PKP has led to symptom reduction, functional recovery and improved quality of life in OVCF patients. However, there are still postoperative complications that cannot be ignored, and NVCF is one of the most common complications. It is increasingly important to better anticipate and reduce the occurrence of complications in advance. Therefore, accurate predictive models are needed to help clinicians and patients share decisions and understand the risk of postoperative complications. With advances in the field of artificial intelligence, machine learning is often able to perform better than traditional linear models [12, 13].
Machine learning uses computer algorithms to learn complex relationships or patterns between data from large amounts of data, recognizes the data by training existing algorithms to perform many operations, and iteratively changes the algorithms to achieve optimal performance, resulting in models that relate multiple feature variables to the target variable . Specifically, supervised machine learning identifies relationships between input and output data (i.e. the computer learns from patient data) to produce outcome predictions based on the input data . Clinicians can use this AI-based strategy to help them choose more rational treatment options. ML has the advantage of being highly capable, objective and reproducible when dealing with large datasets with reliable results . It also has the potential to improve the accuracy of early diagnosis, determine disease progression, and improve the ability to predict patient outcomes, such as the risk of complications . These advantages can facilitate the sharing of information for decision-making between clinicians and patients and keep track of disease progression . Machine learning improves the efficacy of predicting clinical outcome metrics by constructing different algorithms for evaluation and comparison. Machine learning algorithms include support vector machines, random forests, gradient boosting machines, neural networks, and deep learning, in addition to traditional logistic regression and decision trees, which have been extended on this basis . With the availability of computers and large clinical datasets, machine learning as a form of artificial intelligence has started to be used in clinical settings to assist in medical decision-making.
PKP has become a common treatment for OVCFs, but the incidence of postoperative NVCFs can be as high as 50% . In a random forest-based ranking of the importance of variables, patients with a shorter time to fracture were at greater risk of postoperative NVCF (Fig. 3), and patients with a longer time to injury had a greater increase in bone formation markers than bone resorption markers . Progressive degeneration may increase the lumbar BMD over time compared with lower lumbar BMD values and a longer duration of back pain in patients hospitalized for surgery within a short period of time after symptom onset. Fresher fractures may cause oedema or haematoma around and within the fractured vertebra and may lead to prolonged back pain and poor functional recovery. A review  showed that for patients with OVCF within 6 weeks, PKP did not demonstrate a significant benefit compared to placebo. A meta-analysis by Buchbinder et al.  reported no significant differences in pain, disability, quality of life, or outcomes with PKP compared to sham surgery. Therefore, from a clinical perspective, the treatment goals for patients with fresh fractures should focus on strategies that first improve functional recovery, relieve back pain, and increase lumbar spine BMD values.
In this study, previous fracture history [OR = 12.298, 95% CI (6.250–24.199), P = 0.000] was the most effective predictor of postoperative fracture risk. Many patients with clinically asymptomatic previous fractures are found to have a history of previous vertebral fractures on imaging at the next visit to the hospital when the fracture arises, and therefore, patients do not recognize the danger of osteoporosis until symptoms develop. It has been reported  that individuals with a history of clinically diagnosed fractures or radiographic evidence of vertebral fracture patterns are at increased risk for hip, spine, and other fractures. It was also reported that approximately half of women with fracture patterns did not report having back pain, and approximately two-thirds had no previous clinical diagnosis of fracture. A study by Torgerson et al.  examined the association between multiple fractures and secondary fractures, with a 5.9-fold increased risk of secondary fractures in women with two or more fractures. This suggests that the risk of a new fracture following the presence of multiple prior fractures is greatly increased. Therefore, patients with a history of fractures should undergo further evaluation for osteoporosis and fracture risk.
The degree of osteoporosis is a major risk factor for the development of postoperative NVCF, and bone mineral density, an index to assess the mineral content of bone, is commonly used to diagnose osteoporosis . Ning et al.  found that by including 921 cases with low T values, bone trabeculae that were denser became sparse, leading to reduced bone support and toughness, an increased risk of fracture postoperatively and a greater risk of NVCF. It has been suggested that the progression of osteoporosis is associated with the development of postoperative NVCFs  and that anti-osteoporosis treatment may slow the progression of osteoporosis and prevent the development of NVCFs [27–29]. A 3-year follow-up study by Bawa et al.  showed that effective anti-osteoporosis treatment significantly reduced the incidence of postoperative VCFs. Multifactorial analysis showed that ineffective anti-osteoporosis treatment was a significant risk factor for NVCFs after PKP surgery. Therefore, anti-osteoporosis therapy should be used as a routine treatment after PKP in patients with OVCFs to reduce the incidence of refracture.
Leakage of bone cement through the ruptured endplate into the intervertebral disc causes changes in the surrounding vertebral body stresses and changes in the stiffness of the injured vertebral body due to reinforcement of the injured vertebral body but has a limited effect on the adjacent vertebral body after cushioning by the disc. However, when bone cement leaks into the intervertebral disc, it can increase the stress on the endplate of the adjacent vertebral body, and this alteration may increase the risk of new vertebral fractures . A study by Nieuwenhuijse et al.  found a significant association between bone cement leakage into the disc and the occurrence of postoperative NVCF. Multifactorial analysis in this study showed that leakage of bone cement into the intervertebral disc [OR = 2.501, 95% CI (1.029–6.082), P = 0.043] was a risk factor positively associated with new fractures. In addition, the heat generated by the bone cement leaking into the intervertebral disc may cause some damage to the disc, which may also be a major contributor to accelerated disc degeneration .
Oestrogen can directly affect bone metabolism by regulating cellular physiological functions. The decrease in oestrogen levels in postmenopausal women inevitably leads to the weakening of its inhibitory effect on osteoclasts, an increase in the number of osteoclasts, a decrease in apoptosis, and the prolongation of lifespan, which enhances bone resorption and promotes the progression of osteoporosis. Although osteoblast-mediated bone formation was also increased, it was not sufficient to compensate for excessive bone resorption. Active and unbalanced bone remodelling leads to thinning or fracture of trabecular bone, increased cortical bone porosity leads to decreased bone strength, and decreased oestrogen reduces bone sensitivity to mechanical stimulation, resulting in bone exhibiting pathological changes such as disuse bone loss.  A multicentre large-sample cohort study on the prevalence of osteoporosis in Chinese individuals by Zeng et al.  found that the number of women suffering from osteoporosis is much greater than that of men. In the USA, approximately 1 in 2 white women or 1 in 5 men will experience an osteoporosis-related fracture in their lifetime . However, in a large cross-sectional study by Wang et al. 18, it was found that in China, 5.0% of men and 20.6% of women aged 40 or older had osteoporosis, and 10.5% of men and 9.7% of men aged 40 or older had vertebral fractures. The similar prevalence of vertebral fractures in men and women suggests that we should also pay attention to the prevention and treatment of osteoporosis in men.
Multivariate analysis showed that the presence of cerebrovascular disease [OR = 28.522, 95% CI (8.749–92.989), P = 0.000] was associated with a higher risk of postoperative NVCF. A study by Tanislav et al.  showed that the occurrence of stroke as well as transient cerebral ischaemia was positively associated with fracture. Various adverse outcomes, such as depression, pain and reduced quality of life following stroke occurrence, lead to a higher risk of falls and fractures . A large cohort study by Wang et al.  found that patients had a risk of fracture of more than 8% 5 years after stroke occurrence and that stroke was significantly associated with fracture risk. Stroke in certain vascular regions of the brainstem can lead to impaired body balance and increased risk of falls . In addition, impairment of visual, motor, sensory or cognitive function after the onset of cerebrovascular disease may also lead to fall-related injuries . Within 2 years after stroke, 60.7% of fallers experienced a second or multiple falls, and 23.4% of patients had a fracture . In addition to falls, the accelerated decrease in bone mineral density after stroke may lead to fractures in stroke patients . Poststroke muscle weakness leads to limited weight bearing and reduced activity of the limb, which results in reduced bone mass. In addition, malnutrition, reduced sun exposure, and vitamin D deficiency can exacerbate bone loss in stroke survivors. Common stroke treatments, such as oral anticoagulants, can also increase the risk of osteoporosis and fracture . Therefore, effective measures should be taken for skeletal health screening and fracture prevention in patients with cerebrovascular disease.
A comparison of multiple machine learning algorithms showed that random forests performed best in predicting the risk of postoperative NVCF. Our study has several advantages. First, few studies have examined which of the logistic regression and machine learning algorithms is better in predicting the probability of NVCF after PKP. Furthermore, our model shows superior predictive ability compared to other models. However, this study also has some limitations. First, the nature of retrospective studies can lead to subjective and selection bias. Second, the sample size included in the single-centre study is still not large enough. Third, because most clinicians have a low level of understanding of techniques such as machine learning, this may limit the dissemination and application of the study results. Finally, single-centre studies may limit the sample selection and its applicability to other regions, so we need further external validation with multicentre data. In this paper, we found that the random forest algorithm has good performance in predicting bone cement leakage after orthopaedic surgery, and at the same time, it has comparable accuracy and ease of use. In the future, we will collaborate with more countries and institutions to include patient samples from different countries, regions and medical centres to conduct multicentre, large-sample prospective studies to obtain more reliable results. We look forward to further improving the predictive power in future studies by applying more advanced and reliable computer technology.