In this work, we developed and validated multiple ML algorithms to predict the risk of SSI following MIS-TLIF under the Quadrant channel. We found that the NB model provided improved predictive ability compared to other models. This study produced interesting findings concerning the added value of machine-learning methods, contrary to some of the wider literature on clinical prediction models. This ML-based model may potentially assist clinical decision-making when assessing and advising patients after MIS-TLIF under the Quadrant channel and when deciding on ongoing management.
MIS-TLIF is well known to have many advantages compared to open TLIF in terms of reducing muscle injury, smaller incisions, and scarring, especially for elderly patients who can not bear the trauma of traditional open TLIF surgery[20–22]. In contrast, SSI after MIS-TLIF can cause additional prolonged antibiotic use, prolonged length of stay, increased risk of fusion failure, and even the need for re-invasive surgery or removal of the implant[23, 24], which not only increases the medical burden on patients and society but also causes increased mortality in patients.
With the continuous development of minimally invasive spine surgery, MIS-TLIF has gradually attracted the attention of spine surgeons. It has been reported that the incidence of SSI after lumbar interbody fusion is 2.4–5%[25–28]. In this investigation, the incidence of SSI was 4.78, which was consistent with the previous report. Although risk factors associated with SSI after MIS-TLIF have been previously reported, little attention has been paid to the incidence of SSI following MIS-TLIF under the Quadrant channel. More importantly, we still lack a concise and reliable tool to identify high-risk groups and assist clinicians in their decision-making. Therefore, in this study, several ML-based models were constructed using AI techniques to predict the risk of SSI.
Many studies have shown that diabetes mellitus is an important risk factor for SSI after spine surgery[23, 29–31]. Usually, glucose metabolism is closely related to the structure and pathophysiology of blood vessels. Disorders of glucose metabolism are inextricably linked to vascular structure and dysfunction, as has been demonstrated in many studies. And vascular pathological alterations cause local microcirculatory disorders leading to tissue ischemia and hypoxia, which provide a favorable environment for the growth of anaerobic bacteria and pose a potential risk for delayed postoperative wound healing and surgical site infection. Also, systemic immune deficiency caused by diabetes is the main reason for the occurrence of SSI. Therefore, surgeons usually strictly control patients' perioperative blood glucose and control preoperative fasting glucose below 6.9 mmol/L before considering surgical treatment[32]. Interestingly, glycosylated hemoglobin has also been identified as a risk factor for SSI. Chronic hyperglycemic state leads to cell-mediated immune and macrophage dysfunction, reduced overall immune function of the body, and diminished ability to respond to bacterial viruses. And the relative importance in our prediction model was ranked first. Therefore, we need to be more cautious about the perioperative glycemic management of patients.
In the present study, we found that high BMI is also a risk factor for SSI. In a large retrospective study by Onyekwelu et al.[33], BMI > 30 kg/m2 was found to be a high-risk factor for SSI after lumbar spinal fusion. The problems caused by obesity are present in almost all orthopedic surgeries, not only in spine surgery. Obesity can lead to endocrine disruption and insulin resistance, causing impaired glucose tolerance and even diabetes. On the other hand, obese patients may have a variety of underlying diseases that cause a decrease in the patient's immune capacity. Equally worrisome is the increased vascularity in the adipose tissue of obese patients and the even greater incidence of postoperative fat liquefaction, which also increases the risk of SSI.
Intriguingly, age was also identified as an independent risk factor in this study. However, we are also aware that this is a non-modifiable risk factor. Previous studies reported that the risk of SSI after spinal surgery was three times higher in patients aged ≥ 60 years than in the younger group[1, 27]. Similar results were observed in the present study. Although it is still controversial whether advanced age is a risk factor for SSI, the current mainstream of scholars believe that elderly patients have reduced metabolism, degenerated immune function, and may be accompanied by numerous underlying diseases, which significantly reduce the reserve capacity of the organism and make it more sensitive to surgical trauma strikes. The ability of local tissues to resist infection as well as self-repair is weakened, increasing the risk of SSI following spine surgery.
Although univariate logistics regression analysis indicated a statistical difference in operation time and EBL between the two groups (P < 0.05), in multivariate logistic regression analysis, we found that EBL and operation time were identified as risk factors for SSI. It has been reported in the literature that prolonged operation time is a significant risk factor for SSI that can not be ignored. Prolonged operation time leads to increased tissue stretching time, aggravating local ischemia and tissue inflammatory factor release, while prolonged surgical wound exposure leads to an increased possibility of direct blood entry of pathogenic bacteria from the wound. Whereas the relationship between EBL and SSI has been debatable. According to previous literature, intraoperative blood transfusion > 1000 ml was reported as a risk factor for postoperative SSI in spine surgery and cardiac surgery[7, 27]. Pullter et al.[33] investigated 3175 patients undergoing spine surgery and found that the incidence of postoperative SSI was 2.5% in patients with intraoperative bleeding < 1000 ml, and 3.5% in patients with intraoperative bleeding > 1000 ml. The incidence of SSI was 5.6% (p = 0.001) and was identified as an independent risk factor for SSI.
In the present study, we found that low serum albumin was an important risk factor for SSI. Serum albumin level is closely related to the nutritional status, liver and kidney function of patients. Hypoproteinemia will lead to immune suppression, local tissue edema, impaired microcirculation, and increased tissue fluid exudation, which will affect normal wound healing and create conditions for bacterial colonization. Previous literature has reported preoperative hypocalcemia as a risk factor for postoperative spinal infection[30], and another interesting finding is that electrolyte variables such as blood calcium and potassium in this investigation were not statistically significantly different between the SSI and Non-SSI groups. Patients with toxic shock syndrome are usually found to suffer from hypocalcemia, which may be due to an increase in calcitonin secretion caused by the infection, resulting in lower blood calcium levels[34]. Many previous studies have reported hypocalcemia in approximately 40% of patients with bacteremia[35]. According to the inclusion and exclusion criteria of our study, none of the patients in the 2 groups had a significant active infection before surgery, so it is not difficult to explain why there was no statistically significant difference in preoperative blood calcium between the 2 groups.
Compared with other research attempting to determine the risk of SSI following MIS-TLIF, our work has several advantages. First, a few studies focused on patients undergoing MIS-TLIF under the Quadrant channel. This may provide a reference for spine surgeons. Furthermore, while ML approaches have shown unparalleled prediction performance in MIS-TLIF under the Quadrant channel in recent reports, There is, however, little research in the available literature on applying ML algorithms to SSI following MIS-TLIF. As far as we know, this is the first study to develop a prediction model using ML algorithms for risk evaluation of SSI with easy-to-use clinical and laboratory data. Fortunately, our prediction model evidences good predictive performance of the model, which distinguishes itself from linear models adopted by previous research.
In this study, several limitations of this study require attention. First, the nature of a retrospective study might have introduced a selection bias. Second, the ML algorithm model we established, to some extent, was confined to one single center, which might restrict its generalizability pending further validation in real-world scenarios. Third, further independent external validation is required to confirm these findings.