ShuYuan Zhuang, Jing Wang, Peng Du, SiHong Dong, Jiao Wu, DeLong Li, YuanTong Zang, Li Li
{"title":"A clinical risk prediction model for perioperative lower extremity DVT in patients undergoing spinal fracture surgery.","authors":"ShuYuan Zhuang, Jing Wang, Peng Du, SiHong Dong, Jiao Wu, DeLong Li, YuanTong Zang, Li Li","doi":"10.3389/fsurg.2025.1597101","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To develop a perioperative lower-extremity deep vein thrombosis (DVT) risk prediction model for spinal fracture surgery patients using logistic regression, supporting clinical prevention strategies.</p><p><strong>Methods: </strong>Clinical data from 249 patients undergoing spinal fracture surgery (July 2019-October 2024) were retrospectively analyzed. Participants were divided into a model group (<i>n</i> = 166) and a validation group (<i>n</i> = 83) in a 2:1 ratio. Univariate and multivariate logistic regression identified independent risk factors for perioperative DVT, and a predictive model was established. Model fit was evaluated using the Hosmer-Lemeshow test, and predictive performance was assessed via receiver operating characteristic (ROC) curve analysis.</p><p><strong>Results: </strong>Independent risk factors included perioperative blood transfusion, elevated C-reactive protein, D-dimer >500 μg/L, hypertension, age ≥60 years, and prolonged bed rest. The model [<i>P</i> = 1/(1 + e^-Z)] demonstrated a good fit (Hosmer-Lemeshow <i>χ</i> <sup>2</sup> = 12.139, <i>P</i> = 0.807). ROC analysis showed AUC values of 0.75 (95% CI: 0.80-0.92) for the model group and 0.81 (95% CI: 0.64-0.98) for the validation group, indicating robust predictive performance.</p><p><strong>Conclusion: </strong>The identified risk factors are critical predictors of perioperative DVT in spinal fracture patients. The proposed model exhibits strong clinical utility for early risk stratification and intervention guidance.</p>","PeriodicalId":12564,"journal":{"name":"Frontiers in Surgery","volume":"12 ","pages":"1597101"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489396/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fsurg.2025.1597101","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To develop a perioperative lower-extremity deep vein thrombosis (DVT) risk prediction model for spinal fracture surgery patients using logistic regression, supporting clinical prevention strategies.
Methods: Clinical data from 249 patients undergoing spinal fracture surgery (July 2019-October 2024) were retrospectively analyzed. Participants were divided into a model group (n = 166) and a validation group (n = 83) in a 2:1 ratio. Univariate and multivariate logistic regression identified independent risk factors for perioperative DVT, and a predictive model was established. Model fit was evaluated using the Hosmer-Lemeshow test, and predictive performance was assessed via receiver operating characteristic (ROC) curve analysis.
Results: Independent risk factors included perioperative blood transfusion, elevated C-reactive protein, D-dimer >500 μg/L, hypertension, age ≥60 years, and prolonged bed rest. The model [P = 1/(1 + e^-Z)] demonstrated a good fit (Hosmer-Lemeshow χ2 = 12.139, P = 0.807). ROC analysis showed AUC values of 0.75 (95% CI: 0.80-0.92) for the model group and 0.81 (95% CI: 0.64-0.98) for the validation group, indicating robust predictive performance.
Conclusion: The identified risk factors are critical predictors of perioperative DVT in spinal fracture patients. The proposed model exhibits strong clinical utility for early risk stratification and intervention guidance.
期刊介绍:
Evidence of surgical interventions go back to prehistoric times. Since then, the field of surgery has developed into a complex array of specialties and procedures, particularly with the advent of microsurgery, lasers and minimally invasive techniques. The advanced skills now required from surgeons has led to ever increasing specialization, though these still share important fundamental principles.
Frontiers in Surgery is the umbrella journal representing the publication interests of all surgical specialties. It is divided into several “Specialty Sections” listed below. All these sections have their own Specialty Chief Editor, Editorial Board and homepage, but all articles carry the citation Frontiers in Surgery.
Frontiers in Surgery calls upon medical professionals and scientists from all surgical specialties to publish their experimental and clinical studies in this journal. By assembling all surgical specialties, which nonetheless retain their independence, under the common umbrella of Frontiers in Surgery, a powerful publication venue is created. Since there is often overlap and common ground between the different surgical specialties, assembly of all surgical disciplines into a single journal will foster a collaborative dialogue amongst the surgical community. This means that publications, which are also of interest to other surgical specialties, will reach a wider audience and have greater impact.
The aim of this multidisciplinary journal is to create a discussion and knowledge platform of advances and research findings in surgical practice today to continuously improve clinical management of patients and foster innovation in this field.