Identifying predictors of surgical site infection in closed patellar fracture surgery: a multicenter study

Abstract

Surgical site infection (SSI) is a prevalent complication in orthopedic surgery, leading to significant morbidity and potentially severe outcomes. This study aimed to identify predictors of SSI in patients undergoing closed patellar fracture surgery. We retrospectively analyzed 2664 patients treated for closed patellar fractures at two hospitals from November 2013 to January 2023. Patients were divided into SSI and non-SSI groups. We employed univariate analysis, logistic regression, and receiver operating characteristic (ROC) curve analyses to evaluate demographic data, comorbidities, and preoperative laboratory results. The SSI rate was 1.4% (38 of 2664 patients). Univariate analysis revealed significant predictors of SSI, including higher preoperative globulin measured at admission (first blood draw) (p = 0.024), high-energy trauma (p < 0.001), higher ASA scores (p = 0.016), and prolonged preoperative stay (p = 0.013). Logistic regression confirmed these factors as independent predictors: elevated GLOB levels (p = 0.001, OR 1.108), high-energy trauma (p = 0.004, OR 2.674), higher ASA scores (p = 0.044, OR 2.228), and prolonged preoperative stay (p = 0.013, OR 1.075). Conversely, high albumin (ALB) levels were protective against SSI (p = 0.002, OR 0.918). ROC analysis determined the cut-off values for predicting SSI as 21.98 g/L for GLOB and 8 days for preoperative stay. Elevated preoperative GLOB levels, high-energy trauma, higher ASA scores, and extended preoperative stays were found to be independent predictors of SSI in closed patellar fracture surgery. Conversely, high ALB levels were protective. These findings are exploratory and underscore the importance of timely surgery and routine biomarker assessment; confirmation in larger, prospectively collected cohorts with standardized antimicrobial prophylaxis and prevention bundles is warranted.

Introduction

Patellar fractures are common knee injuries, typically resulting from direct trauma such as falls or impacts, or from indirect forces like sudden quadriceps contraction¹. The annual incidence is about 13.5 per 100,000 people, with higher rates observed in young males and elderly females². Young males often experience high-energy trauma from traffic accidents or sports injuries, while elderly females typically suffer from low-energy trauma like falls³. Nonoperative treatment is preferred for nondisplaced or minimally displaced patellar fractures, involving the use of long leg casts or braces to immobilize the knee and promote healing. Surgical treatment is indicated for significantly displaced fractures, especially when fracture fragments are separated by more than 2–3 mm or when the articular surface step-off exceeds 2 mm, as these conditions often result in knee dysfunction and require surgical intervention to restore proper alignment and function⁴. The most common and widely used operative technique is the modified anterior tension-band wiring. This method employs a vertical figure-of-eight pattern or combines cannulated lag screws with tension-band wiring to neutralize the tension forces on the patella, converting them into compressive forces to facilitate fracture healing⁵.

However, patellar fractures treated with metal implants are often associated with local soft tissue irritation and wound complications, which can increase the risk of infection⁶. The incidence of surgical site infections (SSIs) following patellar fracture surgery ranges from approximately 2–7%⁷.When infections occur, they can lead to significant complications, including delayed healing or nonunion of the fracture, the need for additional surgeries, prolonged pain, and functional impairment. These complications often result in extended hospital stays and increased medical costs. Furthermore, SSIs can trigger systemic complications such as sepsis, which can be life-threatening in severe cases⁸.

Despite the clinical significance, existing research on the prevalence and risk factors of SSIs after patella fracture surgery is sparse, with few large-scale studies addressing this critical issue. To bridge this knowledge gap, we conducted a comprehensive multicenter retrospective study focusing on patients with closed patella fractures. Our objective is to determine the prevalence and identify the risk factors associated with SSIs in this patient population. The insights gained from this extensive analysis will assist healthcare providers in developing targeted strategies to prevent SSIs, ultimately improving patient care and outcomes.

Materials and methods

Ethics statement

Our study involved a comprehensive review of electronic medical records for patients who underwent surgical treatment for closed patellar fractures. These cases were identified and treated at Hebei Medical University Third Hospital and Baoding No. 1 Central Hospital between November 2013 and January 2023. Ethical approval was secured from the institutional review boards of both hospitals, adhering to the ethical guidelines of the 1964 Declaration of Helsinki (approval number: 2023–002-1, 2,024,181).

Patients

This retrospective study was conducted at Hebei Medical University Third Hospital and Baoding No.1 Central Hospital, both tertiary care facilities with Level I trauma centers. Patients eligible for inclusion were those who underwent surgical treatment for closed patellar fractures, had complete preoperative medical histories and follow-up records, and were over 18 years of age. The exclusion criteria were: (1) patients with open patellar fractures; (2) patients showing signs of infection before surgery; and (3) patients with additional fractures around the knee joint, to avoid confounding the study results (Fig. 1).

Fig. 1.

Exclusion criteria and the eligible cases included in this study.

Based on these criteria, we included 2,664 patients (1,730 men and 934 women) who had undergone surgical treatment for closed patellar fractures. These patients were subsequently categorized into two groups: those who developed surgical site infections (SSI group) and those who did not (non-SSI group) following the procedure.

Diagnosis of patellar fractures

Patients with suspected patellar fractures were diagnosed through clinical evaluation and imaging studies. Clinically, these patients often presented with pain, swelling, and an inability to extend the knee or perform a straight leg raise. Physical examinations typically revealed tenderness and sometimes a palpable defect in the patella. Imaging, primarily standard radiographs including anteroposterior (AP), lateral, and oblique views of the knee, was crucial for confirming the diagnosis. In more complex cases, CT scans provided detailed views of the fracture pattern, aiding in precise classification and treatment planning⁹.

Surgical treatment strategies for closed patellar fractures

Patients underwent surgical treatment involving techniques such as tension band wiring combined with Kirschner wires (K-wires)¹⁰. This method converts tensile forces into compressive forces, stabilizing the fracture and allowing early knee mobilization. An incision was made to access the fracture site, ensuring precise alignment of bone fragments before fixation. In cases of comminuted fractures, additional K-wires were used for enhanced stability¹¹.

Perioperative infection-prevention protocol

Across participating hospitals, a standardized infection-prevention bundle was in place throughout the study period and remained unchanged. Antimicrobial prophylaxis consisted of a first- or second-generation cephalosporin (e.g., cefazolin or cefuroxime) administered within 60 min before skin incision, with weight-based dosing and intraoperative re-dosing when indicated (prolonged procedures or substantial blood loss). Prophylaxis was discontinued within 24 h postoperatively per institutional policy. Non-pharmacologic measures included alcohol-based chlorhexidine skin preparation, clipper-only hair removal (no razors), active warming to maintain normothermia, perioperative glycemic management, and standard sterile draping procedures. The protocol was applied uniformly across sites; however, patient-level adherence was not abstracted and therefore these variables were not incorporated into the models.

Definition of SSI

SSI was defined according to the Centers for Disease Control and Prevention (CDC) criteria¹². Superficial SSI involved infection of the skin and subcutaneous tissue within 30 days postoperatively, characterized by localized pain, persistent wound purulent discharge, spontaneous incision dehiscence, and positive bacterial culture. Deep SSI, on the other hand, affected deeper soft tissue layers, such as fascia or muscle, within 90 days postoperatively and included similar symptoms. The decision to perform open surgical intervention for SSI depended on the condition of the surgical incision and the bacterial culture results¹³.

Data collection

The study analyzed 72 variables potentially linked to the development of SSI. These variables included patient demographics such as age, gender, BMI (body mass index), minority status, occupation, time from injury to admission, and preoperative stay. Other factors considered were the mechanism of injury, whether the trauma was high-energy (defined as injuries from car crashes, crush injury or hurt by a heavy object), and ASA (American Society of Anesthesiologists) scores, which were categorized into grades 1–2 and grades 3–4. Lifestyle factors like smoking and alcohol consumption were also included. Additionally, comorbidities such as coronary heart disease, hypertension, diabetes, cerebral infarction, anemia, and hypoproteinemia were considered. Besides, we examined a comprehensive range of preoperative laboratory indicators to assess their association with the development of SSI. All preoperative laboratory variables were defined as the results from the first blood draw at hospital admission. If multiple tests were performed on the admission date, the earliest timestamp was used. These indicators included various blood cell counts such as basophils (BAS), eosinophils (EOS), hematocrit (HCT), hemoglobin (HGB), immature cells (IMM), lymphocytes (LYM), monocytes (MON), neutrophils (NEU), platelets (PLT), red blood cells (RBC), and white blood cells (WBC). Biochemical parameters included albumin (ALB), alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine transaminase (ALT), calcium (Ca), globulin (GLOB), cholinesterase (CHE), creatine kinase (CK), creatine kinase MB (CKMB), creatinine (CREA), direct bilirubin (DBIL), glucose (GLU), GTPase-activating protein (GAP), gamma-glutamyltransferase (GGT), potassium (K), indirect bilirubin (IBIL), lactic dehydrogenase (LDH), phosphorus (P), total bilirubin (TBIL), triglycerides (TG), total cholesterol (TC), total carbon dioxide (TCO2), total protein (TP), urea, uric acid (UA), and very low-density lipoprotein (VLDL). Coagulation parameters included activated partial thromboplastin time (APTT), fibrinogen (FIB), international normalized ratio (INR), prothrombin time (PT), and prothrombin activity (PTA). Additionally, we assessed specific ratios such as the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR). These indicators provided a thorough preoperative assessment to better understand the patients’ baseline health status and potential risks associated with SSI.

Statistical analysis

In this study, we utilized SPSS software (version 25.0, SPSS Inc., New York, USA) for statistical analysis, considering a p-value of less than 0.05 as statistically significant. The normality of continuous variables was assessed using the Shapiro–Wilk test. Variables that met the normality assumption were expressed as mean ± standard deviation (SD) and compared using the t-test. For variables that did not meet the normality assumption, the Mann–Whitney U test was applied. Categorical variables were presented as numbers and percentages, with between-group differences evaluated using Chi-square and Fisher’s exact tests. To identify independent risk factors for SSI after surgery in patients with closed patella fractures, binary logistic regression analysis was performed to determine the best predictors of SSI.

To determine appropriate cut-off values for continuous variables, we used ROC (receiver operating characteristic) analysis, commonly utilizing the Youden index (sensitivity + specificity—1) to maximize diagnostic accuracy. Variables were categorized into low-risk and high-risk groups based on these cut-off values. The diagnostic ability of each variable was assessed by the area under the ROC curve (AUC), which ranges from 0 to 1, with a larger AUC indicating greater diagnostic capability.

Results

This study included 2,664 patients, comprising 1,730 men and 934 women, all of whom underwent open reduction and internal fixation for closed patellar fractures. The incidence of SSI following this procedure was 1.4%. Specifically, 35 patients (1.3%) developed SSIs during hospitalization, while an additional 3 patients (0.1%) were diagnosed with SSIs upon readmission for further intervention.

All 38 patients underwent wound secretion bacterial culture and drug sensitivity testing to guide appropriate treatment. The most frequently isolated pathogen was Staphylococcus aureus, found in 25 patients (65.8%). Escherichia coli was identified in 5 patients (13.2%), while Pseudomonas aeruginosa was cultured from 3 patients (7.9%). Enterobacter cloacae was present in 2 patients (5.3%), and mixed bacterial infections were observed in 3 patients (7.9%).

Table 1 describes the demographics and comorbidities data of patients with and without SSI. Significant differences were observed between the SSI and non-SSI groups in several key areas. There were statistically significant differences in BMI (p = 0.005), preoperative stay (p = 0.017), mechanism of injury (p = 0.005), high-energy trauma (p < 0.001), and ASA classification (p = 0.016). The study revealed that patients with higher BMI levels, prolonged preoperative stay, injuries caused by high-energy trauma, and higher ASA scores were more prone to developing SSIs. Other variables, such as smoking and alcohol consumption history, showed no significant statistical differences between the two groups (all p > 0.05).

Table 1.

Demographics and comorbidities data of patients with and without SSI.

Characteristics	SSI group(n = 38)	Non-SSI group(n = 2626)	p
Age, years	49.5(37.3 ~ 57.8)	52.0(40.0 ~ 62.0)	0.229
Gender, n(%)			0.139
male	29(76.3%)	1701(64.8%)
female	9(23.7%)	925(35.2%)
BMI, kg/m2	25.19(24.67 ~ 28.13)	25.15(22.86 ~ 26.12)	0.005*
Minority, n(%)			0.536
Yes	2(5.3%)	210(8.0%)
No	36(94.7%)	2416(92.0%)
Occupation,n(%)			0.390
Workers	3(7.0%)	206(7.8%)
Peasants	22(57.9%)	1192(45.4%)
Students	0(0.0%)	28(1.1%)
Retiree	2(5.3%)	311(11.8%)
Staff	1(2.6%)	246(9.4%)
Freelancer	6(15.8%)	238(9.0%)
Civil servant	4(10.5%)	405(15.4%)
Time from injury to admission, hours	10.0(4.8 ~ 48.0)	8.0(4.0 ~ 24.0)	0.450
< 12	24(63.2%)	1552(59.1%)	0.476
12–24	4(10.5%)	474(18.1%)
> 24	10(26.3%)	600(22.8%)
Preoperative hospital stay, days	4.0(3.0 ~ 9.0)	4.0(2.0 ~ 6.0)	0.017*
Mechanism of injury,n(%)			0.005*
car crash injury	16(42.1%)	477(18.2%)
Fall Injury	22(57.9%)	2082(79.3%)
crush injury	0(0.0%)	9(0.3%)
sprain	0(0.0%)	28(1.1%)
hurt by a heavy object	0(0.0%)	30(1.1%)
High-energy trauma, n(%)			< 0.001*
Yes	16(42.1%)	516(19.6%)
No	22(57.9%)	2110(80.4%)
ASA classification, n(%)			0.016*
I, II	29(76.3%)	2332(88.8%)
III, IV	9(23.7%)	294(11.2%)
Smoking history, n(%)			0.328
Yes	2(5.3%)	264(10.1%)
No	36(94.7%)	2362(89.9%)
Alcohol history, n(%)			0.084
Yes	0(0.0%)	191(7.3%)
No	38(100.0%)	2435(92.7%)
Coronary heart disease, n(%)			0.078
Yes	0(0.0%)	199(7.6%)
No	38(100.0%)	2427(92.4%)
Hypertension, n(%)			0.626
Yes	10(26.3%)	603(23.0%)
No	28(73.7%)	2023(77.0%)
Diabetes, n(%)			0.937
Yes	4(10.5%)	287(10.9%)
No	34(89.5%)	2339(89.1%)
Cerebral Infarction, n(%)			0.930
Yes	2(5.3%)	130(5.0%)
No	36(94.7%)	2496(95.0%)
Anemia,n(%)			0.590
Yes	14(36.8%)	859(32.7%)
No	24(63.2%)	1767(67.3%)
hypoproteinemia,n(%)			0.455
Yes	3(7.9%)	136(5.2%)
No	35(92.1%)	2490(94.8%)

BMI = body mass index; ASA = American Society of Anesthesiologists; Values are presented as the number(%) or the median(interquartile range). *p < 0.05, statistical significance.

Table 2 presents an extensive analysis of 54 preoperative laboratory indicators for both the SSI and non-SSI groups. The results indicated that the SSI group had significantly elevated levels of LYM (p = 0.016) and GLOB (p = 0.024) compared to the non-SSI group. Moreover, the ALB levels were notably lower in the SSI group (p = 0.013). However, no significant differences were observed between the two groups for the remaining 51 laboratory indicators (all p > 0.05).

Table 2.

Laboratory results of patients with and without SSI.

	SSI group(n = 38)	Non-SSI group(n = 2626)	p
BAS	0.03(0.02 ~ 0.05)	0.03(0.02 ~ 0.04)	0.188
EOS	0.06(0.03 ~ 0.17)	0.06(0.02 ~ 0.12)	0.245
HCT	38.58(34.36 ~ 40.81)	38.14(34.28 ~ 41.44)	0.834
HGB	130.90(115.33 ~ 141.08)	128.00(114.90 ~ 139.70)	0.844
IMM	0.01(0.00 ~ 0.04)	0.01(0.00 ~ 0.03)	0.674
LYM	1.74(1.46 ~ 2.03)	1.50(1.16 ~ 1.93)	0.016*
MCH	31.12(29.62 ~ 32.34)	31.12(30.08 ~ 32.22)	0.921
MCHC	334.60(326.78 ~ 342.78)	336.25(330.00 ~ 343.00)	0.421
MON	0.64(0.52 ~ 0.93)	0.60(0.46 ~ .0.77)	0.083
MCV	91.57(89.48 ~ 96.41)	92.31(89.49 ~ 95.58)	0.928
MPV	8.21(7.69 ~ 9.41)	8.68(7.95 ~ 9.50)	0.095
NEU	6.20(4.74 ~ 6.86)	5.83(4.42 ~ 7.78)	0.866
PLT	214.80(179.48 ~ 242.63)	204.00(168.78 ~ 244.50)	0.388
RBC	4.16(3.74 ~ 4.47)	4.13(3.73 ~ 4.50)	0.668
WBC	8.80(6.92 ~ 9.48)	8.17(6.69 ~ 10.18)	0.665
ALB	39.59(37.08 ~ 41.38)	41.00(37.90 ~ 43.50)	0.013*
ALP	69.00(49.75 ~ 82.00)	63.00(51.00 ~ 75.00)	0.187
ALT	17.00(12.00 ~ 31.00)	19.00(13.00 ~ 31.00)	0.548
AST	20.00(16.00 ~ 28.75)	19.00(15.00 ~ 29.00)	0.361
Ca	2.21(2.08 ~ 2.26)	2.21(2.11 ~ 2.31)	0.347
CHE	7.76(6.80 ~ 8.85)	7.73(6.45 ~ 9.03)	0.984
CK	119.20(70.50 ~ 305.50)	121.00(71.00 ~ 301.00 )	0.853
CKMB	13.25(10.70 ~ 16.78)	12.80(9.40 ~ 19.00 )	0.809
CL	105.30(103.60 ~ 107.10)	104.80(102.55 ~ 106.80)	0.144
CREA	69.98(52.53 ~ 76.55)	63.88(54.70 ~ 73.80)	0.421
DBIL	4.46(3.34 ~ 5.87)	4.51(3.20 ~ 6.34)	0.723
GAP	10.95(7.85 ~ 12.68)	10.88(8.70 ~ 13.00)	0.709
GGT	21.00(14.00 ~ 36.25)	21.00(15.00 ~ 33.25)	0.742
GLOB	23.98(22.67 ~ 28.58)	23.10(20.20 ~ 26.10)	0.024*
GLU	5.59(4.78 ~ 6.68)	5.80(5.17 ~ 6.83)	0.169
K	3.85(3.61 ~ 4.19)	3.91(3.66 ~ 4.17)	0.802
IBIL	12.14(7.56 ~ 15.91)	10.64(7.60 ~ 14.32)	0.359
LDH	192.18(155.75 ~ 258.55)	184.00(158.47 ~ 225.05)	0.401
Mg	0.88(0.80 ~ 0.97)	0.88(0.81 ~ 0.95)	0.895
Na	139.72(138.00 ~ 141.05)	139.60(137.90 ~ 141.50)	0.609
P	1.12(1.00 ~ 1.24)	1.11(0.96 ~ 1.25)	0.783
TBIL	15.52(11.80 ~ 22.16)	15.10(11.20 ~ 20.30)	0.696
TC	4.16(3.29 ~ 4.78)	4.16(3.56 ~ 4.82)	0.651
TCO2	25.05(22.35 ~ 27.00)	24.71(22.60 ~ 27.00)	0.932
TG	1.15(0.84 ~ 1.67)	1.13(0.80 ~ 1.67)	0.802
TP	64.66(61.53 ~ 68.50)	64.10(59.87 ~ 68.20)	0.489
UA	305.00(240.00 ~ 353.00)	295.00(230.00 ~ 363.00)	0.663
UREA	4.81(4.09 ~ 5.63)	5.10(4.22 ~ 6.24)	0.108
VLDL	0.53(0.39 ~ 0.76)	0.51(0.36 ~ 0.76)	0.804
APTT	29.05(27.25 ~ 31.93)	29.70(27.50 ~ 32.20)	0.315
D-DIMER	0.60(0.16 ~ 2.64)	0.76(0.30 ~ 1.78)	0.835
FIB	2.79(2.45 ~ 3.44)	3.09(2.56 ~ 3.74)	0.078
INR	1.05(1.00 ~ 1.09)	1.05(0.99 ~ 1.11)	0.827
PT	11.55(11.18 ~ 12.40)	11.70(11.10 ~ 12.60)	0.610
PTA	93.00(88.30 ~ 103.03)	94.00(86.00 ~ 103.43)	0.983
TT	14.60(13.23 ~ 15.93)	14.50(13.50 ~ 15.90)	0.897
PLR	124.28(104.63 ~ 142.15)	134.34(102.10 ~ 177.55)	0.104
NLR	3.38(2.56 ~ 4.70)	3.79(2.62 ~ 5.95)	0.167
MLR	0.38(0.29 ~ 0.57)	0.40(0.29 ~ 0.57)	0.819

BAS = basophil; EOS = eosinophil; HCT = hematocrit; HGB = hemoglobin; IMM = immature; LYM = lymphocyte; MCH = mean corpusular hemoglobin; MCHC = mean corpusular hemoglobin concentration; MON = monocyte; MCV = mean corpuscular volume; MPV = mean platelet volume; NEU = neutrophil; PLT = platelet; RBC = red blood cell; WBC = white blood cell; ALB = albumin; ALP = alkaline phosphatase; AST = aspartate aminotransferase; ALT = alanine transaminase; Ca = Calcium; GLOB = globulin; CHE = cholinesterase; CK = creatine kinase; CKMB = creatine kinase MB; CREA = creatinine; DBIL = direct bilirubin; GLU = glucose; GAP = GTPase-activating protein; GGT = Gamma-glutamyltransferase; K = kalium; IBIL = indirect bilirubin; LDH = lactic dehydrogenase; P = phosphorus; TBIL = total bilirubin; TG = triglyceride; TC = total cholesterol; TCO2 = total carbon dioxide; TP = total protein; UREA = ureophil; UA = uric acid; VLDL = very low-density lipoprotein; APTT = activated partial thromboplastin time; FIB = fibrinoge; INR = international normalized ratio; PT = prothrombin time; PTA = prothrombin activity; prothrombin activity; PLR = platelet-to-lymphocyte ratio; NLR = neutrophil-to-lymphocyte ratio; MLR = monocyte-to-lymphocyte ratio. Values are presented as the number(%) or the median(interquartile range). *p < 0.05, statistical significance.

Logistic regression analysis identified several significant factors associated with the occurrence of SSI. Notably, prolonged preoperative stay were at an increased risk of developing SSI (p = 0.013, OR = 1.075, 95% CI [1.015 to 1.138]). High-energy trauma was another significant risk factor, substantially elevating the likelihood of SSI (p = 0.004, OR = 2.674, 95% CI [1.359 to 5.260]). Additionally, higher ASA scores were correlated with a greater risk of SSI (p = 0.044, OR = 2.228, 95% CI [1.022 to 4.855]), and elevated globulin (GLOB) levels were closely linked to increased SSI risk (p = 0.001, OR = 1.108, 95% CI [1.040 to 1.179]). Conversely, albumin (ALB) levels were found to be a protective factor against SSI. Higher ALB levels were associated with a reduced risk of SSI (p = 0.002, OR = 0.918, 95% CI [0.869 to 0.970]), suggesting that maintaining adequate albumin levels may help prevent the occurrence of SSI (Table 3).

Table 3.

Binary logistic regression analysis of variables associated with SSI.

Characteristics	OR	95%CI	p
Preoperative hospital stay	1.075	1.015 to 1.138	0.013*
High-energy trauma	2.674	1.359 to 5.260	0.004*
ASA	2.228	1.022 to 4.855	0.044*
GLOB	1.108	1.040 to 1.179	0.001*
ALB	0.918	0.869 to 0.970	0.002*

Our analysis includes ROC curves for two significant predictors of SSI: GLOB levels and prolonged preoperative stay. Detailed information about these predictors is shown in Figs. 2 and 3. For GLOB, the ROC analysis yielded a p-value of 0.016 with an AUC of 0.607 (95% CI [0.588 to 0.625]), and a cut-off value of 21.98 g/L. The prolonged preoperative stay had a p-value of 0.020 with an AUC of 0.612 (95% CI [0.593 to 0.631]), and a cut-off value of 8 days. When these factors were combined, the diagnostic value significantly improved. The combined ROC analysis showed an AUC of 0.664, indicating a better diagnostic capability for predicting SSI (Fig. 4).

Fig. 2.

ROC curve for GLOB.

Fig. 3.

ROC curve for preoperative stays.

Fig. 4.

ROC curve for GLOB + preoperative stays.

Discussion

Patellar fractures, commonly caused by trauma or sudden quadriceps contractions, have an incidence of about 13.5 per 100,000 people^1,2. Young males typically sustain these fractures from high-energy events, while elderly females often suffer from falls. Nondisplaced fractures are treated nonoperatively, whereas significantly displaced fractures require surgical intervention, commonly using modified anterior tension-band wiring. This surgical method can lead to SSIs, with rates between 2–7%, causing delayed healing, additional surgeries, and systemic complications like sepsis⁷. Despite the importance of this issue, research on SSIs in patellar fractures is limited. Our multicenter retrospective study aims to identify the prevalence and risk factors of SSIs to improve prevention and patient outcomes.

To our knowledge, this study represents the first extensive retrospective analysis aimed at examining the prevalence and identifying the potential risk factors for SSIs following surgery for closed isolated patellar fractures. Our study found an SSI rate of 1.4% (38 out of 2664 patients). Through univariate analysis, we identified several SSI predictors: BMI, preoperative stay, mechanism of injury, high-energy trauma, ASA classification, LYM, and GLOB. ALB emerged as a protective factor. Logistic regression analysis highlighted that prolonged preoperative stay, high-energy trauma, and elevated GLOB levels significantly increased SSI risk. ROC curve analysis determined the cut-off values for predicting SSI were 21.98 g/L for GLOB and 8 days for preoperative stay. The combination of these factors provided the most accurate diagnostic prediction for SSIs.

The incidence of infection in our cohort was 1.4%, which is relatively low compared to the postoperative infection rates reported in other studies, which range from 2.1% to 10%^6,14–17. For example, Kadar et al. found a high infection rate of 6.9% in their cohort, which they attributed to the relatively older average age of the patients (56 years) and a high proportion of associated comorbidities (56%)¹⁶. Similarly, Christopher et al. reported a 3.2% infection rate in a meta-analysis of 18 studies involving 522 patellar fractures; however, their analysis included patients with open patellar fractures and complex multi-trauma cases¹⁷. In contrast, our study had a larger sample size and a younger average patient age (51 years). Additionally, we excluded cases with potential confounding factors such as open injuries and multiple fractures. These differences likely explain why our infection rate of 1.4% is lower than that reported in the previous studies.

The impact of preoperative hospital stays on the risk of SSI in patellar surgery must be carefully considered due to the multifactorial contributing factors. Extended preoperative stays may be necessary for optimizing patient comorbidities, dealing with unavailability of surgical resources, or scheduling issues such as weekends and holidays. In our study, the average preoperative stay for all patients with closed patellar fractures requiring surgical treatment was 4.57 days. We found that delaying surgery is an independent risk factor for postoperative infections. Specifically, for each additional day of delay, the risk of SSI increases by 1.075 times. Moreover, through ROC curve analysis, we determined that the cut-off value for the preoperative stay is 8 days. This means that if surgery is delayed beyond 8 days, the risk of SSI significantly increases. Supporting this, Meng et al. found in their retrospective study that prolonged preoperative stays were independently associated with an increased risk of SSI, with each additional day of delay increasing the risk by 1.21 times¹⁸. A systematic review further highlighted that the preoperative phase is crucial for preventing SSIs, noting that prolonged hospital stays before surgery are linked to higher infection rates¹⁹. Additionally, research on neurosurgical patients demonstrated that preoperative stays of 7 or more days significantly increased SSI risk compared to shorter stays²⁰. Therefore, minimizing preoperative hospital stays is essential. Reducing unnecessary delays can help lower infection rates, optimize patient outcomes, and improve overall surgical care.

In our study, the mechanisms of injury were categorized into five types: car crash injury, fall injury, crush injury, sprain, and being hurt by a heavy object. Among these, we defined car crash injury, fall injury, and being hurt by a heavy object as high-energy injuries. The specific injury mechanisms for the patients in this study are detailed in Table 1. Interestingly, we found that patients with high-energy injuries had a significantly higher probability of developing SSIs compared to those with low-energy injuries, with the risk being 2.674 times greater. Hu et al. found in their study on calcaneal fractures that patients who suffered high-energy injuries had a 5.6 times higher likelihood of developing SSIs compared to the control group²¹. Additionally, research on closed pilon fractures also demonstrated that high-energy trauma, categorized by severe injury classifications, was an independent risk factor for SSIs²². Cheng et al. also showed that patients with high-energy trauma typically have longer surgical times and more extensive surgical interventions, both contributing to a higher risk of SSIs²³. These findings align with our study results. High-energy injuries result in significant damage to the patella and surrounding soft tissues, severely compromising the blood supply to the knee joint. This compromised blood supply creates an environment conducive to bacterial growth at the surgical site, thereby increasing the risk of SSIs²⁴. Furthermore, high-energy patellar fractures are often more complex and necessitate longer surgical procedures. Clinicians should be vigilant in monitoring soft tissue edema in patients with high-energy patellar fractures. Implementing measures such as administering mannitol and delaying surgery can help alleviate soft tissue edema. These precautions are essential to reduce the elevated risk of SSIs associated with high-energy injuries, ultimately improving patient outcomes and minimizing postoperative complications.

The ASA classification system is used to assess and communicate a patient’s pre-anesthesia medical comorbidities, categorizing patients based on their overall health status and predicting potential perioperative risks, though it is not solely a risk classification system. Our study found that patients undergoing patellar fracture surgery with an ASA classification of III or higher have a significantly increased risk of developing SSIs. Specifically, the probability of developing SSIs in patients with ASA III or higher is 2.228 times greater compared to those with ASA I or II. Supporting this, a review study by Marzoug et al. found that an ASA classification of III or higher is an independent risk factor for SSIs among general surgery patients in specialized hospitals in the Amhara region²⁵. Similarly, Meron et al. discovered that surgical patients with an ASA score of III or higher had a 6.7 times higher hazard of developing SSIs compared to those with an ASA classification of II or lower²⁶. Additionally, a retrospective cohort study by Wang et al. found that higher ASA scores were closely associated with wound breakdown and subsequent infections²⁴. Our findings align with these studies, showing that patients undergoing patellar fracture surgery with an ASA classification of III or higher have a significantly increased risk of developing SSIs. This increased risk is due to several mechanisms. Patients with higher ASA scores generally have more severe systemic diseases, which can impair their immune response and wound healing capabilities. These patients are also more likely to have multiple comorbidities that can complicate surgery and recovery²⁷. These findings underscore the importance of thorough preoperative assessment and optimization of patients’ health status to mitigate the risk of SSIs, particularly in those with higher ASA classifications. Enhanced perioperative care and tailored infection prevention strategies are crucial for improving surgical outcomes in these high-risk groups.

GLOB are a group of proteins in the blood, produced primarily by the liver and the immune system. They play various crucial roles in the human body. Gamma globulins, also known as immunoglobulins, are essential components of the immune system. They help fight infections by identifying and neutralizing pathogens like bacteria and viruses. Elevated globulin levels can be a sign of acute or chronic infections as the body produces more immunoglobulins to combat pathogens²⁸. Our study found that preoperative GLOB levels were significantly higher in patients who developed SSI compared to those who did not. Specifically, for patients undergoing patellar fracture surgery, each 1 g/L increase in GLOB was associated with a 1.108-fold increase in the risk of developing SSI. Consequently, we identified patients with preoperative GLOB levels exceeding 21.98 g/L as being at high risk for SSI. Elevated GLOB levels have been linked to various infections and inflammatory conditions, suggesting a potential role in immune response and infection susceptibility. Research has found that high globulin levels can be indicative of autoimmune diseases, infections, or chronic inflammation²⁹. A detailed review noted that elevated globulin levels are often associated with conditions where the immune system is actively responding to pathogens³⁰. Supporting this, a study on sheep by Bastos et al. found that elevated IgM levels were associated with a reduced risk of abscess formation, highlighting the protective role of globulins in infection control³¹. These findings align with our observations, underscoring the importance of measuring globulin levels as part of the diagnostic process for infections and related conditions. Elevated globulin levels, therefore, serve as a crucial biomarker for identifying and monitoring infections and inflammatory states in patients.

Interestingly, our study found that ALB serves as a protective factor against the occurrence of SSI. High levels of ALB appear to be beneficial in preventing the development of SSI. Several studies support this finding. For instance, a meta-analysis indicated that patients with serum albumin levels below 35 g/L had a significantly higher risk of developing SSIs compared to those with higher albumin levels. This suggests that maintaining higher albumin levels could reduce the risk of infections post-surgery³². Additionally, another study found that a higher perioperative albumin level was associated with better postoperative outcomes, including a lower incidence of SSIs³³. These findings highlight the protective role of albumin in infection control and underscore the importance of assessing and managing albumin levels as part of preoperative care to mitigate the risk of SSIs. The protective mechanism of ALB against SSI primarily involves its role in maintaining nutritional status and immune function. High albumin levels indicate good nutritional status, which is essential for a robust immune response and effective wound healing. Additionally, albumin has anti-inflammatory properties, helping to neutralize endotoxins and reduce systemic inflammation. By maintaining oncotic pressure, albumin prevents tissue edema, creating an environment less conducive to bacterial growth. These factors collectively enhance the body’s ability to prevent and control infections, emphasizing the importance of adequate albumin levels in surgical patients^33,34.

Limitation.

Despite the comprehensive nature of this study, several limitations must be acknowledged. First, the retrospective design of the study inherently limits the ability to establish causality between identified risk factors and SSI. Second, data were collected from electronic medical records, which may be subject to documentation biases and inaccuracies. Third, our study was conducted at two tertiary care centers, which may limit the generalizability of the findings to other settings, especially those with different patient populations or healthcare practices. Additionally, we did not account for variations in surgical techniques, surgeon experience, operation time or postoperative care protocols, which could influence SSI rates. Besides, although a standardized infection-prevention bundle and antimicrobial prophylaxis protocol were in place across sites, patient-level adherence was not systematically captured, precluding model adjustment and leaving the possibility of residual confounding. Finally, the exclusion of patients with open fractures or multiple injuries may limit the applicability of our findings to more complex clinical scenarios. Future prospective studies with larger, more diverse populations and standardized protocols are necessary to validate our results and explore the mechanisms underlying the observed associations.

Conclusion

In conclusion, our study identified elevated preoperative GLOB levels, High-energy trauma, higher ASA scores and prolonged preoperative hospital stay as independent predictors of SSI in patients undergoing closed patellar fracture surgery. The cut-off value for GLOB to predict SSI was 21.98 g/L, and for preoperative stay, it was 8 days. High ALB levels were found to be protective against SSI. These findings underscore the importance of routine biomarker assessment and timely surgical intervention. Further studies with larger sample sizes are needed to explore additional factors, such as operating time, influencing SSI rates.

Author contributions

SY, ZPL and EDZ were responsible for study concept and writing the article. YRL,YBL and JQZ were responsible for screened the abstracts and reviewed the article. FW,LL,TW and ZYH were responsible for reviewing and writing the article.

Funding

The research was supported by the Natural Science Foundation of Hebei (H2022104011, H2020206193 and H2021206054); the Science and Technology Project and Intellectual Property Bureau of Baoding City, China (2041ZF260); the Natural Science Foundation of Hebei (H2024206022) The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data and materials availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Email: drzyhou@hebmu.edu.cn.

Code availability

Not Applicable.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This retrospective study was approved by the Institutional Review Board of the 3rd Hospital of Hebei Medical University and Baoding No.1 Central Hospital before collecting data. Due to the retrospective nature of the study, the Institutional Review Board of the 3rd Hospital of Hebei Medical University and Baoding No.1 Central Hospital waived the need of obtaining informed consent.

Consent for publication

Not applicable.