Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture

Hu, Jia-sen; Huang, Chengbin; Mao, Shu-ming; Fang, Kang-Hao; Wu, Zhiping; Zhao, You-Ming

doi:10.1186/s12893-022-01735-4

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…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%

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: 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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Bone and joint infection: past, present, and future

2024

Frontiers Research Topics

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Development of a nomogram to predict surgical site infection after closed comminuted calcaneal fracture

Cited by 2 publications

References 29 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

Bone and joint infection: past, present, and future

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