Background: To investigate the risk factors for new vertebral compression fractures (NVCFs) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) and to create a nomogram to predict the occurrence of new postoperative fractures. Methods: This was a retrospective analysis of the clinical data of 529 OVCF patients who received PKP treatment in our hospital from June 2017 to June 2020. Based on whether there were new fractures within 2 years after surgery, the patients were divided into a new fracture group and a nonnew fracture group. Univariate and multivariate analyses were used to determine the risk factors for the occurrence of NVCFs after surgery. The data were randomly divided into a training set (75%) and a testing set (25%). Nomograms predicting the risk of NVCF occurrence were created based on the results of the multivariate analysis, and performance was evaluated using receiver operating characteristic curves (ROCs), calibration curves, and decision curve analyses (DCAs). A web calculator was created to give clinicians a more convenient interactive experience. Results: A total of 56 patients (10.6%) had NVCFs after surgery. The univariate analysis showed significant differences in sex and the incidences of cerebrovascular disease, a positive fracture history, and bone cement intervertebral leakage between the two groups (P < 0.05). The multivariate analysis showed that sex [OR = 2.621, 95% CI (1.030–6.673), P = 0.043], cerebrovascular disease [OR = 28.522, 95% CI (8.749–92.989), P = 0.000], fracture history [OR = 12.298, 95% CI (6.250–24.199), P = 0.000], and bone cement intervertebral leakage [OR = 2.501, 95% CI (1.029–6.082), P = 0.043] were independent risk factors that were positively associated with the occurrence of NVCFs. The AUCs of the model were 0.795 (95% CI: 0.716–0.874) and 0.861 (95% CI: 0.749–0.974) in the training and testing sets, respectively, and the calibration curves showed high agreement between the predicted and actual states. The areas under the decision curve were 0.021 and 0.036, respectively. Conclusion: Female sex, cerebrovascular disease, fracture history and bone cement intervertebral leakage are risk factors for NVCF after PKP. Based on this, a highly accurate nomogram was developed, and a webpage calculator (https://new-fracture.shinyapps.io/DynNomapp/) was created.
Keywords: Osteoporosis; Vertebral kyphoplasty; Vertebral compression fracture; Nomogram; Prediction models
The incidence of osteoporosis increases as the proportion of aging adults increases. Osteoporotic vertebral compression fractures (OVCFs) present with persistent low back pain, vertebral kyphosis, and decreased quality of life [[
A total of 529 patients who underwent PKP to treat OVCFs at our institution from June 2017 to June 2020 were entered into the study cohort, and these patients were followed up for a mean duration of 28.92 ± 4.21 months.
Inclusion criteria: (
Exclusion criteria: (
All operations were performed by the same medical team. The patient was placed in a prone position, and the injured vertebra was located and marked using C-arm fluoroscopy before surgery. A 4-mm incision was made at the localization point under local anaesthesia, and a puncture needle (Suzhou Aide Technology Development Co., Ltd.) was placed along the vertebral arch under C-arm guidance, with an angle of approximately 15–20° between the needle and the sagittal plane of the body. The puncture needle was stopped when it reached the posterior anterior 1/3 of the vertebral body. A balloon was placed inside the vertebral body via the puncture needle core, and a contrast agent was injected to slowly expand the balloon. The extent of balloon expansion and the height of the vertebral body were observed under fluoroscopy, and when the balloon position was satisfactory, the balloon and contrast agent were removed. Polymethylmethacrylate (PMMA) bone cement (Tecres S.P.A.) was prepared in the form of toothpaste, 3–5 ml was injected into each vertebral body, and the distribution of the bone cement in the vertebral body was closely observed. The C-arm confirmed that the bone cement was well distributed, and the procedure was completed. PKP was performed on the opposite side using the same method as in the bilateral arch approach.
After surgery, the patients wore a support device around the waist to get out of bed and were advised to do muscle exercises while lying in bed to prevent deep vein thrombosis in the lower limbs. The frontal and lateral radiographs of the spine were reviewed 24 h after surgery. The patients attended follow-up visits at 1 month, 6 months, 1 year and 2 years postoperatively at the outpatient clinic. If the patient did not return for the follow-up visit, they were called and asked to disclose the reason for missing the follow-up visit. If the patient suddenly developed back pain during the follow-up period, MRI was performed to determine if a new vertebral fracture had occurred. Diagnostic criteria for the development of NVCFs after PKP were as follows: 1) reappearance of low back pain after postoperative pain relief and limited movement, especially when turning or getting up. 2) MRI showing a high T2 signal and a low T1 signal. Postoperatively, if there was no contraindication, calcium carbonate D3 (Jiangsu Suzhou Wyeth Pharmaceutical Co., Ltd., orally, 1 time/day, 1 tablet/time, for 2–3 months), osteoporotic triol (Shandong Qingdao Zhengda Pharmaceutical Co., Ltd., orally, 1 time/day, 0.25 µg/day, for 2–3 months), and zoledronic acid injection (Novartis, Switzerland, intravenous infusion, 5 mg/dose once/year for 2–3 years) were used to treat osteoporosis. Patients were asked at each review whether they complied with the doctor's orders for anti-osteoporosis treatment.
The following information was recorded preoperatively.
(
Fracture history: history of other vertebral fractures (with or without symptoms) that occurred prior to the OVCF or a history of fractures elsewhere in the body. Old vertebral fractures on magnetic resonance images were also indicative of a positive history of prior fracture.
Bone cement distribution: bone cement not crossing the midline of the vertebral body on the frontal X-ray was defined as unilateral distribution; otherwise, it was bilateral distribution; if the bone cement was discontinuous bilaterally, it was bilaterally separated distribution; otherwise, it was bilaterally fused distribution.
Vertebral height recovery rate: the anterior edge (biconcave fracture) or midline (wedge fracture) height of the fractured vertebrae was recorded preoperatively and 24 h postoperatively (the height of the anterior edge of each fractured vertebra was taken, and the average value was calculated). In this study, the average value of the heights of the two adjacent normal, same-segment vertebrae of the fractured vertebrae was taken as the normal vertebral height, and then the vertebral height recovery rate was calculated with the following formula: normal vertebral height H0 = (previous normal vertebral height H1 + next normal vertebral height H2)/2. Vertebral body height recovery rate = (postoperative height of injured vertebra—preoperative height of injured vertebra)/H0 * 100%.
Cobb angle: the angle between the upper edge of the head end of the fractured vertebral body and the lower edge of the tail end.
Continuous variables were expressed as the mean ± standard deviation, and categorical variables were expressed as ratios. The data were randomly divided into a training set (75%) and a validation set (25%). In this study, the training set was used to construct a nomogram, and the testing set was used to validate the efficacy of the nomogram.
Univariate and multivariate logistic regression analyses were used to filter the variables in the dataset, and variables with P < 0.05 were included in the model. The "rms" package in R-Studio was used to build the nomogram. Calibration curves of the model were drawn using the 1000-sample validation method to determine the consistency of the model. The predictive power of the nomogram was tested using tenfold cross-validation. The sensitivity and specificity of the model were evaluated using the area under the curve (AUC), and the larger the AUC value, the better the predictive power of the model. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. The model capability was further validated in the testing set following the same method as above.
Data analysis was performed using SPSS (Version 26.0, IBM Corporation, Chicago, USA) and R-Studio (Version 3.6.2, R Foundation for Statistics Computing, Vienna, Austria), and several R packages were applied, including regplot, rms, ggDCA, ggplot, pROC, etc., to plot nomograms, calibration plots, decision curves, and ROC curves. P < 0.05 was statistically significant.
The clinical data of 529 patients, including 135 males and 394 females, with a mean age of 71.185 ± 10.012 years, were included in this study. Among them, 56 patients had new fractures after surgery, and 473 patients did not have any new fractures. The patients' baseline data are presented in Table 1. Figure 1 shows the heatmap of the correlation of the dataset.
Table 1 Baseline information [n (%), x ̅ ± s]
Baseline information Patients (n) 529 Age (years) 71.185 ± 10.012 Sex (M/F) 135 (25.5)/394 (74.5) BMI (kg/m2) 23.394 ± 3.299 BMD -2.811 ± 0.270 Hypertension 200 (37.8)/329 (62.2) Diabetes 45 (8.5)/484 (91.5) Heart disease 28 (5.3)/501 (94.7) Respiratory diseases 7 (1.3)/522 (98.7) Cerebrovascular disease 21 (4.0)/508 (96.0) Time of injury 26.718 ± 67.426 Time from admission to surgery 3.010 ± 2.552 Number of fractured vertebral bodies 1.327 ± 0.693 Location of the fractured vertebral thoracic/thoracolumbar spine/lumbar 72 (13.6)/347 (65.6)/110 (20.8) Operation approach unilateral/bilateral 147 (27.8)/382 (72.2) Fracture history (Y/N) 90 (17.0)/439 (83.0) New fracture (Y/N) 56 (10.6)/473 (89.4) Paravertebral leakage 74 (14)/455 (86) Intervertebral leakage 63 (11.9)/466 (88.1) Spinal leakage 12 (2.3)/517 (97.7) Cobb of post operation 12.508 ± 8.293 Bone cement distribution unilateral/bilateral fusion/bilateral separated 119 (22.5)/285 (53.9)/125 (23.6) Bone cement contact with the endplate 417 (78.8)/417 (21.2) Fracture type biconcave/wedge/compression 208 (39.3)/264 (49.9)/57 (10.8) Anti-osteoporosis (Y/N) 429 (81.1)/429 (18.9) Vertebral height recovery height 14.393 ± 13.441
Graph: Fig. 1Heatmap of Data Correlation. Factors near positive colors are highly expressed and positively correlated, while factors near negative colors are lowly expressed and negatively correlated. Each square indicates the correlation between the factors in that row and column, and the color is used to indicate the amount of correlation. Abbreviation: hypert (hypertension); heart.dis (heart disease); resp.dis (respiratory diseases); cerebro.dis(cerebrovascular disease); ats.time(time from hospital admission to surgery); fract.num (fracture number); fract.loc (fracture location); fract.his (fracture history); new.fract (new fracture); para.leak (paravertebral leakage); inter.leak (intervertebral leakage); spinal.leak (spinal leakage); post.cobb (post-operation cobb angle); cement.dis (cement distribution); fract.typ (fracture type); anti.ost (anti-osteoporosis); VHRA (vertebral height recovery rate)
The univariate analysis (Table 2) showed statistically significant differences (P < 0.05) in age, BMD, cerebrovascular disease status, and fracture history between the two groups. The multivariate analysis showed (Table 2) that sex [OR = 2.621, 95% CI (1.030–6.673), P = 0.043], cerebrovascular disease [OR = 28.522, 95% CI (8.749–92.989), P = 0.000], fracture history [OR = 12.298, 95% CI (6.250–24.199), P = 0.000], and cemented intervertebral leakage [OR = 2.501, 95% CI (1.029–6.082), P = 0.043] were independent risk factors positively associated with new fractures.
Table 2 Univariate and multivariate analysis
Variables Univariate analysis Multivariate analysis Age 1.896 (0.903–3.981) 0.091 / / Sex 1.032 (1.001–1.064) 0.041 2.621(1.030–6.673) 0.043 BMI 1.029 (0.947–1.119) 0.498 / / BMD 0.235 (0.091–0.609) 0.003 / / Hypertension 1.615 (0.925–2.817) 0.092 / / Diabetes 1.964 (0.865–4.460) 0.107 / / Heart disease 1.415 (0.167–11.972) 0.750 / / Respiratory diseases 1.014 (0.296–3.473) 0.982 / / Cerebrovascular disease 14.061 (5.615–35.208) 0.000 28.522(8.749–92.989) 0.000 Time of injury 0.985 (0.970–1.001) 0.070 0.985(0.969–1.001) 0.065 Time from admission to surgery 0.995 (0.892–1.111) 0.931 / / Number of fractured vertebral 0.926 (0.607–1.414) 0.722 / / Location of the fractured vertebral 1.192 (0.740–1.920) 0.470 / / Operation approach 1.651 (0.829–3.287) 0.154 / / Fracture history 10.471 (5.747–19.081) 0.000 12.298(6.250–24.199) 0.000 New fracture 1.028 (0.465–2.270) 0.946 / / Paravertebral leakage 1.979 (0.964–4.063) 0.063 / / Intervertebral leakage 0.000 (0.000-inf) 0.983 2.501(1.029–6.082) 0.043 Spinal leakage 0.982 (0.947–1.018) 0.311 / / Bone cement distribution 1.209 (0.802–1.821) 0.364 / / Bone cement in contact with the endplate 1.111 (0.554–2.225) 0.767 / / Fracture type 1.151 (0.754–1.757) 0.515 / / Anti-osteoporosis(Y/N) 0.667 (0.349–1.275) 0.220 / / Vertebral height recovery height 0.995 (0.974–1.017) 0.660 / /
Independent predictors were derived by multivariate analysis, and four predictors were finally included in the model: female sex, positive fracture history, cerebrovascular disease diagnosis, and cemented intervertebral leakage (Fig. 2). A web calculation (Fig. 3) was created based on the results of the study (https://new-fracture.shinyapps.io/DynNomapp/). Each factor in the nomogram corresponds to the score of the vertex axis, and finally, the scores of each factor were summed to calculate the total score. A straight line was drawn from the corresponding total score point to obtain the outcome probability.
Graph: Fig. 2Nomogram. Abbreviations: inter.leak: intervertebral leakage; cerebro.dis: cerebrovascular disease; frac.his: fracture history
Graph: Fig. 3web calculator. A web-based interactive calculator interface based on a nomogram for predicting the risk of NVCF after PKP. Assignment method: sex: 0, male; 1, female; inter.leak (intervertebral space leakage): 0 (no), 1 (yes); cerebro.dis (cerebrovascular disease): 0 (no), 1 (yes); fract.his (fracture history): 0 (no), 1 (yes)
The model was validated by AUC, calibration curves, and decision curves. In the training set, the ROC curve showed that the obtained model had a good discriminatory ability with an AUC of 0.795 (95% CI: 0.716–0.874), indicating that it could predict the risk of NVCF development after PKP more accurately. The calibration curve showed high agreement between the prediction of the nomogram and the actual observation (Fig. 4), and the area under the decision curve (AUDC) was 0.021. In the testing set, the AUC of the model was 0.861 (95% CI: 0.749–0.974), the calibration curve assessed good agreement between the predicted and observed actual results, and the AUDC was 0.036 (Fig. 5).
Graph: Fig. 4ROC curve, calibration curve and decision curve of training set. A ROC curve; B calibration curve; C DCA curve
Graph: Fig. 5ROC curve, calibration curve and decision curve of testing set. A ROC curve; B calibration curve; C DCA curve
As a minimally invasive technique, PKP has been widely used in clinical practice in recent years. NVCFs are one of the most common complications after surgery for OVCFs. Nomograms are a visual prediction tool based on a statistical regression model that can measure the influence of various factors on the possibility of event occurrence and have been widely used in various medical fields [[
There are many factors affecting the development of NVCFs. The multivariate analysis revealed that female sex, cerebrovascular disease diagnosis, positive fracture history, and intervertebral leakage of bone cement were independent risk factors for NVCFs after surgery for OVCFs. Based on this, we developed a nomogram based on four of the more influential and readily available independent predictors to provide an accurate tool to predict new postoperative fracture risk. At the same time, the results of the internal validation also show good discriminative and calibration abilities, and the higher AUC value indicates that the nomogram can be widely and accurately applied.
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 the 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 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 NVCFs. A study by Tanislav et al. [[
Osteoporosis progresses slowly, is not easily detected and may remain unnoticed for years until patients develop painful symptoms. In this study, most patients were first diagnosed with osteoporosis because of symptomatic vertebral fractures. Patients with previous fractures will therefore be at significantly increased risk of refracture in the future [[
When vertebral compression severely involves the endplate or is due to improper puncture, it can lead to the leakage of bone cement through the ruptured endplate to the intervertebral disc, thus altering the surrounding stresses [[
In this study, we established a nomogram model based on a larger cohort and successfully validated the model in a validation cohort. Each variable included in the nomogram is a relatively accessible factor. By calculating scores for each of the 4 factors, orthopaedic surgeons can easily estimate the risk of NVCFs after surgery. Based on the assessment results, patient management strategies can be improved to reduce the risk of NVCFs. Likewise, for low-risk patients, some preventive measures can be implemented to reduce the financial burden.
This study has some limitations. First, this was a retrospective study, so there may be selection bias. However, we included as many preoperative and surgical factors because of the large sample of patients to minimize bias. Second, this study is a single-centre study. Although this nomogram has been validated in a validation cohort, the incidence of postoperative refractures varies across hospitals, regions, and countries, which may limit the application of this model in some hospitals. Third, 1 of the 4 parameters included in the model was determined postoperatively, which may not better assess the likelihood of postoperative refracture in patients preoperatively. Through multicentre retrospective studies or prospective randomized clinical trials, the sensitivity and specificity of nomograms can be further improved, providing high-level evidence for future clinical applications.
Female sex, cerebrovascular disease diagnosis, positive fracture history and bone cement intervertebral leakage are risk factors for NVCFs after PKP. Based on this, a highly accurate nomogram was developed, and a webpage calculator (https://new-fracture.shinyapps.io/DynNomapp/) was created.
Ma Yiming, Lu Qi, Wang Xuezhi, and Wang Yalei obtained the data, conceived the manuscript, and Ma participated in the study design and reviewed the manuscript. Ma analysed the data and wrote the first draft of the manuscript. Wang Yalei made contributions during the manuscript revision period. Chen and Yuan assisted with the preliminary data analysis and reviewed the draft of the manuscript. All the authors have read and approved the manuscript.
This study was funded by the Natural Science Foundation of Jiangsu Provincial Department of Science and Technology (BK20221211); Jiangsu Provincial Health Commission Project (Z2021070); Xuzhou Medical key talents training Project(XWRCSL20220029).
The original data in the study will be made available by the authors, further inquiries can be directed to the corresponding author.
The study was approved by the Ethics Committee of Xuzhou Medical University (XYFY2017-JS013-01), and this study follows the guidelines in the Helsinki Declaration. All participants provided informed consent prior to commencing study involvement.
The coauthors and supporting institutions have consented to publication.
The authors declare no competing interests.
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By Yiming Ma; Qi Lu; Xuezhi Wang; Yalei Wang; Feng Yuan and Hongliang Chen
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