Abstract
Purpose
The predictors of muscle necrosis after acute compartment syndrome (ACS) remain debated. This study aimed to investigate the predictors for muscle necrosis in ACS patients.
Methods
We collected data on ACS patients following fractures from January 2010 to November 2022. Patients were divided into the muscle necrosis group (MG) and the non-muscle necrosis group (NG). The demographics, comorbidities, and admission laboratory indicators were computed by univariate analysis, logistic regression analysis, and receiver-operating characteristic (ROC) curve analysis.
Results
In our study, the rate of MN was 37.6% (83 of 221). Univariate analysis showed that numerous factors were associated with muscle necrosis following ACS. Logistic regression analysis indicated that crush injury (p = 0.007), neutrophil (NEU, p = 0.001), creatine kinase myocardial band (CKMB, p = 0.047), and prothrombin time (PT, p = 0.031) were risk factors. Additionally, ROC curve analysis identified 11.415 109/L, 116.825 U/L, and 12.51 s as the cut-off values for NEU, CKMB, and PT to predict muscle necrosis, respectively. Furthermore, the combination of NEU, CKMB, and PT had the highest diagnostic accuracy.
Conclusions
Our findings showed that crush injury and the level of NEU, CKMB, and PT were risk factors for muscle necrosis after ACS. Additionally, we also identified the cut-off values of NEU, CKMB, and PT and found the combination of crush injury, PT, and NEU with the highest diagnostic accuracy, helping us individualize the assessment risk of muscle necrosis to manage early targeted interventions.
Keywords: Acute compartment syndrome, Muscle necrosis, Predictors
Introduction
Acute compartment syndrome (ACS), one of the serious complications following fractures, impacts 30.4% of patients with shaft and proximal tibial fractures, particularly in those with comminuted fractures or tibial plateau fractures [1–3]. It frequently results in increased intra-compartmental pressure, which drives the decline of tissue perfusion [1–6]. Up until now, it has been challenging for clinicians to promptly diagnose and treat because it is often based on the clinicians’ experience instead of the gold standard tests. Once there is a delay in diagnosis and treatment, it can result in sensory deficits, paralysis, infection, or muscle necrosis [7]. The sequela of muscle necrosis can cause disability by limiting the motion and function of limbs over the long term [6, 8].
Therefore, it is urgent to identify the predictors of muscle necrosis in patients with ACS. Mortensen [9] found that open fracture was the only independent risk factor of muscle necrosis and crush injury and soft tissue injuries relatively increased the risks of necrotic muscle. However, little attention was paid to the role of admission laboratory indicators in the prediction of necrotic muscle. To our knowledge, few studies have reported the predictors of muscle necrosis following ACS. Thus, the objective of this study is to identify the risk factors for muscle necrosis in patients with ACS according to overall data, including demographics, comorbidities, and admission laboratory indicators.
Patients and methods
Ethics approval and consent to participate
This retrospective study was approved by the Institutional Review Board of our hospital (NCT04529330, S2020-022–1) before collecting data. There was no need to obtain informed consent forms from patients because this was a retrospective study.
Patients
A retrospective analysis of all patients with ACS following fractures, including upper and lower limb fractures, between January 2010 and November 2022 in our hospital. Patients with muscle necrosis were classified as the muscle necrosis group (MG), while those without muscle necrosis were classified as the non-muscle necrosis group (NG). The inclusion criteria were as follows: (1) patients with ACS following fractures, (2) adult patients (≥ 18 years old), and (3) no comorbidity was present at the time of admission. The exclusion criteria were as follows: (1) patients with death during hospital stays, (2) patients with vascular injuries, (3) patients with pathological fractures, and (4) incomplete data.
The demographics, comorbidities, and admission laboratory examinations of patients were collected in this study. The demographics data included age, gender, body mass index (< 24, 24–28, and > 28 kg/m2), mechanism of injury (car crash injury, fall injury, crush injury, and hurt by a crashing object), time from injury to admission, injury type (open fractures vs closed fractures), dehydrant (yes vs no), American society of anesthesiologists (ASA, I, II vs III, IV), blisters (yes vs no), multiple fractures (yes vs no), smoking (yes vs no), alcohol (yes vs no), and emergency fasciotomy (within 24 h after injury, yes vs no). Comorbidities consist of patients with a history of arrhythmia, coronary heart disease, hypertension, diabetes, and cerebral infarction. Admission laboratory indicators covered basophil (BAS, 109/L), BAS(%), eosinophil (EOS, 109/L), EOS(%), lymphocyte (LYM, 109/L), LYM(%), monocyte (MON, 109/L), MON(%), neutrophil (NEU, 109/L), NEU(%), neutrophil to lymphocyte ratio (NLR), platelet (PLT, 109/L), white blood cell (WBC, 109/L), red blood cell (RBC, 1012/L), haemoglobin (HGB, g/L), total protein (TP, g/L), albumin (ALB, g/L), globulin (GLOB, g/L), ALB/GLOB, creatine kinase (CK, U/L), creatine kinase myocardial band (CKMB, U/L), Ca2+(mol/L), K+(mol/L), Na+(mol/L), Mg2+(mol/L), P3+(mol/L), lactic dehydrogenase (LDH, U/L), ureophil (UREA, mol/L), total carbon dioxide (TCO2, mol/L), osmotic pressure (OSM, mol/L), prothrombin time (PT, s), international normalized ratio (INR, s), fibrinogen (FIB, g/L), activated partial thromboplastin time (APTT, s), thrombin time (TT, s), D-dimer(mg/L), and antithrombin III (AT III, %).
Statistics
We utilized SPSS (version 25.0 SPSS Inc., Chicago, IL) and regarded p < 0.05 as statistical significance. Regarding continuous variables, if data met normality criteria, all measurement data were presented as the mean ± SD (standard deviation) using the t-test, but if not, the Mann–Whitney U test was used to perform statistical analysis between groups. For count data, the chi-square test was used for data analysis. Furthermore, to identify the best predictors of muscle necrosis, we used binary logistic regression analysis to detect independent predictors of muscle necrosis. Additionally, receiver operator characteristic (ROC) curve analysis was used to identify the cut-off values for continuous variables. The area under the ROC curve (AUC) was used to determine the diagnostic ability, ranging from 0 to 100%, with more area meaning better ability. We choose the cut-off values for continuous variables by the maximum Youden index (sensitivity + specificity-1) in the ROC curve analysis.
Results
From January 2010 to November 2022, a total of 385 consecutive patients presenting with ACS following fractures were screened and assessed for eligibility in this study. Finally, we collected a total of 221 patients (120 lower leg fractures and 101 upper limb fractures) due to the exclusion criteria, including 83 patients with muscle necrosis and 138 patients without muscle necrosis (Fig. 1). The rate of muscle necrosis in patients with ACS was 37.6%. Notably, 78.7% (174 of 221) of patients received emergency fasciotomy (within 24 h after injury) and others underwent fasciotomy 24 h after injury. As present in Table 1, crush injury (p = 0.005), open fracture (p = 0.009), and patients without hypertension (p = 0.045) were found to be associated with the risk of muscle necrosis.
Fig. 1.
Flow diagram of included patients
Table 1.
Characteristic of patients with acute compartment syndrome in two groups
| Characteristics | Necrotic muscle group (n = 83) | Non-necrotic muscle (n = 138) | p value |
|---|---|---|---|
| Age, years | 35.0 (26.0 ~ 48.0) | 30.0 (24.0 ~ 48.0) | 0.418 |
| Gender | 0.07 | ||
| Male | 73 | 108 | |
| Female | 10 | 30 | |
| Body mass index (kg/m2) | 24.3 (21.8 ~ 26.78) | 24.22 (20.2 ~ 25.89) | 0.051 |
| < 24 | 31 | 67 | 0.234 |
| 24–28 | 39 | 56 | |
| > 28 | 13 | 15 | |
| Mechanism of injury | 0.057 | ||
| Car crash injury | 14 | 25 | |
| Fall Injury | 13 | 31 | |
| Crush injury | 31 | 28 | |
| Hurt by a crashing object | 7 | 22 | |
| Unknown trauma | 18 | 32 | |
| Crush injury | 0.005 | ||
| Yes | 31 | 28 | |
| No | 52 | 110 | |
| Time from injury to admission (hours) | 6.0 (3.0 ~ 11.0) | 6.5 (4.0 ~ 13.0) | 0.265 |
| Injury type | 0.009 | ||
| Open fracture | 35 | 35 | |
| Closed fracture | 48 | 103 | |
| Dehydrant | 0.052 | ||
| Yes | 49 | 99 | |
| No | 34 | 39 | |
| American Society of Anesthesiologists | 0.182 | ||
| I, II | 71 | 126 | |
| III, IV | 12 | 12 | |
| Blisters | 0.317 | ||
| Yes | 19 | 24 | |
| No | 64 | 114 | |
| Multiple fractures | 0.652 | ||
| Yes | 27 | 49 | |
| No | 56 | 89 | |
| Smoking | 0.66 | ||
| Yes | 10 | 14 | |
| No | 73 | 124 | |
| Alcohol | 0.529 | ||
| Yes | 8 | 10 | |
| No | 75 | 128 | |
| Emergency fasciotomy (with 24 h after injury) | 0.055 | ||
| Yes | 71 | 103 | |
| No | 12 | 35 | |
| Comorbidities | |||
| Arrhythmia | 0.558 | ||
| Yes | 2 | 1 | |
| No | 81 | 137 | |
| Coronary heart disease | 1.000 | ||
| Yes | 3 | 6 | |
| No | 80 | 132 | |
| Hypertension | 0.045 | ||
| Yes | 2 | 13 | |
| No | 81 | 125 | |
| Diabetes | 0.653 | ||
| Yes | 1 | 4 | |
| No | 82 | 134 | |
| Cerebral Infarction | 0.086 | ||
| Yes | 0 | 6 | |
| No | 83 | 132 | |
Table 2 shows that the levels of NEU (p = 0.001), NEU% (p < 0.0001), NLR (p < 0.0001), WBC (p = 0.02), CKMB (p = 0.001), LDH (p = 0.021), UREA (p = 0.007), OSM (p = 0.001), PT (p = 0.002), and INR (p = 0.004) were significantly higher in MG, but the levels of LYM% (p < 0.0001), CKMB% (p < 0.0001), Ca2+ (p = 0.001), Na+ (p = 0.001), Mg2+ (p < 0.0001), APTT (p = 0.011), D-dimer (p < 0.0001), and AT III (p < 0.0001) were markedly lower in MG. Logistic regression analysis presented that crush injury [p = 0.007, OR = 2.598, 95%CI (1.298, 5.201)] and the level of NEU [p = 0.001, OR = 1.124, 95%CI (1.048, 1.205)], CKMB [p = 0.047, OR = 1.002, 95%CI (1.000, 1.004)], and PT [p = 0.031, OR = 1.992, 95%CI (1.066, 3.720)] were independent risk factors for muscle necrosis (Table 3). However, the level of Mg2+ [p = 0.032, OR = 0.012, 95%CI (0.000, 0.686)] and Na+ [p < 0.0001, OR = 0.741, 95%CI (0.629, 0.874)], as well as hypertension [p = 0.048, OR = 0.188, 95%CI (0.036, 0.986)], was protective factors of muscle necrosis (Table 3).
Table 2.
Laboratory indicators of patients with acute compartment syndrome in two groups
| Laboratory indicators | Necrotic muscle group (n = 83) | Non-necrotic muscle (n = 138) | p value |
|---|---|---|---|
| Basophil (BAS, 109/L) | 0.05 (0.01 ~ 0.09) | 0.04 (0.02 ~ 0.07) | 0.593 |
| Basophil, % | 0.40 (0.10 ~ 0.60) | 0.40 (0.11 ~ 0.60) | 0.374 |
| Eosinophil (EOS,109/L) | 0.13 (0.06 ~ 0.18) | 0.12 (0.07 ~ 0.17) | 0.486 |
| Eosinophil, % | 0.90 (0.7 ~ 1.2) | 0.95 (0.7 ~ 0.11) | 0.598 |
| Lymphocyte (LYM, 109/L) | 1.54 (1.12 ~ 2.12) | 1.72 (1.24 ~ 2.13) | 0.206 |
| Lymphocyte, % | 9.9 (7.0 ~ 13.7) | 14.5 (9.0 ~ 20.3) | < 0.0001 |
| Monocyte (MON, 109/L) | 0.93 (0.6 ~ 1.21) | 0.80 (0.59 ~ 1.04) | 0.111 |
| Monocyte, % | 6.2 (4.8 ~ 8.0) | 6.79 (5.3 ~ 8.0) | 0.194 |
| Neutrophil (NEU, 109/L) | 12.4 (7.9 ~ 16.1) | 9.4 (7.0 ~ 12.9) | 0.001 |
| Neutrophil, % | 81.8 (77.0 ~ 85.8) | 77.3 (71.2 ~ 83.6) | < 0.0001 |
| Neutrophil to lymphocyte ratio (NLR) | 8.47 (5.74 ~ 12.09) | 5.65 (3.49 ~ 9.02) | < 0.0001 |
| Platelet (PLT, 109/L) | 216.3 (165.0 ~ 258.0) | 232.9 (190.5 ~ 278.1) | 0.089 |
| Red blood cell (RBC, 1012/L) | 4.33 (3.52 ~ 4.65) | 4.25 (3.80 ~ 4.71) | 0.608 |
| White blood cell (WBC, 109/L) | 14.9 (11.0 ~ 19.5) | 12.0 (9.4 ~ 15.8) | 0.02 |
| Haemoglobin (HGB, g/L) | 132.7 (112.4 ~ 146.4) | 128.1 (116.8 ~ 141.5) | 0.887 |
| Total protein (TP, g/L) | 60.0 (54.3 ~ 63.4) | 58.9 (57.2 ~ 63.4) | 0.904 |
| Albumin (ALB, g/L) | 37.46 (33.2 ~ 39.8) | 37.29 (36.38 ~ 40.53) | 0.213 |
| Globulin (GLOB, g/L) | 22.56 (20.89 ~ 23.90) | 21.74 (20.89 ~ 23.33) | 0.103 |
| A/G | 1.73 (1.40 ~ 1.78) | 1.74 (1.65 ~ 1.76) | 0.171 |
| Creatine kinase, (CK, U/L) | 3387.7 (777.0 ~ 4164.1) | 3337.4 (518.3 ~ 4166.3) | 0.288 |
| Creatine kinase myocardial band (CKMB, U/L) | 104.4 (32.0 ~ 157.0) | 80.3 (22.0 ~ 97.3) | 0.001 |
| CKMB% | 6.39 (2.94 ~ 6.84) | 7.55 (4.01 ~ 8.07) | < 0.0001 |
| Ca2+ (mol/L) | 2.14 (2.09 ~ 2.16) | 2.16 (2.11 ~ 2.23) | 0.001 |
| K+ (mol/L) | 3.94 (3.69 ~ 4.00) | 3.97 (3.91 ~ 3.99) | 0.140 |
| Na+ (mol/L) | 137.2 (136.0 ~ 138.0) | 137.7 (137.4 ~ 138.1) | 0.001 |
| Mg2+ (mol/L) | 0.82 (0.76 ~ 0.84) | 0.84 (0.83 ~ 0.86) | < 0.0001 |
| P3+(mol/L) | 1.16 (0.94 ~ 1.23) | 1.17 (1.12 ~ 1.19) | 0.188 |
| Lactic dehydrogenase (LDH, U/L) | 722.9 (479 ~ 787) | 649.6 (301.2 ~ 743.6) | 0.021 |
| Ureophil (UREA, mol/L) | 5.43 (4.48 ~ 5.79) | 5.11 (4.62 ~ 5.20) | 0.007 |
| Total carbon dioxide (TCO2, mol/L) | 23.45 (22.13 ~ 24.0) | 23.18 (22.73 ~ 24.1) | 0.564 |
| Osmotic pressure (OSM, umol/L) | 269.9 (261.2 ~ 271.2) | 268.9 (267.8 ~ 269.2) | 0.001 |
| Prothrombin time (PT, s) | 12.6 (12.0 ~ 13.5) | 12.4 (11.4 ~ 12.8) | 0.002 |
| International normalized ratio (INR, s) | 1.1 (1.06 ~ 1.18) | 1.09 (1.02 ~ 1.14) | 0.004 |
| Fibrinogen (FIB, g/L) | 2.96 (2.13 ~ 3.15) | 2.86 (2.31 ~ 3.20) | 0.524 |
| Activated partial thromboplastin time (APTT, s) | 30.04 (27.4 ~ 30.44) | 30.75 (28.3 ~ 31.6) | 0.011 |
| Thrombin time (TT, s) | 15.46 (13.8 ~ 16.10) | 15.7 (14.1 ~ 16.19) | 0.426 |
| D-dimer (mg/L) | 4.78 (4.33 ~ 4.97) | 5.91 (5.13 ~ 6.08) | < 0.0001 |
| Antithrombin III (AT III, %) | 95.35 (94.77 ~ 96.03) | 97.00 (74.55 ~ 98.08) | < 0.0001 |
Table 3.
Logistic regression analysis in muscle necrosis following acute compartment syndrome
| Variables | OR | 95%CI | p value | |
|---|---|---|---|---|
| Lower limit | Upper limit | |||
| Crush injury | 2.598 | 1.298 | 5.201 | 0.007 |
| NEU | 1.124 | 1.048 | 1.205 | 0.001 |
| CKMB | 1.002 | 1.000 | 1.004 | 0.047 |
| PT | 1.992 | 1.066 | 3.720 | 0.031 |
| Hypertension | 0.188 | 0.036 | 0.986 | 0.048 |
| Mg2+ | 0.012 | 0.000 | 0.686 | 0.032 |
| Na+ | 0.741 | 0.629 | 0.874 | < 0.0001 |
NEU, neutrophil; CKMB, creatine kinase myocardial band; PT, prothrombin time
ROC curve analysis showed that the cut-off values of NEU, CKMB, and PT to predict muscle necrosis following ACS were 11.415 109/L, 116.825 U/L, and 12.51 s, respectively (Table 4). Additionally, we tried to find which one with the highest diagnostic accuracy and identified NEU + CKMB + PT [p < 0.0001, AUC area = 0.666, 95%CI (0.591, 0.741)] with the highest diagnostic accuracy (Table 4 and Fig. 2).
Table 4.
ROC curve analysis and cut-off value in muscle necrosis following acute compartment syndrome
| Variables | Area under the ROC curve | p value | 95%CI | Cut-off value | |
|---|---|---|---|---|---|
| Lower limit | Upper limit | ||||
| PT | 0.621 | 0.003 | 0.544 | 0.699 | 12.51 s |
| CKMB | 0.638 | 0.001 | 0.555 | 0.721 | 116.825 U/L |
| NEU | 0.631 | 0.001 | 0.554 | 0.709 | 11.415 109/L |
| CKMB + NEU | 0.649 | < 0.0001 | 0.573 | 0.725 | NA |
| CKMB + PT | 0.621 | 0.003 | 0.544 | 0.699 | NA |
| PT + NEU | 0.631 | 0.001 | 0.554 | 0.709 | NA |
| NEU + CKMB + PT | 0.666 | < 0.0001 | 0.591 | 0.741 | NA |
PT, prothrombin time; CKMB, creatine kinase myocardial band; NEU, neutrophil; ROC, receiver operator characteristic
Fig. 2.
Risk factors of muscle necrosis following ACS in receiver operating characteristic curve analysis
Discussion
ACS commonly occurs in the lower leg and upper limb and represents tissue ischemia that is related to an increase in pressure within a closed compartment [10–12]. It often leads to some tough complications, such as infection, amputation, or muscle necrosis. Mortensen [9] retrospectively reviewed 357 patients with ACS and identified open fractures as the only independent risk factor for muscle necrosis following ACS, while Hope [13] collected the data from 164 cases and found that muscle necrosis was more common in the absence of a fracture than in those with a fracture. As far as we know, few studies have focused on the risk factors for muscle necrosis after ACS. After reviewing the related articles, we found that previous articles were more concerned with the injury mechanism but overlooked the predictive role of admission laboratory indicators. Therefore, we tried to investigate the risk factors of muscle necrosis in patients with ACS according to overall data, including demographics, comorbidities, and admission laboratory indicators.
In our study, the rate of muscle necrosis was 37.6% (83 of 221). Logistic regression analysis indicated that crush injury and levels of NEU, CKMB, and PT were independent risk factors for muscle necrosis. However, the level of Mg2+ and Na+, as well as hypertension, played protective roles in muscle necrosis. ROC curve analysis indicated that 11.415 109/L, 116.825 U/L, and 12.51 s were the cut-off values for NEU, CKMB, and PT to predict muscle necrosis following ACS, respectively. Furthermore, the combination of NEU, CKMB, and PT had the highest diagnostic accuracy.
Mortensen [9] showed 14% of patients suffering muscle necrosis, whereas Vaillancourt [14] found 49% of 76 ACS patients with muscle necrosis. Hope [13] has reported that 8% of patients with fractures and 20% of patients without fractures experienced muscular necrosis. We found 37.6% of patients with muscle necrosis, which was within the range of previous literature reports. Mortensen [9] discovered that open fracture and crush injury were associated with muscle necrosis in univariate analysis but only found that open fracture was the only risk factor for necrotic muscle, which was inconsistent with our findings. In the current study, we found that crush injury was an important predictor, but open fractures were not closely related to muscle necrosis. The difference can be explained by the fact that our study included more potential factors. It is well known that a crush injury is a relatively serious injury type that is prone to muscle necrosis.
It is well known that the increasing pressure in the compartment can cause a hypoxic and ischemic microenvironment and increase the degree of muscle necrosis resulting in aseptic inflammation [15–18], which inevitably leads to variations in immune cells. We first focused on the role of immune cells in muscle necrosis following ACS. In this study, univariate analysis showed that the levels of NEU, NEU%, NLR, and WBC were significantly higher in MG than in NG. Furthermore, logistic regression analysis indicated that NEU was an independent risk factor for muscle necrosis. As we know, NEU, a major type of WBS, is not only effective antimicrobial cells but also responsible for the removal of necrotic tissues [19], which implies that the level of NEU can reflect the degree of tissue necrosis to some extent. Furthermore, we identified 11.415 109/L as the cut-off value of NEU by ROC curve analysis. Notably, we tried to investigate the role of NLR, a valuable predictor of inflammation [20, 21], in the diagnosis of muscle necrosis. NLR was found to be associated with muscle necrosis in univariate analysis, but it was not an independent risk factor based on logistic regression analysis.
CK and CKMB have been widely used to detect acute myocardial injury in clinical practice due to their high sensitivity and specificity [22]. Then, it was also applied to monitor other injuries, such as skeletal muscle injury [23, 24], pulmonary embolism [25], or brain injury [26]. However, to our knowledge, few studies have reported the effects of CK and CKMB on the detection of muscle necrosis in ACS patients. In this study, we explored their roles in the prediction of muscle necrosis following ACS and found that CKMB played a crucial role in muscle necrosis according to univariate analysis and logistic regression analysis. Moreover, we identified 116.825 U/L as the cut-off value of CKMB to predict muscle necrosis following ACS. Similarly, PT represents a widely used method to investigate coagulation in clinical laboratories. Recent studies have reported its new functions in the forecast of other medical fields, such as poor clinical outcomes in COVID-19 patients [27], bleeding risk in liver cirrhosis [28], or recurrence-free survival time of renal cancer [29]. This was the first study to use PT as a potential risk factor for muscle necrosis after ACS. Interestingly, PT was found to be a vital risk factor in univariate analysis and logistic regression analysis and we also indicated the cut-off value of PT to predict muscle necrosis. We also tried to use the combination of risk factors to predict muscle necrosis and found the combination (NEU, CKMB, and PT) with the highest diagnostic accuracy.
We also found some indicators that acted as protective factors for muscle necrosis. For example, patients with a history of hypertension played a protective role in the prevention of muscle necrosis according to logistic regression analysis. High blood pressure may be able to reverse the build-up of increased compartment pressure and maintain the pressure gradient required for oxygen and nutrients to flow into muscle tissue, which may be beneficial to delay the process of muscle necrosis. In addition, the levels of Mg2+ and Na+ in MG were significantly lower than in NG, implying that they contributed to the prevention of muscle necrosis after ACS. Previous studies [30, 31] have described that Na+ can relieve ischemic damage and the important role of Mg2+ in immune responses and signaling events of immune cells, which may explain their protective roles in delaying muscle necrosis.
Although this was the first study to specifically investigate the relationship between admission laboratory indicators and muscle necrosis following ACS and provided several novel findings, several limitations should also be noted. First, a single-center study is unavoidable to affect the accuracy of the results. A large sample, multicenter, and randomized controlled study was needed. Secondly, this was a retrospective study with inherent boundedness in data collection, especially in terms of self-reported comorbidities. Third, due to the limited number of ACS patients, we did not conduct a subgroup analysis based on the location of fractures, such as lower leg fractures or upper limb fractures.
Conclusions
Muscle necrosis is a serious complication after ACS; however, its risk factors remain poorly understood. In this study, we found that crush injury and level of NEU, CKMB, and PT were independent risk factors for muscle necrosis in patients with ACS following fractures. We also identified the cut-off values of NEU, CKMB, and PT, as well as the combination of NEU, CKMB, and PT with the highest diagnostic accuracy, which can help us assess the risk of muscle necrosis when facing ACS patients and take early targeted actions to lower its rate or even avoid it.
Abbreviations
- ACS
Acute compartment syndrome
- ASA
American Society of Anesthesiologists
- BAS
Basophil
- EOS
Eosinophil
- HGB
Hemoglobin
- LYM
Lymphocyte
- PLT
Platelet
- MON
Monocyte
- NEU
Neutrophil
- NLR
Neutrophil to lymphocyte ratio
- PLT
Platelet
- RBC
Red blood cell
- WBC
White blood cell
- RBC
Red blood cell
- CK
Creatine kinase
- CKMB
Creatine kinase isoenzyme
- ALB
Albumin
- GLOB
Globulin
- CK
Creatine kinase
- CKMB
Creatine kinase myocardial band
- CREA
Creatinine
- LDH
Lactic dehydrogenase
- TCO2
Total carbon dioxide
- UREA
Ureophil
- PT
Prothrombin time
- INR
International normalized ratio
- FIB
Fibrinogen
- APTT
Activated partial thromboplastin time
- TT
Thrombin time
- AT III
Antithrombin III
Author contribution
TW and SY were responsible for study concept and writing the article. JFG and TW were responsible to screen the abstracts and review the article. ZYH and YBL were responsible for reviewing and writing the article.
Funding
The research was supported by the Science and Technology Project of the Hebei Education Department (SLRC2019046); the Government-funded Clinical Medicine Outstanding Talent Training Project (2019); and the Natural Science Foundation of Hebei (H2020206193 and H2021206054). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability
Yes.
Code availability
Not applicable.
Declarations
Ethics approval and consent to participate
This retrospective study was approved by the Institutional Review Board of our hospital (NCT04529330, S2020-022–1) before collecting data. There is no need to write informed consent forms from patients because this is a retrospective study.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Tao Wang, Shuo Yang, and Junfei Guo contributed equally to this work.
Contributor Information
Yubin Long, Email: longyubin1987@163.com.
Zhiyong Hou, Email: drzyhou@gmail.com.
References
- 1.Acklin YP, Potocnik P, Sommer C. Compartment syndrome in dislocation and non-dislocation type proximal tibia fractures: analysis of 356 consecutive cases. Arch Orthop Trauma Surg. 2012;132:227–231. doi: 10.1007/s00402-011-1408-0. [DOI] [PubMed] [Google Scholar]
- 2.Ziran BH, Becher SJ. Radiographic predictors of compartment syndrome in tibial plateau fractures. J Orthop Trauma. 2013;27:612–615. doi: 10.1097/BOT.0b013e31828e25b6. [DOI] [PubMed] [Google Scholar]
- 3.Guo J, Yin Y, Jin L, Zhang R, Hou Z, Zhang Y. Acute compartment syndrome: cause, diagnosis, and new viewpoint. Medicine (Baltimore) 2019;98(27):e16260. doi: 10.1097/MD.0000000000016260. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Duckworth AD, McQueen MM. The diagnosis of acute compartment syndrome: a critical analysis review. JBJS Rev. 2017;5(12):e1. doi: 10.2106/JBJS.RVW.17.00016. [DOI] [PubMed] [Google Scholar]
- 5.Le Thua TH, Pham DN, Boeckx W. Missed compartment syndrome of the forearm: limb salvage and reconstruction. J Hand Microsurg. 2015;7(1):129–132. doi: 10.1007/s12593-014-0118-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lollo L, Grabinsky A. Clinical and functional outcomes of acute lower extremity compartment syndrome at a major trauma Hospital. Int J Crit Illn Inj Sci. 2016;6(3):133–142. doi: 10.4103/2229-5151.190648. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Challa ST, Hargens AR, Uzosike A, Macias BR. Muscle microvascular blood flow, oxygenation, pH, and perfusion pressure decrease in simulated acute compartment syndrome. J Bone Joint Surg Am. 2017;99(17):1453–1459. doi: 10.2106/JBJS.16.01191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Goyal S, Naik MA, Tripathy SK, Rao SK. Functional outcome of tibial fracture with acute compartment syndrome and correlation to deep posterior compartment pressure. World J Orthop. 2017;8(5):385–393. doi: 10.5312/wjo.v8.i5.385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Mortensen SJ, Zhang D, Mohamadi A, Collins J, Weaver MJ, Nazarian A, von Keudell AG. Predicting factors of muscle necrosis in acute compartment syndrome of the lower extremity. Injury. 2020;51(2):522–526. doi: 10.1016/j.injury.2019.11.022. [DOI] [PubMed] [Google Scholar]
- 10.Matsen FA. Compartmental syndromes: an unified concept. Clin Orthop Relat Res. 2018;113:8–14. doi: 10.1097/00003086-197511000-00003. [DOI] [PubMed] [Google Scholar]
- 11.Schmidt AH. Acute compartment syndrome. Injury. 2017;48(Suppl 1):S22–S25. doi: 10.1016/j.injury.2017.04.024. [DOI] [PubMed] [Google Scholar]
- 12.Mehta V, Chowdhary V, Lin C, Jbara M, Hanna S. Compartment syndrome of the hand: a case report and review of literature. Radiol Case Rep. 2018;13(1):212–215. doi: 10.1016/j.radcr.2017.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hope MJ, McQueen MM. Acute compartment syndrome in the absence of fracture. J Orthop Trauma. 2004;18(4):220–224. doi: 10.1097/00005131-200404000-00005. [DOI] [PubMed] [Google Scholar]
- 14.Vaillancourt C, Shrier I, Vandal A, Falk M, Rossignol M, Vernec A, et al. Acute compartment syndrome: how long before muscle necrosis occurs? CJEM. 2004;6(3):147–154. doi: 10.1017/S1481803500006837. [DOI] [PubMed] [Google Scholar]
- 15.Labbe R, Lindsay T, Walker PM. The extent and distribution of skeletal muscle necrosis after graded periods of complete ischemia. J Vasc Surg. 1987;6:152–157. doi: 10.1067/mva.1987.avs0060152. [DOI] [PubMed] [Google Scholar]
- 16.Petrasek PF, Homer-Vanniasinkam S, Walker PM. Determinants of ischemic injury to skeletal muscle. J Vasc Surg. 1994;19:623–631. doi: 10.1016/S0741-5214(94)70035-4. [DOI] [PubMed] [Google Scholar]
- 17.Sheridan GW, Matsen FA, III, Krugmire RB., Jr Further investigations on the pathophysiology of the compartmental syndrome. Clin Orthop Relat Res. 1977;123:266–270. [PubMed] [Google Scholar]
- 18.Matsen FA, III, et al. Physiological effects of increased tissue pressure. Int Orthop. 1979;3:237–244. doi: 10.1007/BF00265718. [DOI] [PubMed] [Google Scholar]
- 19.Burn GL, Foti A, Marsman G, Patel DF, Zychlinsky A. The neutrophil. Immunity. 2021;54(7):1377–1391. doi: 10.1016/j.immuni.2021.06.006. [DOI] [PubMed] [Google Scholar]
- 20.Sabouri E, Majdi A, Jangjui P, Rahigh Aghsan S, Naseri Alavi SA. Neutrophil-to-lymphocyte ratio and traumatic brain injury: a review study. World Neurosurg. 2020;140:142–147. doi: 10.1016/j.wneu.2020.04.185. [DOI] [PubMed] [Google Scholar]
- 21.He S, He S, Yang Y, Li B, Gao L, Xie Q, Zhang L. Correlation between neutrophil to lymphocyte ratio and myocardial injury in population exposed to high altitude. Front Cardiovasc Med. 2021;8:738817. doi: 10.3389/fcvm.2021.738817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Crea F. Challenges of antithrombotic treatment in high-risk populations and a fond farewell to CKMB. Eur Heart J. 2021;42(23):2221–2224. doi: 10.1093/eurheartj/ehab345. [DOI] [PubMed] [Google Scholar]
- 23.Benoist JF, Cosson C, Mimoz O, Edouard A. Serum cardiac troponin I, creatine kinase (CK), and CK-MB in early posttraumatic rhabdomyolysis. Clin Chem. 1997;43(2):416–417. doi: 10.1093/clinchem/43.2.416. [DOI] [PubMed] [Google Scholar]
- 24.Siegel AJ, Silverman LM, Evans WJ. Elevated skeletal muscle creatine kinase MB isoenzyme levels in marathon runners. JAMA. 1983;250(20):2835–2837. doi: 10.1001/jama.1983.03340200069032. [DOI] [PubMed] [Google Scholar]
- 25.Adams JE, 3rd, Siegel BA, Goldstein JA, Jaffe AS. Elevations of CK-MB following pulmonary embolism. A manifestation of occult right ventricular infarction. Chest. 1992;101(5):1203–1206. doi: 10.1378/chest.101.5.1203. [DOI] [PubMed] [Google Scholar]
- 26.Zwirner J, Anders S, Bohnert S, Burkhardt R, Da Broi U, Hammer N, Pohlers D, Tse R, Ondruschka B. Screening for fatal traumatic brain injuries in cerebrospinal fluid using blood-validated CK and CK-MB immunoassays. Biomolecules. 2021;11(7):1061. doi: 10.3390/biom11071061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wang L, He WB, Yu XM, Hu DL, Jiang H. Prolonged prothrombin time at admission predicts poor clinical outcome in COVID-19 patients. World J Clin Cases. 2020;8(19):4370–4379. doi: 10.12998/wjcc.v8.i19.4370. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ruberto MF, Marongiu F, Sorbello O, Civolani A, Demelia L, Barcellona D. Are prothrombin time and clot waveform analysis useful in detecting a bleeding risk in liver cirrhosis? Int J Lab Hematol. 2019;41(1):118–123. doi: 10.1111/ijlh.12934. [DOI] [PubMed] [Google Scholar]
- 29.Bian Z, Meng J, Niu Q, Jin X, Wang J, Feng X, Che H, Zhou J, Zhang L, Zhang M, Liang C. Prognostic role of prothrombin time activity, prothrombin time, albumin/globulin ratio, platelets, sex, and fibrinogen in predicting recurrence-free survival time of renal cancer. Cancer Manag Res. 2020;12:8481–8490. doi: 10.2147/CMAR.S264856. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Wen H, et al. Neuroglobin mediates neuroprotection of hypoxic postconditioning against transient global cerebral ischemia in rats through preserving the activity of Na(+)/K(+) ATPases. Cell Death Dis. 2018;9:635. doi: 10.1038/s41419-018-0656-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Brandao K, Deason-Towne F, Perraud AL, Schmitz C. The role of Mg2+ in immune cells. Immunol Res. 2013;55(1–3):261–269. doi: 10.1007/s12026-012-8371-x. [DOI] [PubMed] [Google Scholar]
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