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. 2026 Jul 24;105(30):e49396. doi: 10.1097/MD.0000000000049396

Relevant factors of lumbar compression fracture patients’ death 1 year after surgery and its prediction model creation and validation

Yun-jian Li a,*
PMCID: PMC13406249  PMID: 42499037

Abstract

This study aimed to investigate the risk factors associated with 1-year postoperative mortality in patients with lumbar compression fractures and to construct and validate a predictive nomogram model. Clinical data of patients admitted between January 2021 and December 2024 were retrospectively analyzed. Independent predictors of 1-year mortality were identified using univariate and multivariate logistic regression analyses. A nomogram was constructed based on the final model. Model discrimination was evaluated using the receiver operating characteristic curve and the area under the curve. Calibration, Bootstrap resampling, and 10-fold cross-validation were used for internal validation. A total of 378 patients were included, of whom 21 (5.56%) died within 1 year postoperatively. Five independent predictors were identified: bone mineral density ≤ −2.5, multiple segmental fractures, age > 70 years, albumin ≤ 40 g/L, and neutrophil-to-lymphocyte ratio > 4. The nomogram showed good discriminative performance, with an area under the curve of 0.826 in the training cohort and 0.813 in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes. One-year postoperative mortality in lumbar compression fracture patients is influenced by multiple clinical and inflammatory factors. The proposed nomogram demonstrates good discriminative ability and may help clinicians identify high-risk patients for early intervention.

Keywords: kyphoplasty, mortality, nomograms, osteoporosis, spinal fractures

1. Introduction

Lumbar compression fracture is a common orthopedic disease in the elderly population, especially due to the high prevalence of osteoporosis, which has led to a significant increase in its incidence in the elderly.[1] Lumbar compression fracture is usually accompanied by severe back pain and activity limitation, which seriously affects the quality of life of patients. With the development of minimally invasive techniques, posterior kyphoplasty (PKP) has become a commonly used surgical procedure for the treatment of lumbar compression fractures.[2] PKP surgery aims to restore vertebral height, reduce pain, and stabilize the fracture by injecting cement into the fractured vertebral body via a percutaneous route. However, despite the effectiveness of PKP surgery in relieving symptoms, its postoperative mortality remains a significant clinical problem, especially in elderly patients with comorbidities of other chronic diseases.[3]

The risk of postoperative mortality is a key factor to focus on when assessing the prognosis of patients with lumbar compression fractures. Studies[3–5] have shown that patient age, bone mineral density (BMD), comorbidities, and the occurrence of postoperative complications are closely related to the risk of postoperative mortality. Elderly patients have a higher risk of death due to more difficult postoperative recovery because of the decline in physiologic function and the presence of multiple underlying diseases. The risk of postoperative death is especially prominent in patients with underlying diseases such as cardiovascular disease, diabetes mellitus, and chronic respiratory disease. In addition, postoperative complications, such as infections, deep vein thrombosis, and pulmonary complications, also tend to be closely associated with the risk of death in patients.[6]

In recent years, inflammatory response has been recognized as an important factor affecting postoperative prognosis. Neutrophil-to-lymphocyte ratio (NLR), as an important marker of inflammatory response, has been demonstrated by several studies to be closely related to the prognosis of various diseases.[7,8] In patients with lumbar compression fractures, high NLR values suggest that patients may have a strong systemic inflammatory response, and this inflammatory response not only affects postoperative recovery, but may also increase the incidence of postoperative complications, thereby raising the risk of death. Therefore, NLR may become an important indicator for assessing the risk of postoperative mortality.

With the accumulation of statistical methods and medical data, the establishment of predictive models has become an important means of assessing the risk of patient death. Nomogram, as an intuitive and easy-to-apply risk assessment tool, is widely used in the prognostic assessment of various diseases.[9,10] It helps physicians to quickly and accurately assess a patient’s risk of death by transforming multiple clinical variables into graphical form.[11] In the treatment of patients with lumbar compression fractures, constructing a Nomogram model that integrates clinical factors and inflammatory responses (e.g., NLR) will help to accurately assess a patient’s risk of postoperative death, thus providing a scientific basis for individualized treatment and early intervention.

The aim of this study was to retrospectively analyze the factors associated with 1-year postoperative mortality in patients with lumbar compression fractures who underwent PKP surgery, focusing on the role of NLR in the risk of postoperative mortality. Independent risk factors affecting postoperative death were screened by unifactorial and multifactorial logistic regression analyses, and a Nomogram prediction model was constructed based on these factors.

2. Data and methods

2.1. Data sources and data collection

In this study, clinical data of patients hospitalized in our hospital for lumbar spine compression fracture between January 2021 and December 2024 were collected by retrospective analysis. As this study constitutes a retrospective analysis of anonymized clinical data without patient contact or intervention, it meets the criteria for exemption from ethics review according to the hospital’s institutional policy. All procedures comply with the ethical standards of the Institutional Review Board and the requirements of the Declaration of Helsinki.

2.2. Inclusion exclusion criteria

Inclusion Criteria: all patients diagnosed with lumbar compression fracture by imaging examination and clinical manifestations, and receiving PKP treatment in our hospital. At the same time, the patients should be able to cooperate with the completion of the relevant assessment scale and data recording. Exclusion criteria: patients with tumors, systemic immune diseases, severe cardiopulmonary dysfunction and other diseases were excluded; patients whose prognosis might be seriously affected by infected fractures, traumatic fractures and other causes were also excluded.

2.3. Collection of relevant variables

In this study, a number of clinical and laboratory data will be collected from patients with lumbar compression fracture, specifically including patients’ gender, age, body mass index, BMD, fracture segment, and relevant biochemical indexes. The patient’s C-reactive protein, albumin (Alb), hemoglobin, platelet count, white blood cell count, and electrolyte levels (Na+, Ca2+, K+) will also be recorded. In addition, considering the impact of inflammatory response on prognosis, NLR was included as an important indicator in this study.

In this study, “multiple segmental fractures” were defined as fractures involving 2 or more lumbar vertebral segments, confirmed by both radiographic and CT imaging. Fracture identification was independently assessed by 2 experienced radiologists, and disagreements were resolved by consensus. BMD was measured using dual-energy X-ray absorptiometry, and a value ≤ −2.5 was classified as osteoporosis according to World Health Organization criteria. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count, with a cutoff of > 4 determined based on previous literature and the distribution characteristics of our cohort.

2.4. Outcome subgroups

After the patients are discharged from the hospital, the research team will regularly follow-up the recovery progress of the patients regularly by telephone follow-up. Specifically, 1 follow-up visit will be conducted in the first 3 months after discharge, and then every 3 months until 1 year after surgery. After 1 year postoperatively, the frequency of follow-up visits will be adjusted to every 6 months. During the follow-up process, if patients unfortunately died within 1 year after surgery, they would be categorized into the death group; patients who did not die were included in the control group for further analysis.

2.5. Statistical analysis

In order to deeply explore the predictive factors of death within 1 year after surgery in patients with lumbar spine compression fracture, the present study adopted a stratified random sampling method, in which the collected clinical data were allocated to the modeling group (70%) and the validation group (30%) in a ratio of 7:3 via R version 4.2.1 (R Foundation for Statistical Computing). In this process, data from the modeling group will be used to construct and initially validate the predictive model, while data from the validation group will be used to independently assess the generalization ability of the model. First, data in the modeling group were analyzed using SPSS version 27.0 (IBM Corp.). Univariate analyses were performed to examine the association between each clinical variable and 1-year postoperative mortality. Categorical variables were compared using the chi-square test (or Fisher exact test when appropriate), with particular attention to identifying statistically significant differences between groups. We set a significance level of P < .05 to screen for clinical variables that were significantly associated with the risk of death at 1 year postoperatively. Subsequently, these significantly associated variables will be further included in a multifactorial logistic regression analysis aiming to identify independent risk factors for 1-year postoperative death in patients with lumbar compression fractures. In the multifactorial regression analysis, variables with a P value of < .05 will be considered statistically significant and included in the final model. Based on the results of the regression analysis, a nomogram was drawn using R software to visualize the degree of influence of each independent risk factor on the risk of death at 1 year postoperatively.

To evaluate the performance of the predictive model, we plotted receiver operating characteristic (ROC) curves in the modeling and validation groups and calculated the area under the curve. This process helped to comprehensively evaluate the predictive ability of the model. In addition, to further improve the reliability of model validation, we used the Bootstrap method (sampling number: 1000 times) combined with 10-fold cross-validation for internal validation to ensure the stability and adaptability of the model. Finally, we comprehensively evaluated the calibration and clinical application value of the prediction model by combining the calibration curve and decision curve analysis.

3. Results

3.1. General

A total of 412 patients were initially screened during the study period. After excluding individuals with tumor-related fractures (n = 12), systemic immune diseases (n = 8), severe cardiopulmonary dysfunction (n = 7), or incomplete clinical data (n = 7), 378 patients met the eligibility criteria and were included in the final analysis. Among these patients, 21 died within 1 year after surgery, corresponding to a 1-year mortality rate of 5.56%. In accordance with the predefined 7:3 allocation ratio, the eligible patients were randomly assigned to a modeling group (n = 265) and a validation group (n = 113). The modeling group was used for nomogram development and internal validation, whereas the validation group was reserved for independent assessment of the model’s generalizability and predictive performance.

3.2. Independent risk factors for 1-year postoperative death in patients with lumbar compression fracture

In the modeling group, we analyzed 14 clinical variables by univariate logistic regression, and the results showed that 7 of these factors were significantly associated with the risk of 1-year postoperative death in patients with lumbar compression fracture. These factors included patients’ age, BMD, presence of multiple fractures, Alb level, hemoglobin concentration, K+, and NLR (Table 1). BMD ≤ −2.5 (odds ratio [OR]: 2.165, 95% confidence interval [CI]: 1.058–5.443), multiple segmental fractures (OR: 3.966, 95% CI: 1.615–8.492), age > 70 years (OR: 2.936, 95% CI: 1.310–7.389), Alb ≤ 40 g/L (OR: 3.436, 95% CI: 1.420–7.285), and NLR > 4 (OR: 4.208, 95% CI: 1.440–9.295) were independent risk factors for death 1 year after surgery in patients with lumbar compression fractures (Table 2).

Table 1.

Univariate analysis of 1-year postoperative mortality in lumbar compression fracture patients.

Factors Death group (N = 15) Control group (N = 250) P
Gender .808
 Male 6 108
 Female 9 142
Age .013
 ≤ 70 years 2 115
 > 70 years 13 135
BMI .722
 ≤ 24kg/m2 8 145
 > 24kg/m2 7 105
BMD .017
 ≤ −2.5 12 121
 > −2.5 3 129
Multiple Fractures .033
 Yes 8 69
 No 7 181
CRP .562
 Normal 3 67
 Elevated 12 183
Alb .030
 Normal 5 154
 Reduced 10 96
Hb .048
 Normal 2 97
 Reduced 13 153
Platelet Count .637
 Normal 13 226
 Reduced 2 24
WBC count .650
 Normal 6 115
 Elevated 9 135
Na+ .673
 Normal 11 195
 Abnormal 4 55
Ca2+ .532
 Normal 10 185
 Abnormal 5 65
K+ .041
 Normal 9 204
 Abnormal 6 46
NLR .004
 Normal 4 159
 Elevated 11 91
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Alb = albumin, BMD = bone mineral density, BMI = body mass index, CRP = C-reactive protein, Hb = hemoglobin, N = number of patients, NLR = neutrophil-to-lymphocyte ratio, WBC = white blood cell.

Table 2.

Multivariate analysis of 1-year postoperative mortality in lumbar compression fracture patients.

Risk factor OR 95% CI (OR) P
BMD ≤ −2.5 2.165 1.058–5.443 .035
Multiple segmental fractures 3.966 1.615–8.492 .006
Age > 70 years 2.936 1.310–7.389 .004
Alb ≤ 40g/L 3.436 1.420–7.285 .036
NLR > 4 4.208 1.440–9.295 .044
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Alb = albumin, BMD = bone mineral density, CI = confidence interval, NLR = neutrophil-to-lymphocyte ratio, OR = odds ratio.

3.3. Development and validation of a nomogram

With the independent risk factors screened by multifactorial logistic regression analysis, we constructed a nomogram to visually predict the risk of death within 1 year after surgery in patients with lumbar spine compression fractures (Fig. 1). In this model, each independent risk factor was assigned a weighted point value according to its regression coefficient, and the sum of these points generated a total score. This total score was then directly mapped to the predicted probability of 1-year postoperative mortality, consistent with the description in the legend of Figure 1. On this basis, we quantified the predictive performance of the model by plotting ROC curves in the modeling and validation groups.The results of the Hosmer-Lemeshow goodness-of-fit test showed a χ2 value of 7.598 with a P value of .457 for the modeling group and 7.914 with a P value of .435 for the validation group, which demonstrated that the model had a good fit and consistency. Further calculations showed that the area under the curve was 0.826 for the modeling group and 0.813 for the validation group (see Fig. 2A and 2B), and these results indicated that the model performed well in differentiating the risk of patient death and was able to effectively identify high-risk patients within 1 year after surgery.

Figure 1.

Figure 1.

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Nomogram prediction model for 1-year postoperative mortality in patients with lumbar spine compression fracture. In this nomogram, each predictor variable is assigned a weighted point value according to its regression coefficient. The individual points are summed to yield a “Total Points” score, displayed on the bottom axis. This total score corresponds directly to the estimated probability of 1-year postoperative mortality. The maximum score of 350 reflects the cumulative contribution of all variables included in the model. The scoring logic and risk probability mapping are fully consistent with the explanation provided in the results section. Alb = albumin, BMD = bone mineral density, NLR = neutrophil-to-lymphocyte ratio.

Figure 2.

Figure 2.

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ROC curve for nomograms predicting 1-year postoperative mortality in patients with lumbar spine compression fracture in (A) training set (B) and validation set. Nomogram calibration curves for predicting 1-year postoperative mortality in lumbar compression fracture patients in (C) training set (D) and validation set. Decision curves for nomograms used to predict 1-year postoperative mortality in patients with lumbar spine compression fractures in (E) training set (F) and validation set. ROC = receiver operating characteristic

To further validate the stability and reliability of the model, we used the Bootstrap method combined with 10-fold cross-validation for internal validation. The results showed that the area under the ROC curve of the validation group was 0.819, the sensitivity was 81.5%, and the specificity was 82.1%. This series of data indicates that the model has high prediction accuracy and stability. On this basis, to verify the degree of conformity between the risk of death predicted by the nomogram and the actual incidence of death at 1 year postoperatively, we plotted a calibration curve (see Fig. 2C and 2D). This calibration curve showed that the risk predicted by the nomogram was highly consistent with the actual observed risk of death, further demonstrating that the model had good calibration and prediction ability. Finally, the decision curve analysis (see Fig. 2E and 2F) also further validated the clinical decision value of the nomogram under different threshold probabilities, showing that the model has high clinical utility in predicting the risk of death at 1 year after surgery.

4. Discussion

Lumbar compression fracture is a common orthopedic condition in the elderly, and its incidence continues to rise with the aging population.[12] These fractures are primarily caused by osteoporosis and often result in vertebral height loss and kyphotic deformity, significantly impairing patients’ quality of life and functional status.[13] PKP is widely utilized due to its efficacy in symptom relief, vertebral height restoration, and functional improvement.[14] However, despite its benefits, postoperative mortality remains a major concern, especially in older adults with multiple comorbidities.[15] Identifying high-risk individuals and implementing early interventions are therefore essential to improving outcomes.

In this study, we developed a nomogram model using 5 readily accessible clinical predictors. After internal validation, the model demonstrated good discriminatory power and calibration ability.

Our analysis found that BMD ≤ −2.5 was an independent risk factor for 1-year postoperative mortality. A BMD value ≤ −2.5 indicates severe osteoporosis, which leads to fragile bones, a higher risk of multiple fractures, and a greater likelihood of complications such as deep vein thrombosis and pulmonary infections.[16] Osteoporosis also correlates with sarcopenia, malnutrition, and chronic inflammation, collectively worsening systemic function and recovery potential.[5,17,18] Furthermore, hormonal imbalances related to osteoporosis may contribute to increased cardiovascular and immune dysfunction, raising mortality risk.[19,20]

Multisegmental vertebral fractures also emerged as a significant risk factor. These fractures suggest advanced bone fragility and are associated with reduced spinal stability and greater kyphotic deformity, leading to prolonged immobility and complications like infections and thrombosis.[21,22] Multisegmental involvement also reflects metabolic bone disease activity, which increases the likelihood of recurrent fractures and poor functional outcomes.[23,24]

Age > 70 years was another independent risk factor identified. With increasing age, patients often exhibit diminished bone healing capacity, weakened immune function, and higher rates of comorbidities such as cardiovascular and respiratory diseases.[25] These factors prolong recovery and elevate complication rates, resulting in increased mortality.[26,27] Psychological factors, such as cognitive decline and poor social support, may also negatively impact rehabilitation and long-term survival.[28,29]

Low serum Alb (≤ 40 g/L) was significantly associated with increased mortality. Alb is a critical marker of nutritional and inflammatory status. Hypoalbuminemia reflects malnutrition and systemic inflammation, which impair immune defense, delay wound healing, and predispose patients to postoperative complications such as infections and pressure ulcers.[30–33]

Finally, NLR > 4 was identified as a predictor of mortality, emphasizing the role of inflammation in prognosis. NLR reflects the balance between neutrophil-mediated inflammation and lymphocyte-mediated immunity. An elevated NLR indicates a pro-inflammatory and immunosuppressed state, which is associated with higher susceptibility to infections and cardiovascular events.[34–37] Additionally, chronic inflammation can worsen osteoporosis and delay bone healing.[38,39]

Collectively, these 5 variables provide a comprehensive view of patients’ systemic health and surgical risk. Incorporating them into a predictive nomogram allows clinicians to stratify risk and implement individualized perioperative management strategies. For example, patients with low BMD should receive intensified anti-osteoporosistherapy, while those with low Alb or high NLR may benefit from nutritional support and anti-inflammatory interventions.

In clinical practice, the nomogram developed in this study can serve as a practical and accessible tool for early risk stratification in patients undergoing PKP for lumbar compression fractures. Because the predictors included in the model (such as age, BMD, Alb level, and NLR) are routinely available during admission, clinicians can rapidly estimate an individual patient’s 1-year mortality risk at the bedside or during early postoperative assessment. Patients identified as high risk can benefit from intensified monitoring, optimization of nutritional and inflammatory status, early mobilization strategies, and more frequent follow-up visits. In addition, the nomogram may assist clinicians in shared decision-making by facilitating communication with patients and their families regarding prognosis, expected recovery trajectories, and the need for targeted postoperative care. From a management perspective, the tool can also help allocate healthcare resources more efficiently by identifying individuals who require greater postoperative support. Overall, the nomogram provides an intuitive, easy-to-use framework that integrates key clinical and laboratory indicators to guide individualized postoperative management.

Despite the model’s favorable performance, several limitations should be acknowledged. First, this was a single-center, retrospective study, which may introduce selection bias and limit the generalizability of the findings. Second, certain potentially influential variables (such as fracture severity [e.g., vertebral compression rate] and social support status) were not included due to limitations in the available medical records. Specifically, fracture severity data were not systematically collected in a standardized manner during the study period, which led to their exclusion from the analysis. The absence of quantified fracture severity may have affected the accuracy of outcome prediction, as patients with more severe vertebral collapse may have a substantially higher risk of adverse outcomes. Therefore, the lack of this variable may have resulted in a potential underestimation of risk in some patients and reduced the comprehensiveness of the model. Future studies should aim to incorporate these parameters to improve the comprehensiveness and robustness of the predictive model. Lastly, although internal validation was conducted using bootstrap and cross-validation techniques, external validation in diverse populations is still required to confirm the model’s applicability across different clinical settings.

5. Conclusion

In summary, our nomogram based on 5 independent risk factors offers a practical tool for predicting 1-year mortality in lumbar compression fracture patients undergoing PKP. It may assist clinicians in early identification of high-risk individuals and guide personalized treatment approaches to improve long-term outcomes.

Acknowledgments

I would like to thank Deng Guanghua for his guidance in analyzing the data and submitting my thesis.

Author contributions

Conceptualization: Yun-jian Li.

Data curation: Yun-jian Li.

Formal analysis: Yun-jian Li.

Investigation: Yun-jian Li.

Methodology: Yun-jian Li.

Software: Yun-jian Li.

Validation: Yun-jian Li.

Visualization: Yun-jian Li.

Writing – original draft: Yun-jian Li.

Writing – review & editing: Yun-jian Li.

Abbreviations:

Alb
albumin
BMD
bone mineral density
CI
confidence interval
Hb
hemoglobin
NLR
neutrophil-to-lymphocyte ratio
OR
odds ratio
PKP
posterior kyphoplasty
ROC
receiver operating characteristic

Due to the nonexperimental nature of the research, the study protocol did not need to be submitted for consideration and approval to an ethical review committee.

The author has no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

How to cite this article: Li Y-j. Relevant factors of lumbar compression fracture patients’ death 1 year after surgery and its prediction model creation and validation. Medicine 2026;105:30(e49396).

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