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Article

Is an Elevated Preoperative CRP Level a Predictive Factor for Wound Healing Disorders following Lumbar Spine Surgery?

1
Department of Neurosurgery, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
2
Department of Neurosurgery, Heinrich-Braun-Klinikum, 08060 Zwickau, Germany
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(7), 667; https://doi.org/10.3390/jpm14070667
Submission received: 3 May 2024 / Revised: 10 June 2024 / Accepted: 18 June 2024 / Published: 21 June 2024
(This article belongs to the Section Clinical Medicine, Cell, and Organism Physiology)

Abstract

:
Postoperative wound infections are a prevalent concern among the hospital-associated infections in Europe, leading to prolonged hospital stays, increased morbidity and mortality, and substantial patient burdens. Addressing the root causes of this complication is crucial, especially given the rising number of spine surgeries due to aging populations. Methods: A retrospective analysis was conducted on a cohort of 3019 patients who underwent lumbar spine surgery over a decade in our department. The study aimed to assess the predictors of wound healing disorders, focusing on laboratory values, particularly inflammatory parameters. Results: Of the 3019 patients, 2.5% (N = 74) experienced deep or superficial wound healing disorders, showing the significant correlation between C-reactive protein (CRP) levels and these disorders (p = 0.004). A multivariate analysis identified several factors, including age, sex, hypertension, diabetes, cardiac comorbidity, surgical duration, dural injury, and blood loss, as being correlated with wound healing disorders. Conclusion: Demographic factors, pre-existing conditions, and perioperative variables play a role in the occurrence of adverse effects related to wound healing disorders. Elevated CRP levels serve as an indicator of increased infection risk, though they are not a definitive diagnostic tool for wound healing disorders.

1. Introduction

In the field of spinal surgery, there has been a notable increase in the frequency of surgical interventions, accompanied by a corresponding rise in postoperative complications. Factors such as patient age, surgical duration, and comorbidities have been identified as contributing elements to these complications [1,2]. Given the escalating rate of surgeries and the shifting age demographics of the populace, the evaluation of risk factors is assuming greater significance [1]. Postoperative wound infections rank as the third most prevalent type of nosocomial infection and present a challenge across various surgical disciplines [3]. Complications such as postoperative wound healing disorders or infections can result in exacerbated back pain, thereby compromising the overall success of the surgical intervention [4,5,6]. Infections subsequent to spinal surgery not only lead to heightened resource utilization but also cause additional financial burdens [5].
Prognostically significant risk factors for wound healing disorders [7,8] in spinal surgery [9] typically include diabetes mellitus, a prolonged surgical duration, and a body mass index (BMI) exceeding 35 kg/m2 [10,11]. Other factors such as the number of segments operated upon, the surgical approach and technique, the type of implants utilized, and surgeon expertise also play contributory roles [12]. Smoking, steroid usage, and perioperative transfusions are additionally recognized as critical risk factors [12].
Preoperative laboratory testing stands out as the foremost and simplest means of detecting inflammatory processes in the human body. Parameters such as leukocyte count, C-reactive protein (CRP) level, and erythrocyte sedimentation rate (ESR) are integral to this assessment [13,14,15,16,17].
In addition to demographic data, pre- and perioperative risk factors and patient-related variables were investigated in this study to assess their influence on wound healing disorders. Moreover, we examined the relationship between elevated CRP levels before surgery and the occurrence of postoperative wound healing disorders.

2. Materials and Methods

In this retrospective study, we examined a cohort of 3019 patients who underwent lumbar region surgery at the Department of Neurosurgery between 2008 and 2018. The spectrum of surgical indications ranged from degenerative spinal canal stenosis to acute traumatic vertebral body injuries.
During the patient assessments, we gathered detailed information on concurrent illnesses and prior lumbar spine surgeries. Preoperatively, depending on their clinical presentation, magnetic resonance imaging (MRI) was conducted to assess the spinal cord and nerves, while computed tomography (CT) was utilized to evaluate bony structures.
The exclusion criteria comprised patients with inflammatory joint and spine diseases, open fractures, and those undergoing further diagnostic or therapeutic interventions as part of follow-up examinations. Clinical data were extracted from a registry and corresponding patient files. Categorical variables such as sex, BMI, ASA classification, hypertension, diabetes mellitus, smoking status, cardiovascular diseases, chronic inflammation, meningeal injuries, the number of operated segments, the use of nonsteroidal anti-inflammatory drugs, glucocorticoid intake, the perioperative use of synthetic materials, and the specific surgical intervention were analyzed.
Additionally, we retrospectively analyzed preoperative laboratory parameters such as hemoglobin levels, erythrocyte sedimentation rates (ESRs), leukocyte counts, and C-reactive protein (CRP) levels, along with age, the number of affected segments, and intraoperative blood loss.
A postoperative wound infection was defined by the presence of one of the following criteria:
(a)
Purulent discharge from the superficial wound area, possibly from below, or pus discharge from the internal drain.
(b)
Patients experiencing postoperative pain and new complaints displaying typical signs of inflammation in the wound area, such as erythema and hyperthermia, along with serologically typical signs of infection, including a renewed increase in inflammatory parameters (CRP, leukocytes).
(c)
In instances of the mentioned criteria, neurological complaints, or persistent lower back pain, MRI imaging was performed postoperatively to rule out potential complications related to wound healing or infection.

Statistical Analysis

Quantitative variables were summarized using the mean and standard deviation (SD) or median [1st quartile, 3rd quartile] depending on the distribution model—whether normal or not. Categorical data were presented as absolute frequencies (n) and percentages (%). The analysis was intentionally conducted at a significance level of 0.05 to accommodate its exploratory nature. Thus, any obtained p-value below 0.05 was considered statistically significant within this framework. For unadjusted analyses, Fisher’s exact test was applied to the categorical variables and the robust t-test (Satterthwaite) was employed for the continuous variables, with log transformation applied when the variables exhibited a notable deviation from normal distribution. Spearman’s correlation coefficient was used for correlation analyses. In addition to employing Fisher’s exact test and the robust t-test, propensity score matching was conducted. The goal was to create control groups, extracting the patients with similar characteristics from the samples to analyze them for risk factors. Data storage and statistical analyses were performed utilizing the SAS University Edition software package 9.4 (SAS Institute, Inc., Cary, NC, USA).

3. Results

A total of 74 out of the 3019 patients were diagnosed with postoperative wound infections, resulting in an infection rate of 2.5%. Table 1 presents a comprehensive list of patients who experienced nosocomial wound infections, accompanied by an overview of the examined risk factors that may have led up to the occurrence of the infection.
In our study, patients with wound healing disorders were an average age of 67.8 years old (p < 0.001), while those without postoperative wound complications were younger, with a mean age of 60.8 years. Gender emerged as a significant factor (p < 0.001). Among the 74 patients experiencing impaired wound healing, 77.03% (N = 57) were female and 22.97% were male (N = 17). However, other factors such as BMI and smoking status did not show a significant connection (BMI p = 0.101, smoker p = 0.173). Preoperative parameters, such as the ASA classification, demonstrated high significance (p < 0.001). Additionally, certain prior illnesses showed notable associations. Out of the 1697 patients with arterial hypertension, 57 patients experienced a postoperative wound infection (infection rate 3.35%) (p < 0.001). Among the 481 patients with diabetes mellitus, 22 patients (4.57%) developed a postoperative wound infection (p = 0.012). In the case of the 689 patients with coronary artery disease, 30 patients (4.35%) experienced a postoperative wound infection (p < 0.001).
The probability of a wound healing disorder in an individual with a healthy heart was significantly lower, at 1.88%. For chronic inflammations such as rheumatoid arthritis, there was no significant evidence (p = 0.078) of a connection. Differences were evident in the number of operated segments (p < 0.001). The majority of wound complications arose when two segments were operated on (54.79%, i.e., 40 patients). The likelihood of a wound complication increases with the number of operated segments: 1.14% with one segment, 3.16% with two segments, and 6.13% of patients with three or more segments operated on develop wound complications. Of the 535 patients with a dural injury, wound healing disorders subsequently occurred in 5.04% (N = 27) (p < 0.001). The risk of developing a wound complication was 2.1 times higher when using synthetic materials (p = 0.002). The intake of cortisone (p = 0.152) and non-steroidal anti-inflammatory drugs (p = 0.293) was not a significant indication of wound healing disorders. The incision–suture time ranged between 122.3 and 135.4 min (p < 0.001). Blood loss during surgery emerged as a significant predictor of wound healing (p < 0.002). The higher the blood loss, the more likely the risk of a wound healing disorder. The mean blood loss in patients with wound healing disorders was 217.9.
All patients with elevated CRP levels are summarized in Table 2 according to their previous illnesses. It becomes evident that many patients had an elevated CRP level without previous illnesses. Laboratory parameters, such as the CRP level (Table 3, Figure 1A), indicate clear evidence of a connection with wound healing disorders (p = 0.004). Patients with wound healing disorders had an average preoperative CRP value of 10.4 mg/L, whereas the mean for patients without wound complications was 4.8 mg/L. Blood sedimentation (Table 3, Figure 1B) also exhibited a significant correlation (p = 0.008). The mean value of the ESR in patients without wound healing disorders was 18.3 mm, whereas it increased to 25.5 mm in patients with wound healing disorders. The laboratory parameter of the small blood count, specifically the leukocyte count, showed no statistical significance (p = 0.117) (Table 3, Figure 1C). However, the mean amount of hemoglobin (Table 3, Figure 1D) in patients with wound healing disorders is 0.7 mmol/L lower (7.9 mmol/L) compared to patients without wound healing disorders (p = 0.001).
In patients with a postoperative wound infection, the average duration of the operation was 128.9 min (Table 3, Figure 1E). This is 26.3 min longer (102.6 min) than in patients who demonstrated good wound healing postoperatively. The incision–suture time ranged between 122.3 and 135.4 min (p < 0.001). Blood loss during surgery emerges as a significant predictor of wound healing (p < 0.002). The higher the blood loss, the more likely the risk of a wound healing disorder. The mean blood loss in patients with wound healing disorders was 217.9. In contrast, patients without wound complications exhibited significantly less blood loss, with an average of 125.1 mL (Table 3, Figure 1F).
For each conceivable cutoff of the variables, receiver operating characteristic (ROC) analyses compare their sensitivity and specificity in distinguishing between patients with and without wound healing disorders/infections. The criteria for differentiation stipulate that a higher value (above the cutoff) indicates wound healing disorders or infections. This assumption is only partially applicable to leukocytes (Figure 2A), as low values (<3.7) can also be pathological. However, these instances are infrequent compared to the values defined as pathological, which are >9.9, and have been disregarded. The area under the curve (AUC) for the CRP level (Figure 2B) is 0.625 (0.559; 0.692). The AUC for the ESR (Figure 2C) is 0.595 (0.520; 0.669). Both biomarkers are deemed unsuitable as diagnostic tools in this context. The best AUC is achieved for Hb (Figure 2D), although at 0.686 [0.629; 0.744], it does not exhibit a particularly high diagnostic quality.

4. Discussion

In this study, we conducted a retrospective analysis to identify and explore the factors influencing postoperative infections subsequent to spinal neurosurgical interventions over a decade. Comparatively, the incidence of infections following spinal neurosurgical procedures appears to be less frequent than those following cranial procedures in extant studies [18].
However, the reported infection rates in the literature exhibit wide variability, ranging from 0.9% to 5%, with a mean infection rate of 2.48% [19,20,21,22,23,24,25,26]. This variability can be attributed to differences in the patient populations across studies, as well as variations in surgeon experience, operative areas, and surgical techniques. The infection rate of wound infections in our study was 2.5%, aligning with the mean of corresponding studies.
Patients with wound healing disorders showed a slightly elevated CRP level, with an average value of 10.4 mg/L. In contrast, the CRP level in patients without wound complications was 4.8 mg/L. Elevated levels of the erythrocyte sedimentation rate, hemoglobin, and CRP have been indicative of an increased risk of infection [27,28,29]. The preoperative CRP value proves to be valuable in assessing the risk of postoperative infections [30,31,32,33,34]. This was also confirmed in our study. In another study, no significant influence of the CRP level on infection was observed [35]. It is pertinent to note that elevated CRP values may be present in various pre-existing diseases and conditions, as well as post-operative procedures unrelated to the manifested infections [16,36]. The ESR demonstrated a significant association with wound healing disorders and was approximately 25.5 mm higher in patients with complications compared to those without (18.3 mm). There is a significant body of literature critically assessing the ESR value for postoperative wound infection assessment [37,38]. Some publications suggest a specific postoperative pattern of ESRs, reporting a regular postoperative increase similar to CRP values and recommending critical evaluation if the value does not decrease after one week [39]. In the current study, ESR and CRP values were analyzed preoperatively as a benchmark for infection risk, and we observed that elevated levels of both factors are associated with increased infection risk. Despite the significant association of elevated CRP and ESR values with the development of wound healing disorders, the ROC analysis, which compares their sensitivity and specificity in distinguishing between patients with and without such disorders or infections, suggested that these two biomarkers may not be deemed suitable diagnostic aids for wound healing disorders.
While some studies emphasize the importance of inflammatory parameters such as leukocytes for the early detection of postoperative complications after lumbar disc surgery [40], our results did not show a statistically significant connection between leukocytes and wound healing disorders (p = 0.117). The leukocyte count in patients with wound complications was 8.9 gpt/L, compared to 8.4 gpt/L in those without complications. Our findings are in line with Jenny et al., who argued that leukocyte determination is not a meaningful indicator for the diagnosis of infection [38,41].
Obesity is widely recognized by most authors as a risk factor for postoperative infections after spinal surgery [10,42]. The data from our study align with this perspective: while only 1.76% of normal-weight patients experienced a postoperative wound infection, the rate increased to 3.52% for patients with grade I–III obesity. In the literature, the male gender is often associated with a higher risk of postoperative infections and is considered a predictive risk factor [43]. However, conflicting studies argue that sex may not have a significant influence on postoperative infections’ development [44,45]. In contrast to these findings, our study revealed that 1.08% of male patients developed a postoperative wound infection, whereas the rate was higher, at 3.93%, for female patients. In terms of percentages, the females experienced postoperative wound infections more frequently. Several studies [46,47] have demonstrated a positive correlation between age and the occurrence of postoperative wound infections after spinal surgery. This correlation is affirmed in our study, where the patients without wound complications were an average age of 60.8, while those with wound healing disorders were significantly older (67.8). The presence of previous illnesses or comorbidities has been linked to an increased rate of postoperative complications by various authors, and our data confirm this correlation [48,49]. The ASA score has been associated with postoperative wound infections in some studies [50,51]. In our study, the probability of postoperative wound healing disorders was 5.37% with an assigned ASA score of 3–4. Generally, a higher ASA score corresponds to a greater likelihood of a wound healing disorder.
Further studies [6,47,52] have shown that diabetes mellitus is a risk factor for postoperative wound infections. Among the 481 patients with insulin-dependent and insulin-independent diabetes mellitus in our study, 22 patients (4.57%) experienced a postoperative wound infection. The diabetic patients were more than twice as likely to develop a wound healing disorder (2.06% in non-diabetics vs. 4.57% in diabetics). In a study by Haleem et al. [53] involving 2309 patients (2.3% infection rate), after the decompression of the spine, arterial hypertension was shown to be a significant risk factor for the occurrence of postoperative wound infection, consistent with our study. Out of the 1640 patients with a prior diagnosis of arterial hypertension, 57 patients (an infection rate of 3.35%) experienced a nosocomial wound infection. A robust association between prior cardiac conditions and the occurrence of complications or difficulties in wound healing has been demonstrated [54,55]. Cardiovascular diseases negatively impact the wound healing process due to altered metabolic conditions and reduced tissue perfusion. In our study, patients with heart disease were more than twice as likely to develop a wound healing disorder compared to those without heart disease (4.35% for heart disease patients vs. 1.88% for heart-healthy patients). A crucial factor in the development of deep wound infections is an injury to the dura mater. This is a common complication of lumbar surgery, with an incidence ranging from 0.2 percent to 20 percent [56]. In our presented study, the complication rate of dural opening was 21.5 percent (535 patients). The risk of developing a wound healing disorder after dural opening is almost three times higher (5.04% with dural opening vs. 1.89% without dural opening). One possible explanation is the delayed mobilization of patients, leading to an increased risk of immobility-related complications or an additional stimulus for proliferative processes in the epidural space due to cerebrospinal fluid leakage.
As an additional intraoperative measure, synthetic materials were used in 22.95% of patients undergoing duraplasty as a necessary therapy for intraoperative durotomy. This prolonged the operation time and simultaneously led to a significantly increased risk of postoperative complications. The risk of developing a wound complication is more than two times higher when using exogenous materials (4.11% with synthetic materials vs. 1.94% without synthetic materials). The use of fibrin glue is discussed in the literature, but there is no evidence of an increased infection rate when using fibrin glue [47].
Our study revealed a significant correlation between the number of operated segments and the occurrence of postoperative wound infections (1.14% with one operated segment vs. 6.13% with at least three operated segments). In the literature, this criterion is described as a known risk factor for the development of wound complications after spinal surgery [7]. This is justified by the extended duration of the operation and the increased volume of damaged soft tissue, which could lead to postoperative complications.
A longer duration of the operation significantly increases the risk of infection [6,57], primarily due to increased bacterial wound colonization [58]. The duration of the operation depends on the intervention, the complexity, and the skill of the surgeon. In this study, the average duration of surgery in patients with wound healing disorders was 128.9 min, while patients without wound complications had a shorter operation time (102.6 min).
Our work demonstrated that patients with postoperative infections lose almost twice as much blood as patients without wound healing disorders (217.9 mL vs. 125.1 mL). As mentioned earlier, the literature links the duration of the operation to the volume of blood loss: the longer the operation, the higher the blood loss [7,47]. The risk of impaired wound healing is attributed to the reduction in hemoglobin levels, resulting in decreased tissue oxygenation.

5. Conclusions

Elevated inflammatory parameters, particularly preoperative CRP levels, were indicative of postoperative wound healing disorders or infections. However, it is important to note that these laboratory parameters cannot be considered predictive factors for wound healing complications. The determinants contributing to an elevated CRP level should be elucidated through a separate study. Identifying significant risk factors allows for targeted interventions aimed at infection prophylaxis in high-risk cases. Since the conclusion of our study, patients with elevated inflammatory parameters have had their elective surgery postponed until a thorough focus search is conducted, and, if necessary, preoperative antibiotic treatment is initiated.
In our view, a prospective, short-term, multicenter study should be conducted to gather data from a large cohort of cases and provide a more precise assessment. Subsequent data analyses from these various centers could aid in the development of guidelines.

Author Contributions

Conceptualization, A.R. and M.L.; methodology, A.R. and M.L.; software, SAS University Edition software package 9.4; validation, A.R., M.L. and I.E.S.; formal analysis, A.R., M.L. and I.E.S.; investigation, A.P., M.L. and A.R.; resources, A.P. and A.R.; data curation, A.P. and A.R.; writing—original draft preparation, A.P. and A.R.; writing—review and editing, A.P., M.L., B.N., C.A.D., K.P.S., I.E.S. and A.R.; visualization, A.P. and A.R.; supervision, I.E.S. and A.R.; project administration, M.L. and A.R.; funding acquisition, No Funding. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All studies were conducted in accordance with the declaration of Helsinki from 1975, revised in 2013, and were approved by the ethics committee of the Otto-von-Guericke University Magdeburg (R02-24).

Informed Consent Statement

All studies in this manuscript were conducted retrospectively on patient data routinely collected during patient care. The ethics committee provided a waiver of the need for informed consent.

Data Availability Statement

The datasets obtained and analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ciol, M.A.; Deyo, R.A.; Howell, E.; Kreif, S. An assessment of surgery for spinal stenosis: Time trends, geographic variations, complications, and reoperations. J. Am. Geriatr. Soc. 1996, 44, 285–290. [Google Scholar] [CrossRef]
  2. Stolke, D.; Sollmann, W.P.; Seifert, V. Intra- and postoperative complications in lumbar disc surgery. Spine 1989, 14, 56–59. [Google Scholar] [CrossRef] [PubMed]
  3. Behnke, M.; Hansen, S.; Leistner, R.; Diaz, L.A.; Gropmann, A.; Sohr, D.; Gastmeier, P.; Piening, B. Nosocomial infection and antibiotic use: A second national prevalence study in Germany. Dtsch. Arztebl. Int. 2013, 110, 627–633. [Google Scholar] [CrossRef]
  4. Patel, H.; Khoury, H.; Girgenti, D.; Welner, S.; Yu, H. Burden of Surgical Site Infections Associated with Select Spine Operations and Involvement of Staphylococcus aureus. Surg. Infect. 2017, 18, 461–473. [Google Scholar] [CrossRef]
  5. Petilon, J.M.; Glassman, S.D.; Dimar, J.R.; Carreon, L.Y. Clinical outcomes after lumbar fusion complicated by deep wound infection: A case-control study. Spine 2012, 37, 1370–1374. [Google Scholar] [CrossRef]
  6. Pull ter Gunne, A.F.; Cohen, D.B. Incidence, prevalence, and analysis of risk factors for surgical site infection following adult spinal surgery. Spine 2009, 34, 1422–1428. [Google Scholar] [CrossRef] [PubMed]
  7. Olsen, M.A.; Nepple, J.J.; Riew, K.D.; Lenke, L.G.; Bridwell, K.H.; Mayfield, J.; Fraser, V.J. Risk factors for surgical site infection following orthopaedic spinal operations. J. Bone Jt. Surg. Am. 2008, 90, 62–69. [Google Scholar] [CrossRef]
  8. Erman, T.; Demirhindi, H.; Göçer, A.I.; Tuna, M.; Ildan, F.; Boyar, B. Risk factors for surgical site infections in neurosurgery patients with antibiotic prophylaxis. Surg. Neurol. 2005, 63, 107–112; discussion 112–113. [Google Scholar] [CrossRef]
  9. Deng, H.; Chan, A.K.; Ammanuel, S.; Chan, A.Y.; Oh, T.; Skrehot, H.C.; Edwards, S.; Kondapavulur, S.; Nichols, A.D.; Liu, C.; et al. Risk factors for deep surgical site infection following thoracolumbar spinal surgery. J. Neurosurg. Spine 2019, 32, 292–301. [Google Scholar] [CrossRef]
  10. Fei, Q.; Li, J.; Lin, J.; Li, D.; Wang, B.; Meng, H.; Wang, Q.; Su, N.; Yang, Y. Risk Factors for Surgical Site Infection After Spinal Surgery: A Meta-Analysis. World Neurosurg. 2016, 95, 507–515. [Google Scholar] [CrossRef] [PubMed]
  11. Kobayashi, Y.; Inose, H.; Ushio, S.; Yuasa, M.; Hirai, T.; Yoshii, T.; Okawa, A. Body Mass Index and Modified Glasgow Prognostic Score Are Useful Predictors of Surgical Site Infection After Spinal Instrumentation Surgery: A Consecutive Series. Spine 2020, 45, E148–E154. [Google Scholar] [CrossRef] [PubMed]
  12. Chahoud, J.; Kanafani, Z.; Kanj, S.S. Surgical site infections following spine surgery: Eliminating the controversies in the diagnosis. Front. Med. 2014, 1, 7. [Google Scholar] [CrossRef]
  13. Aljabi, Y.; Manca, A.; Ryan, J.; Elshawarby, A. Value of procalcitonin as a marker of surgical site infection following spinal surgery. Surgeon 2019, 17, 97–101. [Google Scholar] [CrossRef]
  14. Kurokawa, Y.; Yamashita, K.; Kawabata, R.; Fujita, J.; Imamura, H.; Takeno, A.; Takahashi, T.; Yamasaki, M.; Eguchi, H.; Doki, Y. Prognostic value of postoperative C-reactive protein elevation versus complication occurrence: A multicenter validation study. Gastric Cancer 2020, 23, 937–943. [Google Scholar] [CrossRef]
  15. Chen, Y.; Zhao, Y.; Liu, J.; Teng, Y.; Ou, M.; Hao, X. Predictive value of perioperative procalcitonin, C reactive protein and high-sensitivity C reactive protein for the risk of postoperative complications after non-cardiac surgery in elderly patients: A nested case-control study. BMJ Open 2023, 13, e071464. [Google Scholar] [CrossRef] [PubMed]
  16. Hoeller, S.; Roch, P.J.; Weiser, L.; Hubert, J.; Lehmann, W.; Saul, D. C-reactive protein in spinal surgery: More predictive than prehistoric. Eur. Spine J. 2021, 30, 1261–1269. [Google Scholar] [CrossRef]
  17. Mujagic, E.; Marti, W.R.; Coslovsky, M.; Zeindler, J.; Staubli, S.; Marti, R.; Mechera, R.; Soysal, S.D.; Gürke, L.; Weber, W.P. The role of preoperative blood parameters to predict the risk of surgical site infection. Am. J. Surg. 2018, 215, 651–657. [Google Scholar] [CrossRef]
  18. Gaynes, R.P.; Culver, D.H.; Horan, T.C.; Edwards, J.R.; Richards, C.; Tolson, J.S. Surgical site infection (SSI) rates in the United States, 1992–1998: The National Nosocomial Infections Surveillance System basic SSI risk index. Clin. Infect. Dis. 2001, 33 (Suppl. S2), S69–S77. [Google Scholar] [CrossRef]
  19. Abbey, D.M.; Turner, D.M.; Warson, J.S.; Wirt, T.C.; Scalley, R.D. Treatment of postoperative wound infections following spinal fusion with instrumentation. J. Spinal Disord. 1995, 8, 278–283. [Google Scholar] [CrossRef] [PubMed]
  20. Picada, R.; Winter, R.B.; Lonstein, J.E.; Denis, F.; Pinto, M.R.; Smith, M.D.; Perra, J.H. Postoperative deep wound infection in adults after posterior lumbosacral spine fusion with instrumentation: Incidence and management. J. Spinal Disord. 2000, 13, 42–45. [Google Scholar] [CrossRef]
  21. Wimmer, C.; Gluch, H.; Franzreb, M.; Ogon, M. Predisposing factors for infection in spine surgery: A survey of 850 spinal procedures. J. Spinal Disord. 1998, 11, 124–128. [Google Scholar] [CrossRef]
  22. Weinstein, M.A.; McCabe, J.P.; Cammisa, F.P., Jr. Postoperative spinal wound infection: A review of 2,391 consecutive index procedures. J. Spinal Disord. 2000, 13, 422–426. [Google Scholar] [CrossRef] [PubMed]
  23. Petignat, C.; Francioli, P.; Harbarth, S.; Regli, L.; Porchet, F.; Reverdin, A.; Rilliet, B.; de Tribolet, N.; Pannatier, A.; Pittet, D.; et al. Cefuroxime prophylaxis is effective in noninstrumented spine surgery: A double-blind, placebo-controlled study. Spine 2008, 33, 1919–1924. [Google Scholar] [CrossRef]
  24. Kanafani, Z.A.; Dakdouki, G.K.; El-Dbouni, O.; Bawwab, T.; Kanj, S.S. Surgical site infections following spinal surgery at a tertiary care center in Lebanon: Incidence, microbiology, and risk factors. Scand. J. Infect. Dis. 2006, 38, 589–592. [Google Scholar] [CrossRef]
  25. Kang, B.U.; Lee, S.H.; Ahn, Y.; Choi, W.C.; Choi, Y.G. Surgical site infection in spinal surgery: Detection and management based on serial C-reactive protein measurements. J. Neurosurg. Spine 2010, 13, 158–164. [Google Scholar] [CrossRef]
  26. Blam, O.G.; Vaccaro, A.R.; Vanichkachorn, J.S.; Albert, T.J.; Hilibrand, A.S.; Minnich, J.M.; Murphey, S.A. Risk factors for surgical site infection in the patient with spinal injury. Spine 2003, 28, 1475–1480. [Google Scholar] [CrossRef] [PubMed]
  27. Jiang, W.; Shi, H.; Deng, X.; Hou, W.; Wan, D. The incidence of incision infections after lumbar fusion and the significance of dynamically monitoring serum albumin and C-reactive protein levels. Ann. Palliat. Med. 2021, 10, 10870–10877. [Google Scholar] [CrossRef]
  28. Sharouf, F.; Hussain, R.N.; Hettipathirannahelage, S.; Martin, J.; Gray, W.; Zaben, M. C-reactive protein kinetics post elective cranial surgery. A prospective observational study. Br. J. Neurosurg. 2020, 34, 46–50. [Google Scholar] [CrossRef]
  29. Fujita, R.; Takahata, M.; Kokabu, T.; Oda, I.; Kajino, T.; Hisada, Y.; Takeuchi, H.; Iwasaki, N. Retrospective study to evaluate the clinical significance of a second rise in C-reactive protein level following instrumented spinal fusion surgery. J. Orthop. Sci. 2019, 24, 963–968. [Google Scholar] [CrossRef]
  30. Fransen, E.J.; Maessen, J.G.; Elenbaas, T.W.; van Aarnhem, E.E.; van Dieijen-Visser, M.P. Enhanced preoperative C-reactive protein plasma levels as a risk factor for postoperative infections after cardiac surgery. Ann. Thorac. Surg. 1999, 67, 134–138. [Google Scholar] [CrossRef]
  31. Boeken, U.; Feindt, P.; Zimmermann, N.; Kalweit, G.; Petzold, T.; Gams, E. Increased preoperative C-reactive protein (CRP)-values without signs of an infection and complicated course after cardiopulmonary bypass (CPB)-operations. Eur. J. Cardiothorac. Surg. 1998, 13, 541–545. [Google Scholar] [CrossRef] [PubMed]
  32. Haupt, W.; Hohenberger, W.; Mueller, R.; Klein, P.; Christou, N.V. Association between preoperative acute phase response and postoperative complications. Eur. J. Surg. 1997, 163, 39–44. [Google Scholar] [PubMed]
  33. Vinnes, E.W.; Soldal Lillemoen, P.K.; Persson, R.M.; Meyer, K.; Haaverstad, R.; Bjørke-Monsen, A.L. A novel case of impaired C-reactive protein response following open-heart surgery: A case report and review of the literature. Clin. Chim. Acta 2021, 520, 196–201. [Google Scholar] [CrossRef] [PubMed]
  34. Sahin, V.; Akpinar, M.B.; Sevim, E.; Uyar, I.S.; Abacilar, A.F.; Uc, H.; Tetik, F.; Damar, E.; Okur, F.F.; Alayunt, E.A. Preoperative CRP levels is not predictive early renal dysfunction after coronary artery bypass surgery. Int. J. Clin. Exp. Med. 2015, 8, 4146–4151. [Google Scholar] [PubMed]
  35. Cui, P.; Wang, P.; Hu, X.; Kong, C.; Lu, S. Comparison of Perioperative Outcomes in Patients Undergoing Short-Level Lumbar Fusion Surgery after Implementing Enhanced Recovery after Surgery: A Propensity Score Matching Analysis Focusing on Young-Old and Old-Old. Clin. Interv. Aging 2022, 17, 1793–1801. [Google Scholar] [CrossRef] [PubMed]
  36. Kim, J.H.; Ha, S.W.; Choi, J.G.; Son, B.C. Chronological Changes of C-Reactive Protein Levels following Uncomplicated, Two-Staged, Bilateral Deep Brain Stimulation. J. Korean Neurosurg. Soc. 2015, 58, 368–372. [Google Scholar] [CrossRef] [PubMed]
  37. Okafor, B.; MacLellan, G. Postoperative changes of erythrocyte sedimentation rate, plasma viscosity and C-reactive protein levels after hip surgery. Acta Orthop. Belg. 1998, 64, 52–56. [Google Scholar] [PubMed]
  38. Jenny, J.Y.; Gaudias, J.; Bourguignat, A.; Férard, G.; Kempf, I. C-reactive protein and transthyretin in early diagnosis of infection after open fractures of the lower limbs (a preliminary study). Rev. Chir. Orthop. Reparatrice Appar. Mot. 1999, 85, 321–327. [Google Scholar] [PubMed]
  39. Margheritini, F.; Camillieri, G.; Mancini, L.; Mariani, P.P. C-reactive protein and erythrocyte sedimentation rate changes following arthroscopically assisted anterior cruciate ligament reconstruction. Knee Surg. Sports Traumatol. Arthrosc. 2001, 9, 343–345. [Google Scholar] [CrossRef]
  40. Ji, L.S.; Lu, T.S.; Wang, Y.P.; Jia, Y.J.; Yang, J.W.; Ma, Y.; Liu, H.E.; Luo, C.S. The role of lymphocyte count in the early diagnosis of surgical site infection following posterior lumbar fusion. Eur. Rev. Med. Pharmacol. Sci. 2023, 27, 3941–3946. [Google Scholar] [CrossRef]
  41. Abt, N.B.; Sethi, R.K.; Puram, S.V.; Varvares, M.A. Preoperative laboratory data are associated with complications and surgical site infection in composite head and neck surgical resections. Am. J. Otolaryngol. 2018, 39, 261–265. [Google Scholar] [CrossRef]
  42. Gerometta, A.; Rodriguez Olaverri, J.C.; Bitan, F. Infections in spinal instrumentation. Int. Orthop. 2012, 36, 457–464. [Google Scholar] [CrossRef]
  43. Levi, A.D.; Dickman, C.A.; Sonntag, V.K. Management of postoperative infections after spinal instrumentation. J. Neurosurg. 1997, 86, 975–980. [Google Scholar] [CrossRef] [PubMed]
  44. Lietard, C.; Thébaud, V.; Besson, G.; Lejeune, B. Risk factors for neurosurgical site infections: An 18-month prospective survey. J. Neurosurg. 2008, 109, 729–734. [Google Scholar] [CrossRef] [PubMed]
  45. Watanabe, M.; Sakai, D.; Matsuyama, D.; Yamamoto, Y.; Sato, M.; Mochida, J. Risk factors for surgical site infection following spine surgery: Efficacy of intraoperative saline irrigation. J. Neurosurg. Spine 2010, 12, 540–546. [Google Scholar] [CrossRef] [PubMed]
  46. Christodoulou, A.G.; Givissis, P.; Symeonidis, P.D.; Karataglis, D.; Pournaras, J. Reduction of postoperative spinal infections based on an etiologic protocol. Clin. Orthop. Relat. Res. 2006, 444, 107–113. [Google Scholar] [CrossRef] [PubMed]
  47. Fang, A.; Hu, S.S.; Endres, N.; Bradford, D.S. Risk factors for infection after spinal surgery. Spine 2005, 30, 1460–1465. [Google Scholar] [CrossRef]
  48. Cavaillon, J.M. Exotoxins and endotoxins: Inducers of inflammatory cytokines. Toxicon 2018, 149, 45–53. [Google Scholar] [CrossRef]
  49. Grammatico-Guillon, L.; Baron, S.; Rosset, P.; Gaborit, C.; Bernard, L.; Rusch, E.; Astagneau, P. Surgical site infection after primary hip and knee arthroplasty: A cohort study using a hospital database. Infect. Control Hosp. Epidemiol. 2015, 36, 1198–1207. [Google Scholar] [CrossRef]
  50. Rao, S.B.; Vasquez, G.; Harrop, J.; Maltenfort, M.; Stein, N.; Kaliyadan, G.; Klibert, F.; Epstein, R.; Sharan, A.; Vaccaro, A.; et al. Risk factors for surgical site infections following spinal fusion procedures: A case-control study. Clin. Infect. Dis. 2011, 53, 686–692. [Google Scholar] [CrossRef]
  51. Lai, Q.; Song, Q.; Guo, R.; Bi, H.; Liu, X.; Yu, X.; Zhu, J.; Dai, M.; Zhang, B. Risk factors for acute surgical site infections after lumbar surgery: A retrospective study. J. Orthop. Surg. Res. 2017, 12, 116. [Google Scholar] [CrossRef] [PubMed]
  52. Veeravagu, A.; Patil, C.G.; Lad, S.P.; Boakye, M. Risk factors for postoperative spinal wound infections after spinal decompression and fusion surgeries. Spine 2009, 34, 1869–1872. [Google Scholar] [CrossRef] [PubMed]
  53. Haleem, A.; Chiang, H.Y.; Vodela, R.; Behan, A.; Pottinger, J.M.; Smucker, J.; Greenlee, J.D.; Clark, C.; Herwaldt, L.A. Risk Factors for Surgical Site Infections Following Adult Spine Operations. Infect. Control Hosp. Epidemiol. 2016, 37, 1458–1467. [Google Scholar] [CrossRef] [PubMed]
  54. Janssen-Heijnen, M.L.; Maas, H.A.; Houterman, S.; Lemmens, V.E.; Rutten, H.J.; Coebergh, J.W. Comorbidity in older surgical cancer patients: Influence on patient care and outcome. Eur. J. Cancer 2007, 43, 2179–2193. [Google Scholar] [CrossRef] [PubMed]
  55. Hecker, A.; Hecker, B.; Schwandner, T.; Hecker, M.; Weigand, M.; Padberg, W. Chirurgie bei Patienten mit Vorerkrankungen und Schwangeren. Allg. Visz. Up2date 2012, 6, 231–247. [Google Scholar] [CrossRef]
  56. Takenaka, S.; Makino, T.; Sakai, Y.; Kashii, M.; Iwasaki, M.; Yoshikawa, H.; Kaito, T. Dural tear is associated with an increased rate of other perioperative complications in primary lumbar spine surgery for degenerative diseases. Medicine 2019, 98, e13970. [Google Scholar] [CrossRef] [PubMed]
  57. Valentini, L.G.; Casali, C.; Chatenoud, L.; Chiaffarino, F.; Uberti-Foppa, C.; Broggi, G. Surgical site infections after elective neurosurgery: A survey of 1747 patients. Neurosurgery 2008, 62, 88–95; discussion 95–96. [Google Scholar] [CrossRef]
  58. Mastronardi, L.; Tatta, C. Intraoperative antibiotic prophylaxis in clean spinal surgery: A retrospective analysis in a consecutive series of 973 cases. Surg. Neurol. 2004, 61, 129–135; discussion 135. [Google Scholar] [CrossRef]
Figure 1. Boxplots corresponding to Table 3 depict continuous parameters in relation to wound healing disorders (WHDs). Laboratory parameters and operative characteristics that influence wound healing after surgery are C-reactive protein (A), erythrocyte sedimentation rate (B), leukocytes (C), hemoglobin (D), duration of surgery (E) and blood loss (F).
Figure 1. Boxplots corresponding to Table 3 depict continuous parameters in relation to wound healing disorders (WHDs). Laboratory parameters and operative characteristics that influence wound healing after surgery are C-reactive protein (A), erythrocyte sedimentation rate (B), leukocytes (C), hemoglobin (D), duration of surgery (E) and blood loss (F).
Jpm 14 00667 g001
Figure 2. ROC analyses demonstrate sensitivity and specificity for distinguishing patients with and without wound healing disorders/infections using key laboratory parameters. The biomarkers leukocytes (A), C-reactive protein (B) and erythrocyte sedimentation rate (C) are considered as unsuitable diagnostic tools in this context. The best AUC is achieved for hemoglobin (D), although it does not have a particularly high diagnostic quality.
Figure 2. ROC analyses demonstrate sensitivity and specificity for distinguishing patients with and without wound healing disorders/infections using key laboratory parameters. The biomarkers leukocytes (A), C-reactive protein (B) and erythrocyte sedimentation rate (C) are considered as unsuitable diagnostic tools in this context. The best AUC is achieved for hemoglobin (D), although it does not have a particularly high diagnostic quality.
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Table 1. Among the demographic data and comorbidities, sex, age, ASA classification, hypertension, diabetes, and cardiovascular diseases have a significant influence on postoperative wound healing disorders. Among the operative characteristics, the following have a significant influence on postoperative wound healing disorders: (injury of the dura during surgery; number of operating segments; use of synthetic materials; duration of the operation [minutes]; blood loss during surgery [mL]).
Table 1. Among the demographic data and comorbidities, sex, age, ASA classification, hypertension, diabetes, and cardiovascular diseases have a significant influence on postoperative wound healing disorders. Among the operative characteristics, the following have a significant influence on postoperative wound healing disorders: (injury of the dura during surgery; number of operating segments; use of synthetic materials; duration of the operation [minutes]; blood loss during surgery [mL]).
Parameters Wound Healing Disorder/InfectionNo Wound Healing Disorder/Infectionp-Value
N (%)N (%)
Mean ± SDMean ± SD
Demographic data
Sex Female57 (77.03)1392 (47.27)<0.001
Male17 (22.97)1553 (52.73)
Age 74 (67.8 ± 11.7)2929 (60.8 ± 14.8)<0.001
ASA Classification I1 (1.39)361 (12.30)<0.001
II31 (43.06)1869 (63.70)
III–IV40 (55.56)704 (23.99)
Smoker Yes13 (17.57)741 (25.16)0.173
No61 (82.43)2204 (74.84)
BMI Normal weight12 (16.22)679 (23.08)0.101
Pre-obesity25 (33.78)1185 (40.28)
Obesity grade I–III37 (50.00)1049 (35.66)
Comorbidities
Diabetes Typ I0 (0.00)9 (0.31)0.012
Typ II22 (29.73)472 (16.03)
No52 (70.27)2464 (83.67)
Hypertension Yes57 (77.03)1640 (55.69)<0.001
No17 (22.97)1305 (44.31)
Cardiovascular diseases Yes30 (40.54)659 (22.38)<0.001
No44 (59.46)2286 (77.62)
Chronic inflammation Yes8 (10.81)170 (5.77)0.078
No66 (89.19)2775 (94.23)
Operative Characteristics
Injury of the dura during surgery Yes27 (36.49)508 (17.25)<0.001
No47 (63.51)2437 (82.75)
Number of operating segments 117 (23.29)1466 (49.93)<0.001
240 (54.79)1225 (41.72)
≥316 (21.92)245 (8.34)
Use of synthetic materials Yes29 (39.19)676 (22.95)0.002
No45 (60.81)2269 (77.05)
Duration of the operation [minutes] 72 (128.9)2655 (125.1)<0.001
[122.3; 135.4][63.1; 187.0]
Blood loss during surgery [mL] 67 (217.9)2655 (125.1)0.002
[140.1; 295.6][63.1; 187.0]
Table 2. Previous illnesses with elevated CRP values (>10.5 mg/L), including overlapping conditions. Among patients with arterial hypertension, 34.36% also had diabetes mellitus, while 12.27% were additionally affected by chronic inflammation.
Table 2. Previous illnesses with elevated CRP values (>10.5 mg/L), including overlapping conditions. Among patients with arterial hypertension, 34.36% also had diabetes mellitus, while 12.27% were additionally affected by chronic inflammation.
IllnessesN%
Arterial hypertension32666.8
Chronic inflammation14529.7
Diabetes mellitus12325
Dyslipoproteinemia40.82
Patients without previous illnesses13728
Table 3. Laboratory parameters and operative characteristics that influence wound healing after surgery. Giga particle (Gpt).
Table 3. Laboratory parameters and operative characteristics that influence wound healing after surgery. Giga particle (Gpt).
Wound Healing Disorder/Infection
N/Mean [SD]
YesNop-Value
Duration of the surgery [minutes]72/128.9 [122.3; 135.4]2939/102.6 [96.5; 108.7]<0.001
Blood loss [mL]67/217.9 [140.1; 295.6]2655/125.1 [63.1; 187.0]0.002
Blood sedimentation rate [mm]62/25.5 [20.6; 30.5]2352/18.3 [14.6; 21.9]0.008
Hemoglobin [mmol/L]73/7.9 [7.9; 8.0]2925/8.6 [8.6; 8.6]<0.001
C-reactive protein [mg/L]74/10.4 [1.2; 19.6]2921/4.8 [1.3; 8.2]0.004
Leukocytes [gpt/L]74/8.9 [8.6; 9.1]2929/8.4 [8.2; 8.5]0.117
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Pinchuk, A.; Luchtmann, M.; Neyazi, B.; Dumitru, C.A.; Stein, K.P.; Sandalcioglu, I.E.; Rashidi, A. Is an Elevated Preoperative CRP Level a Predictive Factor for Wound Healing Disorders following Lumbar Spine Surgery? J. Pers. Med. 2024, 14, 667. https://doi.org/10.3390/jpm14070667

AMA Style

Pinchuk A, Luchtmann M, Neyazi B, Dumitru CA, Stein KP, Sandalcioglu IE, Rashidi A. Is an Elevated Preoperative CRP Level a Predictive Factor for Wound Healing Disorders following Lumbar Spine Surgery? Journal of Personalized Medicine. 2024; 14(7):667. https://doi.org/10.3390/jpm14070667

Chicago/Turabian Style

Pinchuk, Anatoli, Michael Luchtmann, Belal Neyazi, Claudia A. Dumitru, Klaus Peter Stein, Ibrahim Erol Sandalcioglu, and Ali Rashidi. 2024. "Is an Elevated Preoperative CRP Level a Predictive Factor for Wound Healing Disorders following Lumbar Spine Surgery?" Journal of Personalized Medicine 14, no. 7: 667. https://doi.org/10.3390/jpm14070667

APA Style

Pinchuk, A., Luchtmann, M., Neyazi, B., Dumitru, C. A., Stein, K. P., Sandalcioglu, I. E., & Rashidi, A. (2024). Is an Elevated Preoperative CRP Level a Predictive Factor for Wound Healing Disorders following Lumbar Spine Surgery? Journal of Personalized Medicine, 14(7), 667. https://doi.org/10.3390/jpm14070667

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