Correlation between blood glucose level and poor wound healing after posterior lumbar interbody fusion in patients with type 2 diabetes

Abstract

To investigate the correlation of blood glucose level with poor wound healing (PWH) after posterior lumbar interbody fusion (PLIF) in patients with type 2 diabetes (T2D). From January 2016 to January 2023, a case–control study was conducted to analyse the clinical data of 400 patients with T2D who were treated by PLIF and internal fixation at our hospital. The following data were recorded: gender; age; body mass index (BMI); surgical stage; average perioperative blood glucose level; perioperative blood glucose variance; perioperative blood glucose coefficient of variation; glycated haemoglobin level; preoperative levels of total protein, albumin and haemoglobin; postoperative levels of total protein, albumin and haemoglobin; surgical time; intraoperative bleeding volume; operator; postoperative drainage volume; and postoperative drainage tube removal time of each group. The indicators for monitoring blood glucose variability (GV) included the SD of blood glucose level (SDBG), coefficient of variation (CV) and maximum amplitude of variation (LAGE) before and after surgery. According to the diagnostic criteria for PWH, patients with postoperative PWH were determined and assigned to two groups: Group A (good wound healing group; n = 330 patients) and Group B (poor wound healing group; n = 70 patients). The preoperative and postoperative blood GV indicators, namely SDBG, CV and LAGE, were compared between these two groups. We also determined the relationship between perioperative blood GV parameters and PWH after PLIF surgery and its predictive value through correlation analysis and receiver‐operating characteristic curve. Of the 400 enrolled patients, 70 patients had PWH. Univariate analysis revealed significant differences between the two groups in the course of diabetes, mean fasting blood glucose (MFBG), SDBG, CV, LAGE, preoperative hypoglycaemic program, surgical segment, postoperative drainage time, incision length and other factors (p < 0.05). However, no significant differences were noted in factors such as gender, age, body mass index, hypertension, coronary heart disease, admission fasting blood glucose, preoperative haemoglobin A1c, surgical time, intraoperative bleeding volume, intraoperative blood transfusion volume and postoperative drainage volume (p > 0.05). The area under the curve (AUC) values of preoperative SDBG, CV and LAGE were 0.6657, 0.6432 and 0.6584, respectively. The cut‐off values were 1.13 mmol/L, 6.97% and 0.75 mmol/L, respectively. The AUC values for postoperative SDBG, CV and LAGE were 0.5885, 0.6255 and 0.6261, respectively. The cut‐off values were 1.94 mmol/L, 24.32% and 2.75 mmol/L, respectively. The multivariate ridge regression analysis showed that preoperative MFBG, SDBG, CV and LAGE; postoperative SDBG, CV and LAGE; postoperative long drainage time; and multiple surgical segments were independent risk factors for T2D patients to develop surgical site infection after PLIF (p < 0.05). The perioperative blood GV in patients with T2D is closely related to the occurrence of PWH after PLIF. Reducing blood GV may help to reduce the occurrence of PWH after PLIF.

1. INTRODUCTION

Poor wound healing (PWH) is a common complication of spinal surgery in patients with diabetes, and it increases postoperative pain, prolongs hospital stay and increases treatment costs. Many risk factors are associated with PWH. Controllable blood glucose fluctuation is an important factor that influences PWH in diabetic patients undergoing lumbar surgery. ¹ Posterior lumbar interbody fusion (PLIF) is one of the main surgical methods for treating lumbar degenerative diseases, with stable therapeutic effects and a low recurrence rate. However, because of factors such as extensive trauma and long surgical time, there is a high risk of poor postoperative incision healing. Therefore, it is clinically important for spine surgeons to evaluate the fluctuation range of perioperative blood glucose levels in patients with diabetes.

As indicators of blood glucose variability (GV), the SD of blood glucose (SDBG), the coefficient of variation (CV) of blood glucose and the maximum amplitude of variation of blood glucose (LAGE) can show the stability and fluctuation range of blood glucose level over a period of time. The evaluation of whether the blood glucose level of diabetes patients meets the standard requirement is critical for predicting clinical events, and this evaluation does not consider glycosylated haemoglobin A1c (HbA_1c) level for prediction. We speculate that high blood GV is a risk factor for increased postoperative complications. Presently, during the perioperative period, clinicians should not only focus on regulating long‐term hyperglycaemia but also pay attention to blood sugar control.

Recently, there has been considerable clinical focus on the average blood glucose and HbA_1c levels of the patients in the past 2–3 months before surgery, which correspond to a wide range of personal blood glucose profiles. The disadvantage of this approach is that it cannot reflect the extent of blood GV and the degree of blood glucose fluctuation. As reported earlier, GV fully reflects the degree of blood glucose fluctuation, and the increase in blood glucose fluctuation and the prolongation of fluctuation time will increase the risk of diabetes‐related complications. There are, however, few studies on perioperative blood GV and postoperative PWH. The present study aimed to retrospectively analyse the correlation between blood GV in patients with type 2 diabetes (T2D) and PWH after PLIF and to investigate the predictive value of blood GV in T2D patients for PWH after PLIF.

2. GENERAL INFORMATION AND METHODS

2.1. General information

This study retrospectively analysed the clinical data of T2D patients with lumbar intervertebral disc degeneration (LDD) who received PLIF treatment from January 2016 to January 2023. According to the inclusion and exclusion criteria, eligible patients were included in the study, and the surgical indications for each selected patient were strictly evaluated. The surgery was completed independently by a team of chief physicians in accordance with the standard operating procedures. The clinical data of all patients during hospitalization were complete.

Inclusion criteria were as follows: (1) based on medical history, symptoms, signs and imaging findings, the clinical diagnosis of LDD included lumbar disc herniation, lumbar spinal stenosis and lumbar spondylolisthesis; (2) clear surgical indications; (3) had PLIF; and (4) associated with T2D.

Exclusion criteria were as follows: (1) presence of lumbar infection, tumour, scoliosis, tuberculosis and intraspinal space occupation; (2) history of lumbar spine surgery; (3) coexistence of diabetic peripheral neuropathy; and (4) incomplete clinical data.

2.2. Research methods

2.2.1. Diagnostic criteria for PWH

1. Surgical site infection (SSI): Diagnosis was made based on the presence of one of the following conditions: (1) redness, swelling, heat, pain or purulent secretion on the surface of the incision; (2) presence of pus in the deep part of the incision (determined through drainage or puncture); (3) purulent secretion or fever >38°C in the cracked incision and local pain or tenderness in the incision; (4) deep incision abscess or other evidence of infection found by surgical exploration, histopathology or imaging examination; and (5) incisional or lacunar infections diagnosed by clinicians on the basis of positive bacterial culture of secretions after pathogen testing.

2. PWH: the condition was diagnosed based on surgical wound exudation, surgical wound rupture, scar hyperplasia, sinus formation and skin or flap necrosis. During medical record data collection, the grouping criteria were (1) excessive use of antibiotics (≥3 days) and (2) description of ‘poor healing of the surgical site’ in the medical and nursing records.

2.2.2. Diagnostic criteria for T2D

According to the diagnostic criteria for diabetes issued by the National Health Commission of the People's Republic of China, an individual is considered to have diabetes if any of the following criteria is met: (1) symptoms of diabetes (polydipsia, polyuria, weight loss, skin itching, blurred vision and other acute metabolic disorders caused by hyperglycaemia) and random blood glucose level ≥11.1 mmol/L; (2) fasting plasma glucose (FPG) level ≥7.0 mmol/L; and (3) the blood glucose level at 2 h after glucose loading is ≥11.1 mmol/L.

2.2.3. Data collection

The hospitalization medical records of all patients who met the inclusion criteria were reviewed. According to the PWH diagnostic criteria, the patients were assigned to the following two groups: the group with poor incision healing (PWH group) and the group with good incision healing (non‐PWH group). The following general data were collected: gender, age, body mass index (BMI), course of diabetes, hypertension, coronary heart disease, admission fasting blood glucose, preoperative glycosylated haemoglobin, preoperative and postoperative mean fasting blood glucose (MFBG) and preoperative hypoglycaemic program (whether the patient received oral metformin). Surgery‐related factors included intraoperative bleeding volume, intraoperative blood transfusion volume, surgical time, surgical segment, postoperative drainage time, postoperative drainage volume and incision length. GV monitoring indicators included SDBG, CV and LAGE before and after surgery. Blood glucose monitoring was performed by the rapid bedside blood glucose monitoring method in the hospital (Contour TS by Ascensia Diabetes Care Inc.). Blood glucose monitoring indicators were calculated as follows: MFBG—fasting blood glucose levels before surgery and 3 days after surgery; SDBG—SD of fasting blood glucose before surgery and 3 days after surgery; CV—the ratio of SD to the mean value of fasting blood glucose before surgery and after surgery; and LAGE—the difference between the highest and lowest blood glucose levels before surgery and within 3 days after surgery.

All the MR examinations were anonymized and randomly presented on a picture archiving and communication system (Siemens AG, Germany) to a spinal surgeon (over 15 years experience in spinal surgery), who was not aware of any patient data. For the assessment of the interreader agreement, a radiological fellow (10 years experience in spinal surgery) evaluated the same images.

The whole image stack of the sagittal STIR sequence was used to confirm or to rule out the presence of posterior lumbar subcutaneous oedema (PLSE), which was defined as diffuse STIR‐hyperintense signal changes within the deep posterior lumbar subcutaneous tissue that did not reach the superficial fat layers.

Degree of fat infiltration paraspinal muscles: Fat in the paraspinal muscles was visually graded at surgical lumbar vertebrae levels using the axial T2‐weighted image (T1 if T2 was lacking) closest to an axial plane through the mid‐sagittal posterior and anterior caudal corners of the upper vertebrae. The grading was based on the criteria used by Khattab et al: 0 = 0 or < 10% of total cross‐section (left plus right side) contains fat, 1 = 11%–50% of cross‐section contains fat, 2 = >50% of cross‐section contains fat.

2.3. Statistical analysis

SPSS 25.0 software was used for statistical analysis. The measurement data were tested for normality and homogeneity of variance, expressed as mean ± SD and compared between the two groups by using t‐test. The count data were expressed as a percentage and analysed by the chi‐square test. Because there was a strong multicollinearity issue between preoperative and postoperative blood glucose indicators (calculation showed tolerance [Tol] < 0.1 and variance inflation factor [VIF] > 10), ridge regression analysis was used to analyse the independent risk factors of postoperative PWH in patients with T2D. The receiver‐operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the relevant indicators for postoperative SSI. A p value of <.05 was considered to indicate a statistically significant difference.

3. RESULTS

3.1. Single factor analysis

A total of 400 patients were enrolled in this study, including 70 patients in the PWH group and 330 patients in the non‐PWH group. Regarding the basic characteristics of the patients, the two groups showed significant differences in the course of diabetes, admission fasting blood glucose, preoperative and postoperative MFBG, preoperative albumin, BMI, and other parameters (p < 0.05). However, no significant differences were observed in gender, age, preoperative HbA1c, preoperative total protein, preoperative haemoglobin, oral metformin intake and surgical implant brand (Tables 1 and 2).

TABLE 1.

Basic characteristics of patients in two groups.

	PWH group	Non‐PWH group	t or χ 2 value	p Value
Gender, n (%)
Male	42 (60.0%)	146 (44.2%)	0.423	0.515
Female	28 (40.0%)	184 (55.8%)
Age (years)	68.23 ± 10.21	66.82 ± 9.16	1.783	0.103
Duration of diabetes (years)	10.06 ± 5.31	8.22 ± 6.08	2.198	0.031
Body mass index
<25 kg/cm2	31 (44.3%)	233 (70.6%)	17.828	0.000
≥25 kg/cm2	39 (55.7%)	97 (29.4%)
Fasting blood glucose on admission (mmol/L)	7.17 ± 2.65	6.57 ± 1.61	−1.824	0.070
Preoperative HbA1c (%)	7.72 ± 1.89	7.16 ± 1.68	−1.547	0.125
Preoperative MFBG (mmol/L)	8.37 ± 2.82	6.58 ± 1.30	−8.190	<0.000
Postoperative MFBG (mmol/L)	8.97 ± 2.44	8.26 ± 2.21	−2.382	0.018
Preoperative total protein (g/dL)	68.76 ± 6.42	70.48 ± 5.45	1.472	0.144
Preoperative albumin (g/dL)	40.92 ± 4.56	42.70 ± 3.28	2.288	0.024
Preoperative haemoglobin (g/dL)	132.31 ± 16.08	130.18 ± 14.39	−0.707	0.481
Oral metformin (%)	36 (51.4%)	198 (60.0%)	1.748	0.168

Abbreviations: HbA_1c, haemoglobin A1c; MFBG, mean fasting blood glucose; PWH, poor wound healing.

TABLE 2.

Comparison of implant brand between groups.

Group	WEGO	WALKMAN	FULE	CHUNLI	DOUBLE	SANYOU	χ 2 Value	p Value
PWH Group	38 (54.3%)	9 (12.9%)	6 (8.6%)	3 (4.3%)	7 (10%)	7 (10%)	6.295	0.279
Non‐PWH group	189 (57.3%)	40 (12.1%)	36 (10.9%)	15 (4.5%)	39 (11.8%)	11 (3.3%)

Abbreviations: PWH, poor wound healing.

Regarding surgery‐related factors, a significant correlation was observed between PWH group and non‐PWH group showed a significant difference in surgical duration, intraoperative bleeding volume, postoperative drainage volume, postoperative drainage time, postoperative cerebrospinal fluid leakage and the number of surgical segments (p < 0.05, Table 3).

TABLE 3.

Comparison of operative factors between two groups.

	PWH group	Non‐PWH group	t or χ 2 value	p Value
Duration of operation (min)	219.73 ± 129.56	172.58 ± 52.29	−2.515	0.014
Intraoperative blood loss (mL)	524.44 ± 488.58	360.55 ± 272.55	−2.119	0.037
Intraoperative blood transfusion (mL)	322.14 ± 187.12	258.50 ± 98.04	−0.842	0.415
Postoperative drainage time (days)	2.89 ± 0.96	2.49 ± 0.60	−2.526	0.013
Postoperative drainage volume (mL)	360.49 ± 185.00	284.89 ± 150.25	−2.255	0.026
Postoperative cerebrospinal fluid leakage (%)	7 (10%)	12 (36%)	5.169	0.023
No. of operative levels
One level	32 (45.7%)	211 (63.9%)	8.054	0.005
Two or more levels	38 (54.3%)	119 (36.1%)

Abbreviation: PWH, poor wound healing.

The PWH group showed significantly higher values of GV indicators, namely SDBG, CV and LAGE, than the non‐PWH group before and after surgery (p < 0.05, Table 4).

TABLE 4.

Comparison of glycaemic variability indicators between groups.

	PWH group	Non‐PWH group	t or χ 2 value	p Value
Preoperative SDBG	3.59 ± 3.26	1.23 ± 1.78	−10.336	<0.001
Preoperative LAGE	2.51 ± 3.21	1.28 ± 1.53	−2.515	0.014
Preoperative CV	19.27 ± 16.37	12.87 ± 9.40	−2.447	0.016
Postoperative SDBG	2.27 ± 1.70	1.12 ± 1.75	−4.505	<0.001
Postoperative LAGE	3.35 ± 2.34	2.03 ± 1.94	−3.093	0.003
Preoperative CV	24.42 ± 13.53	19.36 ± 10.31	−2.121	0.036

Abbreviations: CV, coefficient of variation; LAGE, maximum amplitude of variation; PWH, poor wound healing; SDBG, SD of blood glucose level.

The two groups also showed significant differences in objective imaging indicators, that is, preoperative magnetic resonance imaging (MRI) showed fatty infiltration in the lumbar paravertebral muscle and PLSE (p < 0.05, Table 5).

TABLE 5.

Comparison of magnetic resonance imaging (MRI) factors between groups.

	PWH group	Non‐PWH group	t or χ2 value	p Value
Degree of fat infiltration paraspinal muscles
0 = 0 or < 10 (%)	20 (28.6%)	186 (56.4%)	31.601	0.000
1 = 11–50 (%)	20 (28.6%)	95 (28.8%)
2 = >50 (%)	30 (42.9%)	49 (14.8%)
Posterior lumbar subcutaneous oedema
Yes	71 (21.5%)	259 (78.5%)	64.646	0.000
No	21 (30.0%)	49 (70.0%)

Abbreviation: PHSI, poor wound healing.

3.2. Ridge regression analysis

With the presence of PWH as the dependent variable, the significant indicators of univariate analysis included SDBG, CV and LAGE for fasting blood glucose before and after surgery as independent variables. Ridge regression analysis was used to analyse the risk factors of postoperative PWH in patients with T2D. The results showed that preoperative MFBG; preoperative SDBG, CV and LAGE for fasting blood glucose; and postoperative SDBG, CV and LAGE for fasting blood glucose were the risk factors affecting PWH in patients with T2D after PLIF (p < 0.05, Table 6).

TABLE 6.

Risk factors of poor wound healing (PWH) after posterior lumbar interbody fusion in patients with type 2 diabetes by ridge regression analysis (K = 0.50).

Related factors	Regression coefficient	SE	Standardized regression coefficient	t Value	p Value
Preoperative SDBG	0.041	0.007	0.789	5.336	0.000
Preoperative LAGE	0.016	0.004	0.058	3.748	0.000
Preoperative CV	0.000	0.001	0.013	6.785	0.000
Postoperative SDBG	0.189	0.004	0.066	4.550	0.000
Postoperative LAGE	0.013	0.003	0.066	4.550	0.000
Preoperative CV	0.001	0.003	0.524	3.434	0.000

Abbreviations: CV, coefficient of variation; LAGE, maximum amplitude of variation; SDBG, SD of blood glucose level.

3.3. Diagnostic value of blood glucose indicators for postoperative PWH after PLIF surgery

The area under the curve (AUC) values of SDBG, CV and LAGE for preoperative fasting blood glucose were 0.6657 (95% confidence interval [CI]: 0.5990–0.7324, p < 0.001), 0.6432 (95% CI: 0.5752–0.7111, p < 0.001) and 0.6584 (95% CI: 0.5918–0.7251, p < 0.001), respectively. The cut‐off values were 1.13 mmol/L, 6.97% and 0.75 mmol/L, respectively (Figure 1).

FIGURE 1.

Receiver‐operating characteristic (ROC) curve of preoperative glycaemic variability for the diagnosis of PWH after posterior lumbar interbody fusion in patients with type 2 diabetes. CV, coefficient of variation; LAGE, maximum amplitude of variation; SDBG, SD of blood glucose level.

The AUC values of SDBG, CV and LAGE for postoperative fasting blood glucose were 0.5885 (95% CI: 0.5138–0.6633, p < 0.05), 0.6255 (95% CI: 0.5518–0.6993, p < 0.001) and 0.6261 (95% CI: 0.5525–0.6998, p < 0.001). The cut‐off values were 1.94 mmol/L, 24.32% and 2.75 mmol/L, respectively (Figure 2).

FIGURE 2.

Receiver‐operating characteristic (ROC) curve of postoperative glycaemic variability for the diagnosis of PWH after posterior lumbar interbody fusion in patients with type 2 diabetes. CV, coefficient of variation; LAGE, maximum amplitude of variation; SDBG, SD of blood glucose level.

4. DISCUSSION

In recent years, there has been considerable social progress in developed and developing countries worldwide. Presently, the success of a surgery is determined not only according to the standardization of surgical procedures but also through direct evaluation based on patient perception, aesthetic improvement, no risk of infections and rapid healing of surgical sites. With the global changes in lifestyle and dietary habits, obese people, as a high‐risk group with T2D, are expected to become the predominant population in society and present new medical and health challenges. The aetiology of diabetes is multifactorial, and dietary conditions and genetic factors have been confirmed to play a critical role in the occurrence and development of diabetes. The abnormal endocrine function and increased secretion of proinflammatory cytokines in patients with diabetes are the causes of impairment of the dynamic balance between the skeletal muscle and the internal environment of the wound, which profoundly affects the healing of surgical wounds. ² , ³ Experimental studies have shown that a glucose concentration of 200 mg/dL (11 mmol/L) for 30 min can reduce the respiratory function of neutrophils in vitro, while a glucose concentration of 500 mg/dL (27.78 mmol/L) can reduce the function of immune cells at the infected site. However, this response may depend on the blood glucose concentration, duration, rate of concentration change or change in the hyperglycaemic state. ⁴ , ⁵ A long‐term high‐glucose internal environment can increase the levels of inflammatory cytokines and may alter the balance between proinflammatory and anti‐inflammatory cytokines as well as increase the fragility of blood vessels, leading to local ischemia and hypoxia at the lumbar surgical site; this reduces the healing function, resulting in PWH and increasing the risk of postoperative infections. ⁶ Therefore, it is crucial for surgeons to investigate the influencing factors of PWH in patients with T2D after PLIF.

Long‐term blood glucose control is based on the HbA1c value, while short‐term blood glucose control requires the monitoring of the blood glucose fluctuation range of capillaries during the perioperative period. A controlled range of good perioperative blood glucose fluctuation can reduce adverse complications after lumbar surgery. ⁷ The present study found that both perioperative mean blood glucose levels and blood glucose fluctuations have some influence on the occurrence of PWH after PLIF surgery. According to relevant literature, the average preoperative blood glucose level should be 4.4–10.0 mmol/L, and it is recommended to reduce insulin intake by 25% at night before surgery, which is more conducive to achieving the target blood glucose range for noncardiac surgery. ⁸

The mean postoperative glucose concentration should be less than 12.0 mmol/L. In patients with diabetes undergoing elective surgery, if HbA_1c is above 9%, it is recommended to delay surgery; however, if the patient wishes to undergo surgery, the timing of surgery is best chosen to be performed as early as possible during the day to minimize the impact of blood glucose on the patient and surgery. Moreover, in patients who cannot be operated on in the morning, continuous monitoring of blood glucose levels in the hospital is recommended to promptly detect and mitigate fasting‐induced hypoglycaemia and metabolic disorders. ⁹ A retrospective cohort study noted that among diabetic patients undergoing anterior cervical fusion, those with HbA_1c levels above 6.8% had increased odds of reoperation, and among diabetic patients undergoing posterior cervical fusion, those with HbA1c levels above 7.6% had an increased incidence of rehospitalization ¹⁰ ; this finding suggests that if preoperative HbA1c levels are above these thresholds, the preoperative optimization of glycaemic control should be considered. ¹⁰ Nursing care is also an important approach to control perioperative blood glucose fluctuations. Previous studies have shown that increasing perioperative care for monitoring blood glucose, such as distributing a ‘licence’ for surgery in patients with diabetes improving patient counselling and recruiting a perioperative nurse specialized in treating patients with diabetes, can lead to better blood glucose stability, less frequent episodes of hypoglycaemia and hyperglycaemia and significantly fewer postoperative complications, including blood glucose disorder complications, wound infections due to PWH and other infections. ¹¹

The above‐mentioned expert consensus and guidelines apply to a very broad range of individuals and conditions, and thus, they are not specific to surgical procedures. The degree of damage to the body varies among different surgical procedures. Hence, in future, in‐depth research should be conducted on the effect of perioperative blood glucose fluctuations on different surgical procedures. For patients with diabetes who suffer from low back pain and require urgent surgical treatment, further targeted guidance on the perioperative blood glucose fluctuation range is needed. In the present study, to further improve the perioperative monitoring of blood glucose fluctuations, the parameter of blood GV was introduced. Blood GV, also known as blood glucose stability, is an important indicator of the stability of blood glucose levels in patients with diabetes. This study focuses on the stability of blood glucose between daytime fasting blood glucose levels. The monitored GV indicators included SDBG, CV and LAGE for preoperative and postoperative fasting blood glucose. This study found significant differences in the average fasting blood glucose level and preoperative and postoperative GV between the PWH and non‐PWH groups (p < 0.05). The ROC curves and the AUC values of preoperative and postoperative GV showed that the diagnostic value of preoperative GV for PWH was in the following descending order: preoperative fasting SDBG > preoperative fasting LAGE > preoperative fasting CV. The diagnostic value of postoperative GV for PWH was in the following descending order: postoperative fasting LAGE > postoperative fasting CV > postoperative fasting SDBG. Because there was a collinearity issue among these indicators, ridge regression analysis was used in this study. According to the results, the recommended values of SDBG, CV and LAGE for preoperative fasting blood glucose were 1.13 mmol/L, 6.97% and 0.75 mmol/L, respectively. Furthermore, the recommended values of SDBG, CV and LAGE for postoperative fasting blood glucose were 1.94 mmol/L, 24.32% and 2.75 mmol/L, respectively.

In addition to blood GV, this study also found a correlation between PWH and preoperative albumin levels. A previous study showed a correlation between the postoperative healing of the lumbar spine incision and the dynamic changes in perioperative albumin levels, ¹² which is consistent with the results of the present study. Another previous study reported that serum albumin levels reflect the nutritional status of patients to some extent; thus, the perioperative nutritional status of a patient influences postoperative wound healing of the lumbar spine. ¹³

Surgery‐related factors such as surgical time, surgical bleeding volume, surgical segment, postoperative drainage volume, drainage days and postoperative cerebrospinal fluid leakage were correlated with postoperative PWH after PILF. A higher number of surgical segments leads to a greater extent of surgical injury, longer surgical time, more intraoperative bleeding, longer surgical site healing time and more days of drainage placement, all of which are correlated with PWH. ¹⁴ A previous study confirmed that cerebrospinal fluid leakage can interfere with the healing of the thoracolumbar surgical site and increase the infection rate of the surgical site. ¹⁵ A histological study revealed that haematoxylin and eosin staining of tissue sections in the cerebrospinal fluid leakage model showed the formation of new blood vessels with small diameters, increased reactive interstitial tissue and granulation tissue, fibrosis of striated muscle, malnutrition‐induced calcification, fat necrosis and coagulation necrosis (ischemic necrosis); this demonstrated that cerebrospinal fluid leakage affects wound healing. ¹⁶ The suture materials and dressings after surgery are mostly chosen by the attending physicians based on their experience; consequently, there is no large‐scale, multicenter randomized control trial and expert guidance in this aspect. A systematic review of studies on this topic showed that the use of occlusive dressings after posterior spinal surgery can help reduce the risk of surgical site cracking; however, the risk of bias in this approach is high, and further research is required to address this issue. ¹⁶

As we know that diabetes affected the balance of muscle and fat, ¹⁷ the present study also revealed a clinical correlation between PWH and imaging‐related fat infiltration, and there were statistical differences in patients with T2D after PLIF. A study by University of California showed that the presence of symptomatic degenerative disc pathology results in arthrogenic inhibition and a selective shutting off of the local stabilizing muscles, notably the deep paraspinal muscles, as a protection mechanism, resulting in disuse and eventually, elevated levels of fat infiltration localized to the shorter, deeper fascicles of the paraspinal muscles. ¹⁸

The effect of obesity on spinal surgery complications is not yet fully understood. According to some clinicians, there is no difference in the incidence of postoperative complications between obese patients and normal weight patients. ¹⁷ However, our research findings revealed that obesity is a significant concern in spinal surgery. Obesity is a common condition in patients with diabetes; hence, BMI is a risk factor in our study. During PLIF surgery, fat liquefaction often occurs while stripping the soft tissue, particularly when using an electric knife. Therefore, we also considered factors such as the degree of fat infiltration in the lumbar paravertebral muscles. A retrospective cohort study in the United States showed that obese patients have a higher incidence of intraoperative complications than patients with normal weight. ¹⁹

Oedematous signal changes in the deep subcutaneous area of the waist, soft tissue around the fascia or the subcutaneous area of the posterior lumbar spine, most of which are incidentally discovered on T2 STIR images of MRI, and the cause of this oedema is not fully understood thus far. A previous study showed a correlation between it and weight gain, BMI increase and back fat thickness, thus suggesting an association with obesity. ²⁰ , ²¹ Moreover, obesity causes lymphatic flow disorder, leading to the accumulation of protein‐rich lymph in subcutaneous tissues, which usually results in subcutaneous oedema. ²² These reports confirm the common radiological findings of subcutaneous oedema in MRI, which are consistent with our results. A strong correlation was observed between subcutaneous oedema signals on MRI and PWH, with a significant difference between the PWH and non‐PWH groups. Furthermore, we also observed these patients had subcutaneous oedema and exudation during skin incision. These findings suggest that PWH may be correlated with subcutaneous oedema. However, currently, there is no relevant research confirming the effect of subcutaneous oedema on surgical site healing. Thus, further research on subcutaneous oedema as a predictive factor for surgical site healing is required in the future.

5. CONCLUSION

To summarize, PWH in patients with diabetes after PLIF is closely related to perioperative fasting blood glucose; GV parameters, that is, SDBG, CV and LAGE, for fasting blood glucose; other blood glucose factors; BMI; albumin and other general factors; operation time; manual blood loss; operation segment; postoperative drainage flow; days of drainage placement; postoperative cerebrospinal fluid leakage; and other surgery‐related factors. The maintenance of perioperative blood glucose stability is crucial to reduce the incidence of PWH. The inclusion of blood GV as a research indicator can enable to better understand the pattern of daily changes in blood glucose, improve the predictive indicators of postoperative PWH, provide a basis for establishing a predictive model of postoperative PWH and identify undetected hyperglycaemia and hypoglycaemia; all of these are clinically relevant for diabetes management.

The present single‐centre, retrospective study has some limitations. First, the sample size was small. Second, there are many confounding factors during the perioperative period, including medication patterns, physician growth curves and technological advancements. The lack of standardized blood glucose monitoring protocols is also a major limitation. Currently, noninvasive blood glucose monitoring devices have become popular because of their convenience, and these devices could play a major role in controlling GV during the perioperative period. In future, high‐quality and prospective studies are required to investigate the true diagnostic efficacy of blood GV indicators for postoperative outcomes and their applicability in clinical practice and to conduct in‐depth research on the role of strict blood glucose control in reducing the incidence of postoperative PWH.

AUTHOR CONTRIBUTION

Chen HJ, Wu ZJ and Huang FL were responsible for the conception and design; Chen HJ and Chen DY were responsible for manuscript writing and revision; and all authors read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest relevant to this article.

Chen H, Wu Z, Chen D, Huang F. Correlation between blood glucose level and poor wound healing after posterior lumbar interbody fusion in patients with type 2 diabetes. Int Wound J. 2024;21(1):e14340. doi: 10.1111/iwj.14340

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author, [Fuli Huang], upon reasonable request.