Construction and verification of a nomogram predicting the risk of preoperative deep vein thrombosis progression after elective spine surgery

Yan, Xin; Huang, Kate; Jia, Mengxian; Yang, Jiali; Zhang, Peng; He, Yaozhi; Lai, Jiaxin; Chen, Minghang; Fan, Shikang; Li, Sheng; Teng, Hui

doi:10.1016/j.clineuro.2022.107439

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…In the existing study, we noted that Xin Yan et al predicted the risk of postoperative DVT progression in patients undergoing spinal surgery by plotting a nomogram, and they eventually screened five preoperative and intraoperative indicators, which included the time of surgery (Yan et al, 2022). Unlike their study, firstly, the present study included a large number of cases for calculation in patients with spinal infection, making the nomogram more relevant.…”

Section: Discussionmentioning

confidence: 95%

“…Studies have showed that risk factors for venous thrombosis following spinal surgery are associated with high age, female, spinal fusion, big volume blood loss patients, operation time, and hypertension, diabetes, and walking issue. A series of risk prediction models for deep venous thrombosis after surgery for patients with spinal surgery and fracture (Cheng et al, 2022;Hu et al, 2022;Yan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

Zhang

et al. 2023

Front. Cell. Infect. Microbiol.

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ObjectiveTo investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients’ preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.MethodAccording to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).ResultThe incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.ConclusionIn this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

Zhang

et al. 2023

Front. Cell. Infect. Microbiol.

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“…At the same time, the researches on risk factors of SSI are also increasingly in-depth. Most of the previous studies on risk factors are related to age, diabetes, subcutaneous fat thickness, number of surgical segments, and so on [ 22 ]. However, it has been reported recently that fat infiltration of LMM may be a new risk factor for SSI [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram to predict the risk of surgical site infection within 1 month after transforaminal lumbar interbody fusion

et al. 2023

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Objective Surgical site infection (SSI), a common serious complication within 1 month after transforaminal lumbar interbody fusion (TLIF), usually leads to poor prognosis and even death. The objective of this study is to investigate the factors related to SSI within 1 month after TLIF. We have developed a dynamic nomogram to change treatment or prevent infection based on accurate predictions. Materials and methods We retrospectively analyzed 383 patients who received TLIF at our institution from January 1, 2019, to June 30, 2022. The outcome variable in the current study was the occurrence of SSI within 1 month after surgery. Univariate logistic regression analysis was first performed to assess risk factors for SSI within 1 month after surgery, followed by inclusion of significant variables at P < 0.05 in multivariate logistic regression analysis. The independent risk variables were subsequently utilized to build a nomogram model. The consistency index (C-index), calibration curve and receiver operating characteristic curve were used to evaluate the performance of the model. And the decision curve analysis (DCA) was used to analyze the clinical value of the nomogram. Results The multivariate logistic regression models further screened for three independent influences on the occurrence of SSI after TLIF, including lumbar paraspinal (multifidus and erector spinae) muscles (LPM) fat infiltration, diabetes and surgery duration. Based on the three independent factors, a nomogram prediction model was built. The area under the curve for the nomogram including these predictors was 0.929 in both the training and validation samples. Both the training and validation samples had high levels of agreement on the calibration curves, and the nomograms C-index was 0.929 and 0.955, respectively. DCA showed that if the threshold probability was less than 0.74, it was beneficial to use this nomograph to predict the risk of SSI after TLIF. In addition, the nomogram was converted to a web-based calculator that provides a graphical representation of the probability of SSI occurring within 1 month after TLIF. Conclusion A nomogram including LPM fat infiltration, surgery duration and diabetes is a promising model for predicting the risk of SSI within 1 month after TLIF. This nomogram assists clinicians in stratifying patients, hence boosting decision-making based on evidence and personalizing the best appropriate treatment.

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Nomogram for predicting venous thromboembolism after spinal surgery

Kong,

Shao,

et al. 2023

Eur Spine J

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Purpose This study aimed to establish a nomogram to predict the risk of venous thromboembolism (VTE), identifying potential risk factors, and providing theoretical basis for prevention of VTE after spinal surgery. Methods A retrospective analysis was conducted on 2754 patients who underwent spinal surgery. The general characteristics of the training group were initially screened using univariate logistic analysis, and the LASSO method was used for optimal prediction. Subsequently, multivariate logistic regression analysis was performed to identify independent risk factors for postoperative VTE in the training group, and a nomogram for predict risk of VTE was established. The discrimination, calibration, and clinical usefulness of the nomogram were separately evaluated using the C-index, receiver operating characteristic curve, calibration plot and clinical decision curve, and was validated using data from the validation group finally. Results Multivariate logistic regression analysis identified 10 independent risk factors for VTE after spinal surgery. A nomogram was established based on these independent risk factors. The C-index for the training and validation groups indicating high accuracy and stability of the model. The area under the receiver operating characteristic curve indicating excellent discrimination ability; the calibration curves showed outstanding calibration for both the training and validation groups. Decision curve analysis showed the clinical net benefit of using the nomogram could be maximized in the probability threshold range of 0.01–1. Conclusion Patients undergoing spinal surgery with elevated D-dimer levels, prolonger surgical, and cervical surgery have higher risk of VTE. The nomogram can provide a theoretical basis for clinicians to prevent VTE.

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Construction and verification of a nomogram predicting the risk of preoperative deep vein thrombosis progression after elective spine surgery

Cited by 7 publications

References 23 publications

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

Development and validation of a nomogram to predict the risk of surgical site infection within 1 month after transforaminal lumbar interbody fusion

Nomogram for predicting venous thromboembolism after spinal surgery

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