease history were collected. Laboratory data were obtained from peripheral blood complete blood count parameters within 24 hours of admission: platelet count, absolute neutrophil count, monocyte count, and lymphocyte count.
Calculation of Immune-Inflammatory Indices:
Based on peripheral blood cell counts, the following indices were calculated:
PIV = (Neutrophil count × Monocyte count × Platelet count)/Lymphocyte count
SII = (Platelet count × Neutrophil count)/Lymphocyte count
SIRI = (Neutrophil count × Monocyte count)/Lymphocyte count
NLR = Neutrophil count/Lymphocyte count
PLR = Platelet count/Lymphocyte count
Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics 27.0 and R 4.4.2. Measurement data were first tested for normality; normally distributed continuous variables were expressed as mean ± standard deviation and compared by independent samples t-test; non-normally distributed variables were expressed as median (Q1–Q3) and compared by Mann–Whitney U-test. Categorical variables were expressed as counts (%), and chi-square test or Fisher’s exact test was used for comparison. Separate binary logistic regression models were constructed with each of NLR, PLR, SIRI, PIV and SII as independent variables and chronic osteomyelitis diagnosis (0 = control, 1 = osteomyelitis) as the dependent variable, adjusting for age, sex, diabetes, and drinking history. Crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) were reported. Each inflammatory index was modeled separately to avoid including overlapping components simultaneously; variance inflation factor (VIF) was used to assess multicollinearity. ROC curves were plotted to evaluate the discriminatory ability of each index, and AUC with 95% CI was calculated using 1000 bootstrap resamples. The optimal cut-off value was determined by the maximum Youden index, with corresponding sensitivity and specificity. AUC comparisons were performed using DeLong’s test. Missing data were handled by multiple imputation. All tests were two-sided, and P < 0.05 was considered statistically significant.
Results
Comparison of General Clinical Data and Laboratory Indices Between the Two Groups
Among the enrolled cases, sex distribution differed significantly between groups (P < 0.0001), with the proportion of males in the osteomyelitis group (76.42%) being significantly higher than that in the control group (60.91%). The median age was higher in the osteomyelitis group 56.00 (47.25–64.75) years than in controls 54.00 (39.00–63.75) years(P = 0.026). Analysis of past medical history showed that the proportion of diabetes in the osteomyelitis group (22.64%) was significantly higher than that in the control group (12.50%) (χ2 = 10.313, P = 0.001). In terms of lifestyle habits, the proportion of drinkers in the osteomyelitis group (15.09%) was lower than that in the control group (23.64%), and the difference was statistically significant (χ2 = 5.817, P = 0.016). In addition, no statistically significant differences were found between the two groups in smoking history, cardiovascular history, or body mass index (BMI)(Table 1).
Table 1.
Comparison of General Clinical Data Between the Two Groups
| Variable | Chronic Osteomyelitis Group (n=212) | Control Group (n=440) | Test Statistic | P |
|---|---|---|---|---|
| Gender (n (%)) | 14.636 | <0.0001 | ||
| female | 50 (23.58) | 172 (39.09) | ||
| male | 162 (76.42) | 268 (60.91) | ||
| Smoking history (n (%)) | 0.033 | 0.855 | ||
| no | 164 (77.36) | 336 (76.36) | ||
| yes | 48 (22.64) | 104 (23.64) | ||
| Drinking history (n (%)) | 5.817 | 0.016 | ||
| no | 180 (84.91) | 336 (76.36) | ||
| yes | 32 (15.09) | 104 (23.64) | ||
| History of diabetes (n (%)) | 10.313 | 0.001 | ||
| no | 164 (77.36) | 385 (87.50) | ||
| yes | 48 (22.64) | 55 (12.50) | ||
| History of cardiovascular disease (n (%)) | 0.748 | 0.387 | ||
| no | 189 (89.15) | 403 (91.59) | ||
| yes | 23 (10.85) | 37 (8.41) | ||
| Age (years) | 56 (47.25–64.75) | 54 (39–63.75) | −2.233 | 0.026 |
| BMI (kg/m2) | 23.86 (21.41–26.33) | 23.57 (21.35–26.11) | −0.042 | 0.966 |
Laboratory test results showed (Table 2) no statistically significant difference in WBC count between the two groups. However, among other inflammation-related indices, platelet count, neutrophil count and monocyte count in the osteomyelitis group were all significantly higher than those in the control group, with statistically significant (all P<0.05). On the contrary, the median lymphocyte count in the osteomyelitis group was significantly lower than that in the control group (P=0.007).
Table 2.
Comparison of Clinical Laboratory Indices Between the Two Groups (Median (Q1-Q3))
| Variable | Chronic Osteomyelitis Group (n=212) | Control Group (n=440) | Test Statistic | P |
|---|---|---|---|---|
| White blood cell count (×109/L) | 7.25 (5.59–8.93) | 7.01 (5.38–8.70) | −1.541 | 0.123 |
| Platelet count (×109/L) | 248.00 (204.00–314.50) | 199.50 (160.00–237.75) | −8.622 | <0.0001 |
| Neutrophil count (×109/L) | 6.22 (5.00–8.16) | 4.43 (3.11–5.58) | −10.566 | <0.0001 |
| Monocyte count (×109/L) | 0.54 (0.44–0.69) | 0.50 (0.39–0.64) | −3.196 | 0.001 |
| Lymphocyte count (×109/L) | 1.39 (1.00–1.71) | 1.46 (1.14–1.88) | −2.718 | 0.007 |
Comparison of Immune-Inflammatory Indices Between the Two Groups
The results showed (Table 3) that all five inflammation and immune-related indices were significantly higher in the osteomyelitis group than in controls (all P < 0.0001). Specifically, the median NLR was 4.56 (3.08–7.01) in osteomyelitis versus 2.80 (1.96–4.11) in controls (Z = −9.748). The median PLR was 179.50 (134.29–262.07) versus 134.38 (98.50–178.09) (Z = −8.123). The median SIRI was 2.55 (1.56–4.05) versus 1.45 (0.85–2.40) (Z = −9.348). PIV was markedly higher in osteomyelitis, with a median of 612.11 (383.36–980.30) compared with 279.10 (167.27–465.66) in controls (2.2-fold increase; Z = −11.642). The median SII was 1104.95 (780.89–1892.19) versus 535.85 (374.49–819.12) (Z = −12.407). Collectively, NLR, PLR, SIRI, PIV, and SII were elevated in osteomyelitis, indicating potential utility of these composite indices for reflecting systemic inflammation in this condition.
Table 3.
Comparison of Immune-Inflammatory Indices Between the Two Groups (Median (Q1-Q3))
| Variable | Chronic Osteomyelitis Group | Control Group | Test Statistic | P |
|---|---|---|---|---|
| NLR | 4.56 (3.08–7.01) | 2.80 (1.96–4.11) | −9.748 | <0.0001 |
| PLR | 179.50 (134.29–262.07) | 134.38 (98.50–178.09) | −8.123 | <0.0001 |
| SIRI | 2.55 (1.56–4.05) | 1.45 (0.85–2.40) | −9.348 | <0.0001 |
| PIV | 612.11 (383.36–980.30) | 279.10 (167.27–465.66) | −11.642 | <0.0001 |
| SII | 1104.95 (780.89–1892.19) | 535.85 (374.49–819.12) | −12.407 | <0.0001 |
Binary Logistic Regression Analysis of Peripheral Blood Immune-Inflammatory Indices and Chronic Osteomyelitis Diagnosis
Using chronic osteomyelitis diagnosis as the dependent variable (control = 0, osteomyelitis = 1), separate binary logistic regression models were constructed with NLR, PLR, SIRI, SII, and PIV as the main independent variables. Unadjusted analyses were first performed; then adjustments were made for age, sex, drinking history, and diabetes history. Because the components of these indices overlap, they were not included simultaneously in the same model. Collinearity diagnostics were performed for the independent variables in each adjusted model before modeling, and the results showed that all variance inflation factor (VIF) values were < 5, indicating no significant multicollinearity.
Unadjusted analysis showed that NLR, PLR, SIRI, SII, and PIV were all significantly associated with chronic osteomyelitis diagnosis (all P < 0.001). After adjustment for age, sex, drinking history, and diabetes history, all indices remained statistically significant: NLR (OR = 1.298, 95% CI: 1.207–1.396), PLR (OR = 1.008, 95% CI: 1.005–1.010), SIRI (OR = 1.466, 95% CI: 1.314–1.635), SII (OR = 1.002, 95% CI: 1.001–1.002), and PIV (OR = 1.002, 95% CI: 1.002–1.003), all P < 0.001 (Table 4).
Table 4.
Binary Logistic Regression Analysis of Peripheral Blood Immune-Inflammatory Indices for the Diagnostic Status of Chronic Osteomyelitis
| Index | Crude OR (95% CI) | P | Adjusted OR (95% CI) | P |
|---|---|---|---|---|
| NLR | 1.328 (1.237–1.425) | < 0.001 | 1.298 (1.207–1.396) | < 0.001 |
| PLR | 1.008 (1.006–1.010) | < 0.001 | 1.008 (1.005–1.010) | < 0.001 |
| SIRI | 1.514 (1.361–1.685) | < 0.001 | 1.466 (1.314–1.635) | < 0.001 |
| SII | 1.002 (1.001–1.002) | < 0.001 | 1.002 (1.001–1.002) | < 0.001 |
| PIV | 1.002 (1.002–1.003) | < 0.001 | 1.002 (1.002–1.003) | < 0.001 |
Diagnostic Value of Each Immune-Inflammatory Index for Osteomyelitis
ROC curves (Figure 2A) were plotted to evaluate the diagnostic performance of the five indices (NLR, PLR, SIRI, PIV, and SII) for chronic osteomyelitis. The results are shown in Table 5: the AUC of all indices was > 0.5, with statistically significant differences (all P < 0.0001).
Figure 2.
(A) ROC curves of various indicators for identifying osteomyelitis; (B) Pair-wise comparison of the diagnostic performance of different inflammatory-immune indices.
Table 5.
Analysis of the Discriminative Value of Each Immune-Inflammatory Index for Osteomyelitis
| Variable | Cut-Off Value | AUC (95% CI) | Sensitivity (%) | Specificity (%) | P |
|---|---|---|---|---|---|
| NLR | 2.75 | 0.735(0.696-0.775) | 84.4 | 49.8 | <0.0001 |
| PLR | 150.80 | 0.696(0.653-0.739) | 66.0 | 63.0 | <0.0001 |
| SIRI | 1.81 | 0.726(0.686-0.766) | 69.8 | 63.6 | <0.0001 |
| PIV | 401.66 | 0.781(0.744-0.818) | 73.1 | 70.7 | <0.0001 |
| SII | 800.44 | 0.800(0.764-0.835) | 74.1 | 74.1 | <0.0001 |
SII and PIV showed moderate diagnostic performance: among all evaluated indices, SII exhibited the highest diagnostic efficacy, with an AUC of 0.800 (95% CI: 0.764–0.835). When the optimal cut-off value was set at 800.44, the sensitivity and specificity reached the optimal balance, being 74.1% and 74.1%, respectively. PIV ranked second in diagnostic value, with an AUC of 0.781 (95% CI: 0.744–0.818); at a cut-off value of 401.66, the sensitivity and specificity were 73.1% and 70.7%, respectively.
Diagnostic performance of NLR, SIRI, and PLR: the AUC of NLR was 0.735 (95% CI: 0.696–0.775). Notably, at a cut-off value of 2.75, NLR had the highest sensitivity (84.4%) among all indices, while its specificity (49.8%) was relatively low, suggesting that it is more suitable as a preliminary screening index. The AUC of SIRI was 0.726 (95% CI: 0.686–0.766), with corresponding sensitivity and specificity of 69.8% and 63.6%, respectively. PLR had relatively low diagnostic efficacy, with an AUC of 0.696 (95% CI: 0.653–0.739).
Pairwise Comparison of the Diagnostic Efficacy of Immune-Inflammatory Indices (DeLong Test)
To further verify whether there were statistical differences in the diagnostic efficacy of each biomarker for osteomyelitis, the DeLong test was used for pairwise comparison of the area under the ROC curve (AUC) of the five indices (NLR, PLR, SII, SIRI, and PIV) (Figure 2B). The results showed that:
(1) Significant differences of superior indices: the diagnostic efficacy of SII was significantly superior to that of NLR, PLR, and SIRI. Similarly, PIV significantly outperformed NLR, PLR, and SIRI. This indicates that SII and PIV have stronger independent diagnostic value in identifying chronic osteomyelitis.
(2) Comparison between high‑performance indices: among the two indices with the most outstanding diagnostic efficacy, there was no statistically significant difference in AUC between SII and PIV (P = 0.067, 95% CI: –0.038–0.001), suggesting that the diagnostic accuracy of the two is at a similar level, and both are superior to the other three indices.
(3) Comparisons among other indices: pairwise comparisons among NLR, PLR, and SIRI showed that the AUC differences were not statistically significant (NLR vs PLR: P = 0.071 (95% CI: 0.361–0.502); NLR vs SIRI: P = 0.394 (95% CI: 0.386–0.537); PLR vs SIRI: P = 0.211 (95% CI: –0.017–0.076)), indicating that the diagnostic performance of these three indices in osteomyelitis is essentially equivalent.
Discussion
Accurate early assessment of the body’s inflammatory burden is crucial for timely intervention, prevention of sequestrum formation, and improved prognosis in chronic osteomyelitis. Chronic osteomyelitis represents not only a local infection but a systemic inflammatory event accompanied by substantial host immune remodeling.12 Inappropriate and overuse of antibiotics, glucocorticoids and immunosuppressive agents has also contributed to a gradual increase in the prevalence of drug-resistant bacteria, further complicating the treatment of chronic osteomyelitis.13 In this retrospective cohort analysis, traditional peripheral blood total white blood cell counts showed no statistically significant difference between osteomyelitis and non-osteomyelitis cases. By contrast, composite immune–inflammatory indices derived from differential leukocyte counts—NLR, PLR, SIRI, SII, and PIV—were all markedly elevated in the osteomyelitis group (P < 0.001). These multidimensional indices appear to offer higher sensitivity and more information content than single-cell counts, reflecting the host’s inflammatory–immune imbalance and supporting their potential utility for early identification and risk-stratified management. Importantly, while WBC remained within normal ranges and did not differ between groups, the composite indices captured subthreshold shifts in neutrophil, monocyte, and lymphocyte subsets, which may explain why WBC was not discriminatory while derived indices were highly informative. The ability of these composite indices to integrate multiple cell lineages likely amplifies subtle but coordinated changes in immune cell populations that are not detectable by any single count alone.
Baseline epidemiology in this cohort showed a significantly higher proportion of males in the osteomyelitis group (76.4%) compared with controls, and a higher median age. This pattern aligns with previous findings.14 One plausible explanation is that men are more likely to engage in high-risk or physically demanding activities, increasing exposure to trauma and the risk of osteomyelitis.15 Notably, diabetes prevalence was markedly higher in the osteomyelitis group (22.6%) than in controls (12.5%). Contributing factors include: (1) hyperglycemia promoting inflammatory responses, vascular dysfunction, and impaired nutrient/oxygen delivery to bone; (2) elevated tissue and blood glucose creating a favorable milieu for bacterial growth; and (3) diabetic microvascular changes (capillary damage, occlusion, and reduced blood flow) that may facilitate hematogenous bacterial colonization.16,17 Collectively, these observations underscore the need for enhanced early screening and proactive metabolic control in high-risk populations, particularly among individuals with diabetes.
NLR and SIRI mainly reflect the imbalance between innate immune cells (neutrophils and monocytes/macrophages) and lymphocytes. When acute or persistent infection occurs, pro-inflammatory innate immune cells (especially neutrophils) are mobilized and release large amounts of lysosomal enzymes, reactive oxygen species, and chemokines, which not only directly participate in pathogen clearance but also promote pathogen capture through the formation of neutrophil extracellular traps (NETs). However, excessive NET production can lead to local tissue damage and biofilm stabilization, which is detrimental to infection eradication.18,19 Meanwhile, monocytes/macrophages amplify the inflammatory response by secreting cytokines such as IL-1, IL-6, and TNF-α, and drive abscess formation or fibrosis.19 In contrast, lymphocyte reduction or functional exhaustion (eg, T cell exhaustion) can impair specific immune responses, delay pathogen clearance, and promote chronic inflammation.20 Based on this, elevated NLR and SIRI can be viewed as concise indicators of pro-inflammatory/immunosuppressive imbalance, reflecting both inflammatory intensity and compromised immune surveillance.21
PLR incorporates platelets, emphasizing their multiple roles in infection and inflammation. Beyond hemostasis, platelets can release chemokines and pro-inflammatory mediators, interact with immune cells, and promote leukocyte adhesion and migration in the microvasculature. In addition, platelet-bacteria or implant material interactions contribute to biofilm formation, thereby increasing infection refractoriness.22,23 Elevated PLR therefore represents both inflammatory activation and platelet-mediated microvascular and biofilm-related pathology. SII (neutrophil × platelet/lymphocyte) and PIV (a composite value integrating neutrophils, monocytes, platelets, and lymphocytes) simultaneously integrate information from pro-inflammatory cells, platelets, and lymphocytes, allowing for a more comprehensive biological characterization of the three-dimensional interplay among “pro-inflammatory amplification – aggregation/repair – immunosuppression”. Therefore, when local or systemic infection triggers complex immune responses, such composite indices tend to be more sensitive and robust to disease status than single cell counts, explaining why SII and PIV demonstrated superior diagnostic performance over the other indices in this study.24
Limitations of this study should be acknowledged. First, the retrospective design and single‑center setting raise concerns about selection bias and potential confounding factors that may limit generalizability. Second, the study only captured baseline hematologic parameters at admission, without data on the dynamic trajectories of these indices during antimicrobial therapy or surgical management. Emerging evidence indicates that the trajectory or rate of change of inflammatory indices can improve prediction of infection eradication and recurrence. Third, the lack of subgroup analyses by causative pathogen (eg, MRSA, Gram‑negative bacilli) may overlook pathogen‑specific immune responses. Fourth, we did not compare our indices with established biomarkers such as CRP and ESR, which limits the assessment of added clinical value. Fifth, the AUCs for SII and PIV (0.800 and 0.781, respectively) indicate moderate rather than excellent discrimination; thus, these markers should be interpreted as adjunctive tools rather than standalone diagnostic tests. Sixth, no adjustment for multiple comparisons was performed in the DeLong tests, which may increase the risk of type I error; these findings should therefore be considered exploratory. Future work should include multicenter, large‑sample, prospective cohorts to establish stage‑specific cut‑off values for novel composite indices such as SII and PIV, and to validate their utility in combination with other inflammatory markers such as CRP and ESR, thereby informing precision treatment strategies in osteomyelitis.
Conclusion
In this study, NLR, PLR, SIRI, PIV, and SII were all elevated in osteomyelitis patients. SII and PIV exhibited good discriminatory performance, superior to NLR, PLR, and SIRI, and may serve as straightforward hematological biomarkers for auxiliary diagnosis of osteomyelitis. The DeLong test indicated that SII had significantly better discriminatory ability than NLR, PLR, and SIRI in this study; there was no statistically significant difference in AUC between SII and PIV, suggesting that their diagnostic values are similar. Given the single‑center retrospective nature, further validation in multicenter prospective cohorts is recommended to confirm the above conclusions and to establish robust clinical cut‑off values.
Ethics Statement
This study was approved by the Medical Ethics Committee of Zhongnan Hospital of Wuhan University (No. 2026004K) and complied with the Declaration of Helsinki. Due to the retrospective nature of the study and the use of anonymized routinely collected clinical data, the requirement for individual written informed consent was waived by the ethics committee.
Disclosure
The authors report no conflicts of interest in this work.
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