Abstract
The aim of the study was to investigate the epidemiologic characteristics of surgical site infection (SSI) following surgeries of ankle fractures. This was a retrospective study. Patients who underwent surgeries for ankle fractures in our hospital between January 2016 and June 2019 were included. Inpatient medical records were inquired for data collection, including demographics, comorbidities, injury‐related data, laboratory biomarkers, and confirmation of the SSI cases. Univariate analyses and multivariate logistic regression analyses were used to identify the independent risk factors. Among the 1532 patients, 45 had a postoperative SSI, indicating the incidence rate of 2.9%. About 18% of SSIs were identified after discharge. Twenty percent of SSIs were caused by mixed bacteria, and 39% were caused by drug‐resistant bacteria. In the final multivariate model, 7 factors including 5 biomarkers were identified to be independently associated with SSI: gender (male vs female, OR, 2.69; 95% CI, 1.33‐4.76), perioperative blood transfusion (OR. 3.02; 95% CI, 1.30‐7.04), albumin <35 g/L (OR, 2.87; 95% CI, 1.31‐6.31), lower high‐density lipoprotein cholesterol (HDL‐C) (OR, 2.34; 95% CI, 1.19‐4.60), haemoglobin (OR, 2.16; 95% CI, 1.03‐4.67), elevated alanine aminotransferase (OR, 2.09; 95% CI, 1.10‐3.95) and neutrophile/lymphocyte rate (NLR, OR, 3.45; 95% CI, 1.33‐6.74). These epidemiologic data on SSI may help counsel patients about the risk of SSI, individualised assessment of the risk factors, and accordingly the risk stratification.
Keywords: ankle fracture, biomarker, epidemiology, surgical site infection
1. INTRODUCTION
Ankle fracture is a most common fracture in department of emergency and orthopaedics, representing 7.6% of all adult fractures, 1 with an incidence of 37 to 108 per 100 000 person‐years.2, 3, 4 Because of its special anatomic features, most of ankle fractures involve the articular surface and 50% to 80% of them require surgical interventions.1, 4 However, even promptly surgically treated, a certain proportion of patients would be compromised by the unfavourable outcome, and surgical site infection (SSI) was an important cause. 5 It was reported that 24.1% of the unplanned readmissions were related to SSI. 6 In addition, SSI significantly prolonged the hospitalisation stay by 6 to 12 days and increased the medical care costs. 7 Therefore, understanding of epidemiologic characteristics about SSI especially identification of the related risk factors is of importance in individualised assessment of infection risk and accordingly the targeted prevention.
By far, numerous factors associated with SSI following surgeries of ankle fractures have been identified, which involved demographics, injury, comorbidities, surgery, and the postoperative management. However, very few studies focused on the preoperative biomarkers. One major reason for this phenomenon might be the great variability of biomarkers, especially after the trauma. 8 In fact, the role of these biomarkers in predicting postoperative complications including SSI was underestimated. Hypoalbuminemia was a well‐known serum marker that is related to numerous postoperative complications. 9 Hyperglycaemia was demonstrated to be independently associated with postoperative 31‐day SSI, even in a subgroup of orthopaedic trauma patients without a history of diabetes. 10 In recent years, neutrophil‐to‐lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) have been increasingly emphasised and were showed to be predictive for mortality, 11 infectious events, 12 knee osteoarthritis severity, 13 and deep venous thrombosis. 14
Review of the literature showed very few data about biomarkers with postoperative SSI following orthopaedic surgeries. As a simple and inexpensive measurement, biomarkers should be further explored, especially their predictive value in the postoperative complications. In this study, our aim was to evaluate the incidence of SSI following surgeries of ankle fractures and identify the associated risk factors. We hypothesise that some preoperative biomarkers are independently associated with the postoperative SSI.
2. METHODS
The method of this study was performed in accordance with the STROCSS (Strengthening the Reporting of Cohort Studies in Surgery) guidelines. It was designed as a retrospective study and approved by the ethics committee of the 3rd Hospital of Hebei Medical University, which waived the requirement for informed consent because of the anonymous nature of the data.
3. INCLUSION AND EXCLUSION CRITERIA
Between January 2016 and June 2019, patients who underwent surgeries of ankle fractures in our hospital were included. The relevant baseline characteristics were obtained by inquiring patient's hospitalisation medical records. Inclusion criteria were adult patients (18 years or older), surgical treatment for ankle fracture, and complete data available in medical records. Exclusion criteria were pathological (metastatic) or old fracture (> 3 weeks since occurrence), open fracture, concurrent fractures in other locations, and incomplete medical records.
4. DIAGNOSIS OF SSI
SSI was diagnosed in consistency with the criteria proposed by Center for Disease Control (CDC), 15 as an infection that occurred within postoperative 1 year if implantation was in place. A deep SSI was classified if an infection met at least one of the following: involving deep soft tissue, muscle or fascia; wound discharge or dehiscence; visible abscess or gangrenosis that required surgical debridement or even hardware removal. Superficial SSI was defined as redness, swelling, hot, pain related to the surgical site, responsive well to antibiotic treatment, but not meeting the criteria for diagnosis of deep SSI.
SSI was determined based on the description of signs and symptoms from patients' index hospitalisation EMRs for ankle fracture surgery, bacteria culture results, and antibiotic sensitivity from microbiological records. We also inquired the rehospitalisation EMRs for potential SSI occurrence.
5. DATA COLLECTION
Medical records, picture archiving and communication system (PACS), and operation reports were inquired for relevant data. The demographic data included age, gender, residence (urban or rural), body mass index (BMI), cigarette smoking, and alcohol consumption. The comorbidities included hypertension, diabetes, chronic heart disease, and previous history of any surgery, all of which were self‐reported by patients. Injury‐related data included fracture location (uni, bi‐, or trimalleolar fracture), injury mechanism (low‐ or high‐energy trauma), accompanied dislocation, and American Society of Anesthesiologists (ASA) classification. Low‐energy injury was defined as an injury caused by a fall from a standing height, and others such as fall from a height and motor accidents were high‐energy injury.
The biomarkers from preoperative laboratory tests included total protein (TP) level, albumin (ALB) level, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALT), sodium ions (Na+), total bilirubin (TBIL), indirect bilirubin (IBIL), direct bilirubin (DBIL), fasting blood glucose (FBG) level, preoperative red blood cell (RBC) count, white blood cell (WBC) count, neutrophile (NEUT) count, lymphocyte (LYM) count, neutrophile/lymphocyte rate (NLR), haemoglobin (HGB) level, haematocrit (HCT), platelet (PLT), platelet/lymphocyte rate (PLR), red blood cell distribution width (RDW), serum total cholesterol (TC) level, triglyceride (TG) level, low‐density lipoprotein (LDL‐C) level, high‐density lipoprotein (HDL‐C) level, very low‐density lipoprotein (VLDL) level, lactate dehydrogenase (LDH), hydroxybutyrate dehydrogenase (HBDH), uric acid (UA), urea, and D‐dimer level. If patients had multiple laboratory tests before operation, laboratory tests closest to the operation were chosen for data analysis.
6. STATISTICAL ANALYSIS
Continuous variables were expressed by mean and SD and were evaluated by Student's t test or Mann Whitney U test when appropriate. The categorical data were expressed as number and percentage (%) and were evaluated by chi‐square or Fisher's exact test. As for NLR and PLR that no normal reference ranges have been established in the literature or guidelines, the relevant data were used to construct a receiver operating characteristic (ROC) curve to determine a cut‐off value, at which the specificity plus sensitivity is the maximum and above which the risk of SSI was significantly increased. Area under the curve (AUC) was calculated, with P < .05 considered statistically significant.
Multivariate logistics regression model was used to identify the independent risk factors associated with SSI, using the stepwise backward elimination method. Variables with P < .10 were retained in the final model, and the correlation strength is indicated by odd ratio (OR) and 95% confidence interval (95% CI).The statistical test level was set as P < .05. Hosmer‐Lemeshow (H‐L) test was used to evaluate the fitness of the final model, and P > .05 represented the acceptable result. SPSS23.0 was used to perform all the tests (IBM, Armonk, New York).
7. RESULTS
A total of 1532 patients with 1555 ankle fracture treated surgically were included for data analysis, consisting of 888 males and 644 females, with an average of 43.0 years (SD, 15.1; range, 18‐95; median, 43.0). Unimalleolar fracture accounted for 45.8% (712/1555), followed by trimalleolar fractures (496, 31.9%) and bimalleolar fractures (347, 22.3%). There were 284 (18.3%) fractures that were accompanied by dislocation or subluxation. The total hospitalisation stay was 16.6 days in average (SD, 14.0; median, 14; range, 2‐341).
Of the 1532 patients, 45 had a postoperative SSI and the incidence rate was 2.9%, with 0.8% (12/1532) for deep and 2.1% (33/1532) for superficial SSI. Thirty‐seven (82.2%) cases were identified during the hospitalisation stay for ankle surgery, and 8 (17.8%) were identified after discharge. Swabs or purulent secretion culture showed positive result in 41/45 patients, with mixed bacterial infection in 8 (19.5%) and single bacterial infection in 33 (80.5%). In SSIs caused by single bacteria, Staphylococcus aureus was the most common bacteria (8, 19.5%), followed by Pseudomonas aeruginosa (6, 14.6%), Enterobacter cloacae (4, 9.8%), and Escherichia coli (3, 7.3%), which were detailed in Table 1. The drug susceptibility results showed Methicillin‐Resistant Staphylococcus aureus (MRSA) in 7 cases, Methicillin‐Resistant Coagulase‐Negative Staphylococci (MRCNS) in 4, extended‐spectrum beta‐lactamase (ESBL) of Escherichia coli in 3, and Carbapenem‐resistant acinetobacter baumannii (CR‐AB) in 1. The interval between surgery and diagnosis of SSI was at median of 6.5 days, ranging from 1 to 129 days. The total hospital stay for the index ankle surgery was 43.3 (SD, 28.3) days in patients with SSI, significantly more than that (15.8 ± 13.3) in those without SSI (P < .001).
TABLE 1.
Frequency of causative bacterial
| Bacteria | Frequency |
|---|---|
| Single‐bacteria causing SSI | 33 |
| Staphylococcus aureus | 8 |
| Pseudomonas aeruginosa | 6 |
| Enterobacter cloacae | 4 |
| Escherichia coli | 3 |
| Acinetobacter baumannii | 3 |
| Staphylococcus epidermidis | 2 |
| Proteus mirabilis | 2 |
| Klebsiella pneumoniae pneumoniae | 2 |
| Streptococcus pyogenes | 1 |
| Coagulase‐negative staphylococcus | 1 |
| Enterobacter cinerum | 1 |
| Mixed‐bacteria causing SSI | 8 |
| S aureus + Coagulase‐negative staphylococcus | 2 |
| S aureus + E cloacae | 1 |
| S aureus + Alcaligenes xylosoxidans | 1 |
| S aureus + S epidermidis + Acinetobacter baumannii | 1 |
| S epidermidis+ | 1 |
| E cloacae + Citrobacter freundii + P aeruginosa | 1 |
| Coagulase‐negative staphylococcus + E cloacae | 1 |
Abbreviation: SSI, surgical site infection.
The analyses of ROC curve showed the infection point corresponded to the NLR and PLR was 7.8 and 200, and the area under the curve (AUC) was 0.58 (95% CI, 0.48‐0.67; P = .035) and 0.61 (95% CI, 0.42‐0.78, P = .047), respectively. It was significantly different between SSI and non‐SSI in terms of gender, age, living residence, BMI in categorical form, history of any surgery, fracture location, intraoperative bleeding, and perioperative blood transfusion (P < .05). As for biomarkers, there were 15 significant variables, including TP, ALB, TC, HDH‐C, ALT, AST, TBIL, and NLR in continuous and categorical form, PLR in continuous and categorical form, LDH, HBDH, RBC, HGB, and HCT (P < .05) (Table 2).
TABLE 2.
Univariate analyses of risk factors associated with SSI following ankle fracture surgeries
| Variables | Number (%) of DVT (n = 45) | Number (%) of non‐DVT (n = 1487) | P |
|---|---|---|---|
| Gender (male) | 37 (82.2) | 851 (57.2) | .001 |
| Age (years) | 55.6 ± 14.6 | 43.4 ± 15.0 | <.001 |
| 18–44 | 22 (48.9) | 783 (52.7) | .881 |
| 45–64 | 18 (40.1) | 555 (37.3) | |
| 65 or older | 5 (11.1) | 149 (10.0) | |
| Residence | .002 | ||
| Rural | 35 (77.8) | 815 (54.8) | |
| Urban | 10 (22.2) | 672 (45.2) | |
| BMI (kg/m2) | 26.4 ± 3.3 | 25.9 ± 4.1 | .573 |
| 18.5–23.9 | 7 (15.6) | 31 (2.1) | <.001 |
| <18.5 | 0 | 441 (29.7) | |
| 24.0–27.9 | 21 (46.7) | 606 (40.8) | |
| ≥28.0 | 17 (37.8) | 379 (25.5) | |
| Cigarette smoking | 10 (22.2) | 236 (15.9) | .253 |
| Alcohol consumption | 14 (31.1) | 395 (26.7) | .497 |
| Diabetes mellitus | 9 (20.0) | 196 (13.2) | .186 |
| Hypertension | 6 (13.3) | 202 (13.6) | .961 |
| Chronic heart disease | 3 (6.7) | 67 (4.5) | .494 |
| Cerebrovascular disease | 0 | 28 (1.9) | .353 |
| Chronic liver disease | 2 (4.4) | 33 (2.2) | .325 |
| History of allergies to any medications | 7 (15.6) | 215 (14.5) | .837 |
| History of any surgery | 8 (17.8) | 126 (8.5) | .030 |
| Injury mechanism (high energy) | 15 (33.3) | 375 (25.2) | .218 |
| Fracture location | .010 | ||
| Unimalleolar | 28 (62.2) | 674 (45.3) | |
| Bimalleolar | 12 (26.7) | 332 (22.3) | |
| Trimalleolar | 5 (11.5) | 481 (32.3) | |
| Accompanied dislocation or subluxation | 7 (15.6) | 275 (18.5) | .616 |
| Preoperative stay | 6.6 ± 6.5 | 5.7 ± 3.9 | .151 |
| Total hospital stay | 37.4 ± 28.2 | 15.8 ± 13.3 | <.001 |
| Anaesthesia (general) | 11 (24.4) | 314 (21.1) | .591 |
| ASA class | .268 | ||
| I | 5 (11.1) | 258 (17.4) | |
| II | 32 (71.1) | 1062 (71.4) | |
| III or above | 8 (17.8) | 167 (11.2) | |
| Operation timing | .092 | ||
| Day | 42 (93.3) | 1449 (97.4) | |
| Night | 3 (6.7) | 38 (2.6) | |
| Type of fracture reduction | .780 | ||
| Closed | 4 (8.9) | 41 (2.8) | |
| Open | 41 (91.1) | 1420 (97.2) | |
| Fixation type | .298 | ||
| Screw or pins only | 10 (22.2) | 437 (29.4) | |
| Plate and screws | 35 (77.8) | 1050 (70.6) | |
| Bone grafting | 0 | 58 (3.9) | .177 |
| Intraoperative bleeding | 338.2 ± 396.4 | 170.2 ± 211.0 | <.001 |
| Perioperative blood transfusion (yes) | 10 (22.2) | 49 (3.3) | <.001 |
| Surgical duration | 134.9 ± 74.4 | 130.7 ± 58.2 | .664 |
| Intraoperative prophylactic use of antibiotics | 43 (95.2) | 1416 (95.2) | 1.000 |
| Postoperative use of antibiotics | 41 (91.1) | 1386 (93.2) | .583 |
| TP (<60 g/L) | 23 (51.1) | 264 (17.8) | <.001 |
| ALB (<35 g/L) | 20 (44.4) | 185 (12.4) | <0.001 |
| FBG (>6.1 mmol/L) | 14 (31.1) | 433 (29.1) | .772 |
| UA (> upper limit) | 3 (6.7) | 186 (12.5) | .240 |
| NA+(mmol/L) | .163 | ||
| 135–145 | 36 (80.0) | 1324 (89.0) | |
| <135 | 7 (15.6) | 123 (8.3) | |
| >145 | 2 (4.4) | 40 (2.7) | |
| TC (>5.2 mmol/L) | 1 (2.2) | 251 (16.9) | .009 |
| TG (>1.7 mmol/L) | 10 (22.2) | 278 (18.7) | .551 |
| LDL‐C (>3.37 mmol/L) | 3 (6.7) | 265 (17.8) | .052 |
| HDL‐C (<1.1 mmol/L) | 31 (68.9) | 528 (35.3) | <.001 |
| VLDL (>0.78 mmol/L) | 10 (22.2) | 270 (18.2) | .487 |
| ALT | 23 (51.1) | 324 (21.8) | <.001 |
| AST | 10 (22.2) | 226 (15.2) | .198 |
| TBIL | 7 (15.6) | 101 (6.8) | .024 |
| DBIL | 15 (33.3) | 340 (22.9) | .101 |
| IBIL | 7 (15.6) | 234 (15.7) | .974 |
| ALP | 4 (8.9) | 53 (3.6) | .063 |
| NLR | 6.5 ± 5.9 | 4.6 ± 3.1 | <.001 |
| ≥7.8 | 15 (33.3) | 166 (11.2) | <.001 |
| PLR | 198.9 ± 130.5 | 170.4 ± 85.0 | .028 |
| ≥200 | 18 (40.0) | 388 (26.1) | .037 |
| UA (>upper limit) | 3 (6.7) | 186 (12.5) | .240 |
| LDH | 15 (33.3) | 205 (13.8) | <.001 |
| HBDH | 11 (24.4) | 180 (12.1) | .014 |
| UREA | 1 (2.2) | 40 (2.7) | .848 |
| WBC (>10 × 109/L) | 17 (37.8) | 373 (25.8) | .070 |
| NEUT (>6.3 × 109/L) | 24 (53.3) | 632 (42.5) | .148 |
| LYM (<1.1 × 109/L) | 12 (26.7) | 311 (20.9) | .351 |
| RBC (<lower limit) | 24 (53.3) | 295 (19.8) | <.001 |
| HGB (<lower limit) | 24 (53.3) | 289 (19.4) | <.001 |
| HCT (<lower limit) | 30 (66.7) | 604 (40.6) | <.001 |
| PLT (>300 × 109/L) | 13 (28.9) | 269 (18.1) | .066 |
| PDW (>18.1%) | 6 (13.3) | 141 (9.5) | .387 |
| RDW (>16.5%) | 4 (8.9) | 55 (3.7) | .075 |
| d‐Dimer (>0.3 mg/L) | 21 (46.7) | 902 (60.7) | .059 |
Note: RBC: reference range: female, 3.5‐5.0 × 1012/L; males, 4.0‐5.5 × 1012/L; HGB: reference range: females, 110‐150 g/L; males, 120‐160 g/L; UA: males, 208‐408 umol/L, females, 155‐357 μmol/L; HCT: 40% to 50%; PLT: 100‐300 × 109/L.
Abbreviations: ALB, albumin; ASA, American Society of Anesthesiologists; BMI, body mass index; FBG, fasting blood glucose; HCT, haematocrit; HDL‐C, high density lipoprotein; HGB, haemoglobin; LDL‐C, low density lipoprotein; LYM, lymphocyte; NEUT, neutrophile; PDW, platelet distribution width; PLT, platelet; RBC, red blood cell; RDW, red cell distribution width; TC, total cholesterol; TG, triglyceride; TP, total protein; UA, uric acid; VLDL, very low‐density lipoprotein; WBC, white blood cell.
In the multivariate model, the abovementioned 23 variables together with operation timing (P = .092), LDL‐C (P = .052), ALP (P = .063), WBC (P = .070), PLT (P = .066), RDW (P = .075) and d‐dimer (P = .059) were entered. In the final model, 7 factors including 5 biomarkers were identified to be independently associated with SSI: gender, perioperative blood transfusion, ALB, HDL‐C, HGB, ALT, and elevated NLR (Table 3 ). The H‐L test demonstrated the adequate fitness of the final model (X 2 = 3.817, P = .873; Nagelkerke R 2 = 0.219).
TABLE 3.
Multivariate analyses of risk factors associated with SSI following ankle fracture surgeries
| Variable | OR and 95% CI | P value |
|---|---|---|
| Gender (male vs female) | 2.69 (1.33‐4.76) | .021 |
| Living area (rural vs urban) | 1.94 (0.92‐4.26) | .067 |
| Perioperative blood transfusion | 3.02 (1.30‐7.04) | .010 |
| ALB < 35 g/L | 2.87 (1.31‐6.31) | .009 |
| HDL‐C (<1.1 mmol/L) | 2.34 (1.19‐4.60) | .013 |
| HGB < lower limit | 2.16 (1.03‐4.67) | .042 |
| ALT > 40 U/L | 2.09 (1.10‐3.95) | .024 |
| NLR ≥ 7.8 | 3.45 (1.33‐6.74) | .008 |
Abbreviations: ALB, albumin; ALT, alanine aminotransferase; CI, confidence interval; HDL‐C, high‐density lipoproteincholesterol; HGB, haemoglobin; NLR, neutrophile/lymphocyte rate; OR, odd ratio; SSI, surgical site infection.
8. DISCUSSION
As a simple and inexpensive measurement index, preoperative biomarkers might have special value in predicting a certain complication, but relevant data on SSI following surgeries of ankle fracture are still scarce. In this study, we found the incidence rate of SSI was 2.9% and identified 7 independent associated factors, including 5 preoperative biomarkers. This tested our previous hypothesis.
Due to study design and heterogeneity of included patients among studies, greatly variable incidence rates were reported, 1.1% to 10% for deep SSI and 2.6% to 14.0% for superficial SSI.16, 17 Meng et al 7 reported the incidence rate of deep SSI was 2.83% in 2617 patients with ankle fracture treated by open reduction and internal fixation (ORIF). Shao et al 18 pooled the data in a meta‐analysis of 8103 ankle fractures by ORIF and found the overall rate of SSI was 7.2%. In contrast, this study included ankle fractures reduced by either open or closed manner, and reported a comparable incidence of 2.9%. Schepers et al 19 demonstrated the significant effect of ankle fracture surgery on the occurrence of postoperative infection, but not found in this study.
It was of note that types of pathogenic bacteria causing SSI were greatly variable among studies. In the study by Meng et al, 7 no mixed bacteria‐causative SSIs were reported and S aureus caused 39% of SSIs. In another study, the authors found 36% (9/25) of the SSIs were caused by mixed bacteria and 48% (12/25) were caused by S aureus. 16 In a study of spinal surgeries, the authors found that the 47.1% of the SSIs were caused by S aureus, but no mixed bacteria‐causative SSIs was found. 20 By contrast, in our study, 14 types of pathogenic bacteria were involved, with a remarkable discrete distribution, from 3% to 24.2% in proportion. Another concern was the larger proportion (39.0%) of SSIs caused by the drug‐resistant bacteria, about twofold as that in a previous study. 21 Postoperative overuse of antibiotics might be a notable cause, and in our study 93.1% of patients were prescribed postoperative antibiotics.
The gender difference in risk of infection issues has been little consistent across orthopaedic studies, and almost all the studies specified at ankle fractures did not identify the independent association with postoperative SSI.2, 7, 16, 18, 22, 23 In this study, we found the 2.69‐fold increased risk of SSI in men. The precise mechanism remains unclear, but might be related to differences in skin colonisation or anatomy between men and women. 24 It was suggested in men who had thicker and coarser hair, the risk of infection was higher because hair growth and shaving interfere with wound dressing adherence. 25 However, some confounding factors that were more related to male gender could not be excluded such as lifestyles, fracture severity, and soft tissue damage, which require more studies to explore.
Perioperative blood transfusion has been demonstrated to increase the risk of infection issues in both orthopaedics and other subspecialty surgeries,26, 27 similar to as the preoperative anaemia status. 28 The poorer immunologic status associated with blood transfusion or anaemia status may be an important cause. In a previous study, authors found patients who received any allogeneic blood had a fourfold greater drop in nature killer cell at day 7, and also found the dose‐dependent effect on SSI. 29 Dix et al 30 demonstrated the association of preoperative anaemia with infection and the prolonged hospital stay and also the bone union issues in hindfoot and ankle arthrodesis. We suggest that blood transfusion before ankle fracture surgery repair in those with anaemia may be beneficial, but it is more reasonable to determine which population would benefit most from the blood transfusion.
The lower ALB and HDL‐C levels, elevated ALT level, and NLR were identified to be associated with increased risk of SSI, which had been extensively discussed in the studies of orthopaedics.9, 31, 32 Abnormity of these biomarkers is more likely indication of a poorer nutritional, lower immune, and systematic inflammation status, but the stress effects from trauma could not be ruled out.33, 34 NLR as an inexpensive and readily obtained immune/inflammatory biomarker‐derived index has been repeatedly discussed and demonstrated to be predictive of adverse affairs and even mortality, in cancer, 35 trauma, 31 cardiovascular diseases, 36 or autoimmune diseases. 37 In elective lumbar spinal surgery, NLR was also demonstrated to be predictive of postoperative SSI. 38 In fact, neutrophilia can be a manifestation of infection‐induced inflammatory, and lymphocytes play an important role in the anti‐infection immune response. Accordingly, the NLR, which was calculated by the neutrophils divided by lymphocytes, might combine the level of inflammatory and anti‐infection reaction. So, in this study, NLR rather than the count of neutrophils or lymphocytes was identified to be independently associated with SSI. Another point should be noted that after traumatic fractures, these biomarkers have been consistently changing and each one presented the unique feature, 8 which deserved to be well defined on the basis of time since trauma. In our study, if one patient had multiple laboratory tests before the operation, the laboratory test closest to the operation was chosen for data analysis. Therefore, in consideration of the changes of biomarkers after trauma, our results might be of more value in predicting SSI occurrence, compared with the commonly used meaurement of biomarkers at admission.
There were several limitations of this study. Firstly, the retrospective design of this study might compromise the accuracy in data collection. Secondly, as every multivariate analysis, some potential factors that affect the occurrence of SSI could not be evaluated, such as chronic use of glucocorticoids and haemoglobin a1c level. Thirdly, the overall incidence rate of SSI was underestimated, because some patients who had an SSI but went to other hospitals for treatment were deemed to have no SSI. About 18% (275) of patients in this study were followed less than 1 year (4‐11 months), which might have somewhat but little influence on the incidence rate of SSI, because the maximum interval between operation and SSI was 129 days.
In summary, we found the incidence rate of SSI following ankle fracture surgery was 2.9%. About 20% of the SSI were caused by mixed bacteria and 39% were drug resistant, representing a serious situation during treatment. Seven factors including multiple biomarkers were identified to be associated with SSI: gender (male vs female), perioperative blood transfusion, lower level of ALB, HDL‐C and HGB, elevated ALT, and NLR. These risk factors, although not easily modifiable, may help counsel patients about the risk of SSI, individualised assessment, and accordingly the risk stratification.
CONFLICT OF INTEREST
All the authors declared no potential conflict of interest.
AUTHOR CONTRIBUTIONS
Yingze Zhang conceived the idea for the study; Yanbin Zhu designed the study. Wei Chen performed the statistical analyses. Song Liu and Ming Li prepared the figures and tables. All the authors interpreted the data and contributed to preparation of the manuscript. Dawei Liu, Yanbin Zhu, and Wei Chen prepared the manuscript and Dawei Liu revised the manuscript.
ACKNOWLEDGEMENTS
We are grateful to L. Lin and X. Zhang of the Department of Orthopedics, and to K. Liu and B. Zhang of the Department of statistics and applications for their kind assistance.
Liu D, Zhu Y, Chen W, Li M, Liu S, Zhang Y. Multiple preoperative biomarkers are associated with incidence of surgical site infection following surgeries of ankle fractures. Int Wound J. 2020;17:842–850. 10.1111/iwj.13351
Dawei Liu and Yanbin Zhu contributed equally to this study.
Funding information Hebei Provincial Department of Human Resources and Social Security Funding, Grant/Award Number: NO A201903007
REFERENCES
- 1. Zhang Y. Clinical epidemiology of orthopaedic trauma. New York, NY: Thieme; 2012. [Google Scholar]
- 2. Nåsell H, Ottosson C, Törnqvist H, Lindé J, Ponzer S. The impact of smoking on complications after operatively treated ankle fractures—a follow‐up study of 906 patients. J Orthop Trauma. 2011;25(12):748‐755. [DOI] [PubMed] [Google Scholar]
- 3. Liu S, Zhu Y, Chen W, Wang L, Zhang X, Zhang Y. Demographic and socioeconomic factors influencing the incidence of ankle fractures, a national population‐based survey of 512187 individuals. Sci Rep. 2018;8(1):10443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Jensen SL, Andresen BK, Mencke S, Nielsen PT. Epidemiology of ankle fractures: a prospective population‐based study of 212 cases in Aalborg, Denmark. Acta Orthop Scand. 1998;69(1):48‐50. [DOI] [PubMed] [Google Scholar]
- 5. Naumann MG, Sigurdsen U, Utvåg SE, Stavem K. Functional outcomes following surgical‐site infections after operative fixation of closed ankle fractures. Foot Ankle Surg. 2016;23(4):311‐316. [DOI] [PubMed] [Google Scholar]
- 6. Fu MC, Young K, Cody E, Schairer WW. Most readmissions following ankle fracture surgery are unrelated to surgical site issues: an analysis of 5056 cases. Foot Ankle Orthop. 2017;2(2):1–6. 10.1177/2473011417695254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Meng J, Sun T, Zhang F, Qin S, Li Y, Zhao H. Deep surgical site infection after ankle fractures treated by open reduction and internal fixation in adults: a retrospective case‐control study. Int Wound J. 2018;15(6):971‐977. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Alexandru L, Haragus H, Deleanu B, Timar B, Poenaru DV, Vlad DC. Haematology panel biomarkers for humeral, femoral, and tibial diaphyseal fractures. Int Orthop. 2019;43(7):1567‐1572. [DOI] [PubMed] [Google Scholar]
- 9. Yuwen P, Chen W, Lv H, et al. Albumin and surgical site infection risk in orthopaedics: a meta‐analysis. BMC Surg. 2017;17(1):7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Richards JE, Kauffmann RM, Zuckerman SL, Obremskey WT, May AK. Relationship of hyperglycemia and surgical‐site infection in orthopaedic surgery. JBJS. 2012;94(13):1181‐1186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Karhade AV, Shah KC, Shah AA, Ogink PT, Nelson SB, Schwab JH. Neutrophil to lymphocyte ratio and mortality in spinal epidural abscess. Spine J. 2019;19:1180‐1185. [DOI] [PubMed] [Google Scholar]
- 12. Qu J, Yuan HY, Huang Y, et al. Evaluation of neutrophil–lymphocyte ratio in predicting bloodstream infection. Biomark Med. 2019;13(15):1255‐1261. [DOI] [PubMed] [Google Scholar]
- 13. Taşoğlu Ö, Bölük H, Şahin Onat Ş, Taşoğlu İ, Özgirgin N. Is blood neutrophil‐lymphocyte ratio an independent predictor of knee osteoarthritis severity? Clin Rheumatol. 2016;35(6):1579‐1583. [DOI] [PubMed] [Google Scholar]
- 14. Barker T, Rogers VE, Henriksen VT, et al. Is there a link between the neutrophil‐to‐lymphocyte ratio and venous thromboembolic events after knee arthroplasty? A pilot study. J Orthop Traumatol. 2016;17(2):163‐168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Berríos‐Torres SI et al. Centers for Disease Control and Prevention guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017;152(8):784–791. [DOI] [PubMed] [Google Scholar]
- 16. Sun R, Li M, Wang X, et al. Surgical site infection following open reduction and internal fixation of a closed ankle fractures: a retrospective multicenter cohort study. 2018;49:60‐61. [DOI] [PubMed] [Google Scholar]
- 17. Thangarajah T, Prasad PSV, Narayan B. Surgical site infections following open reduction and internal fixation of ankle fractures. Open Orthop J. 2009;3(1):56‐60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Shao J, Zhang H, Yin B, Li J, Zhu Y, Zhang Y. Risk factors for surgical site infection following operative treatment of ankle fractures: a systematic review and meta‐analysis. Int J Surg. 2018;56:124–132. [DOI] [PubMed] [Google Scholar]
- 19. Schepers T, de Vries MR, van Lieshout EMM, van der Elst M. The timing of ankle fracture surgery and the effect on infectious complications; a case series and systematic review of the literature. Int Orthop. 2013;37(3):489‐494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Wimmer C, Gluch H, Franzreb M, Ogon M. Predisposing factors for infection in spine surgery: a survey of 850 spinal procedures. Clin Spine Surg. 1998;11(2):124‐128. [PubMed] [Google Scholar]
- 21.曹金林, et al., 四肢骨折患者手术部位多药耐药菌感染状况研究. 中华医院感染学杂志. 28(23): p. 102–104+144. [Google Scholar]
- 22. Sun Y, Wang H, Tang Y, et al. Incidence and risk factors for surgical site infection after open reduction and internal fixation of ankle fracture: a retrospective multicenter study. Medicine. 2018;97(7):9901–9906. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Ovaska MT, Mäkinen TJ, Madanat R, et al. Risk factors for deep surgical site infection following operative treatment of ankle fractures. JBJS. 2013;95(4):348‐353. [DOI] [PubMed] [Google Scholar]
- 24. Cohen B, Choi YJ, Hyman S, Furuya EY, Neidell M, Larson E. Gender differences in risk of bloodstream and surgical site infections. J Gen Intern Med. 2013;28(10):1318‐1325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Luft D, Schmoor C, Wilson C, et al. Central venous catheter‐associated bloodstream infection and colonisation of insertion site and catheter tip. What are the rates and risk factors in haematology patients? Ann Hematol. 2010;89(12):1265‐1275. [DOI] [PubMed] [Google Scholar]
- 26. Carson JL, Altman DG, Duff A, et al. Risk of bacterial infection associated with allogeneic blood transfusion among patients undergoing hip fracture repair. Transfusion. 1999;39(7):694‐700. [DOI] [PubMed] [Google Scholar]
- 27. Johnston P, Wynn‐Jones H, Chakravarty D, Boyle A, Parker MJ. Is perioperative blood transfusion a risk factor for mortality or infection after hip fracture? J Orthop Trauma. 2006;20(10):675‐679. [DOI] [PubMed] [Google Scholar]
- 28. Dunne JR, Malone D, Tracy JK, Gannon C, Napolitano LM. Perioperative anemia: an independent risk factor for infection, mortality, and resource utilization in surgery. J Surg Res. 2002;102(2):237‐244. [DOI] [PubMed] [Google Scholar]
- 29. Triulzi DJ, Vanek K, Ryan DH, Blumberg N. A clinical and immunologic study of blood transfusion and postoperative bacterial infection in spinal surgery. Transfusion. 1992;32(6):517‐524. [DOI] [PubMed] [Google Scholar]
- 30. Dix B, Grant‐McDonald L, Catanzariti A, Saltrick K. Preoperative anemia in hindfoot and ankle arthrodesis. Foot Ankle Spec. 2017;10(2):109‐115. [DOI] [PubMed] [Google Scholar]
- 31. Forget P, Moreau N, Engel H, et al. The neutrophil‐to‐lymphocyte ratio (NLR) after surgery for hip fracture (HF). Arch Gerontol Geriatr. 2015;60(2):366‐371. [DOI] [PubMed] [Google Scholar]
- 32. Yombi JC, Schwab P‐E, Thienpont E. Neutrophil‐to‐lymphocyte ratio (NLR) distribution shows a better kinetic pattern than C‐reactive protein distribution for the follow‐up of early inflammation after total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2016;24(10):3287‐3292. [DOI] [PubMed] [Google Scholar]
- 33. Wichmann MW, Ayala A, Chaudry IH. Severe depression of host immune functions following closed‐bone fracture, soft‐tissue trauma, and hemorrhagic shock. Crit Care Med. 1998;26(8):1372‐1378. [DOI] [PubMed] [Google Scholar]
- 34. Cheng J. Study on the clinical value of early liver function detection in patients with multiple fracture trauma and hemorrhagic shock. Chin J Traum Disability Med. 2017;25(3):59‐60. [Google Scholar]
- 35. Panni RZ, Lopez‐Aguiar AG, Liu J, et al. Association of preoperative monocyte‐to‐lymphocyte and neutrophil‐to‐lymphocyte ratio with recurrence‐free and overall survival after resection of pancreatic neuroendocrine tumors (US‐NETSG). J Surg Oncol. 2019;120(4):632‐638. [DOI] [PubMed] [Google Scholar]
- 36. Afari ME, Bhat T. Neutrophil to lymphocyte ratio (NLR) and cardiovascular diseases: an update. Expert Rev Cardiovasc Ther. 2016;14(5):573‐577. [DOI] [PubMed] [Google Scholar]
- 37. Solak B, Dikicier BS, Cosansu NC, et al. Neutrophil to lymphocyte ratio in patients with vitiligo. Postepy Dermatol Alergol. 2017;34(5):468–470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Shen CJ, Miao T, Wang ZF, et al. Predictive value of post‐operative neutrophil/lymphocyte count ratio for surgical site infection in patients following posterior lumbar spinal surgery. Int Immunopharmacol. 2019;74:105705. [DOI] [PubMed] [Google Scholar]