Construction and validation of risk prediction model for deep vein thrombosis in acute exacerbations of chronic obstructive pulmonary disease based on serum angiopoietin 2 levels

He, Jie; Sun, Jian; Zhang, Tongtong; Liu, Guangnan

doi:10.21037/apm-21-1448

Cited by 6 publications

(9 citation statements)

References 31 publications

(33 reference statements)

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“…It is abnormally elevated in the case of blood hypercoagulability or secondary hyperfibrinolysis and is a highly sensitive indicator of thrombosis and dissolution. 53 Many studies have found that the level of D-dimer in MCVT with AECOPD patients was noticeably higher than those of the AECOPD without thrombosis, and that elevated D-dimer was the independent risk factor for MCVT. 53 , 54 Like our study, these studies revealed that D-dimer levels had strong association with venous thrombosis.…”

Section: Discussionmentioning

confidence: 98%

“… 53 Many studies have found that the level of D-dimer in MCVT with AECOPD patients was noticeably higher than those of the AECOPD without thrombosis, and that elevated D-dimer was the independent risk factor for MCVT. 53 , 54 Like our study, these studies revealed that D-dimer levels had strong association with venous thrombosis. However, D-dimer can be affected by many factors, such as infection, tumor, severe hepatorenal insufficiency and pregnancy.…”

Section: Discussionmentioning

confidence: 98%

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Development of Risk Prediction Model for Muscular Calf Vein Thrombosis with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Hu¹,

Li²,

Xu³

et al. 2022

IJGM

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Purpose This study aims to establish a risk prediction model for muscular calf vein thrombosis (MCVT) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods The research sample consisted of 248 patients with AECOPD and all of them underwent vascular ultrasounds of both lower limbs in this retrospective study. Univariate analysis and multivariate logistic regression analysis were conducted on factors with significant group differences to screen for the independent risk factors of MCVT. A nomogram to predict the risk of MCVT was constructed and validated with bootstrap resampling. Results According to the exclusion criteria, 240 patients were included for analysis, divided into the MCVT group (n = 81) and the non-MCVT group (n = 159). Multivariate logistic regression analyses showed that hypertension, elevated MPV, reduced albumin (ALB), elevated D-dimer and bed rest ≥3 days were independent risk factors for MCVT in AECOPD. A nomogram model for predicting AECOPD with MCVT was established based on them. The area under the curve (AUC) of receiver operating characteristic (ROC) curve for the prediction model and the simplified Wells score was 0.784 (95% CI: 0.722–0.847) and 0.659 (95% CI: 0.583–0.735), respectively. The cut-off value and Youden index of prediction model were 0.248 and 0.454, respectively. At the same time, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 85.9%, 59.5%, 84.6%, and 77.4%, respectively. The sensitivity and specificity of the simplified Wells score were 67.9% and 56.3%, respectively. Validation by the use of bootstrap resampling revealed optimal discrimination and calibration, and the decision analysis curve (DAC) suggested that this prediction model involved high clinical practicability. Conclusion We developed a nomogram that can predict the risk of MCVT for AECOPD patients. This model has the potential to assist clinicians in making treatment recommendations and formulating corresponding prevention measures.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 98%

Development of Risk Prediction Model for Muscular Calf Vein Thrombosis with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Hu¹,

Li²,

Xu³

et al. 2022

IJGM

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“…At the same time, the ROC curve and decision curve showed that our established nomogram prediction model had more advantages than the Caprini score in predicting the risk of postoperative DVT in patients with femoral fracture. At present, nomogram models for VTE risk assessment include portal vein thrombosis after splenectomy for liver cirrhosis [29], venous thrombosis after metastatic spinal tumor [30], lower extremity DVT after acute stroke [31], and DVT during acute exacerbation of COPD [32], etc. For trauma patients, Ling et al [15]used a machine learning model to establish a modi ed Caprini score model for evaluating trauma hospitalized patients.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, COPD was an independent risk factor for DVT and was included in the model. COPD patients are usually accompanied by chronic hypoxia, poor lung function, and decreased activity tolerance, resulting in slow blood ow and high blood coagulation [32,47,48]. Therefore, we should pay attention to preoperative optimization of lung disease in patients with bone trauma.…”

Section: Discussionmentioning

confidence: 99%

A nomogram to predict postoperative deep vein thrombosis in patients with Femoral Fracture: a retrospective study

Wu¹,

Cheng²

2023

Preprint

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Objective: More active anticoagulation prevention and treatment has reduced the incidence of perioperative deep vein thrombosis (DVT) in patients with bone trauma effectively, but the incidence is still relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Further risk stratification of DVT high-risk groups remains a challenge. Therefore, the Caprini score most commonly used in the perioperative period is not applicable to orthopaedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture. Methods: We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, we compared the Caprini score and nomogram. Results: The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC=0.571,95% CI: 0.479~0.623), the calibration accuracy and identification ability of nomogram were higher (AUC=0.865,95% CI:0.780~0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score. Conclusions: The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. More specialized DVT risk assessment models should be established for early identification and individualized prevention strategies to further reduce the incidence.

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“…To eliminate the influence of extreme values on the regression results, continuous variables were dichotomously transformed using the cut-off value corresponding to the receiver operating characteristic (ROC) curve [14]. The discrimination of the risk prediction model was tested by the area under the ROC curve (AUC), and AUC >0.9 indicated high discriminatory power [15]. Hosmer-Lemeshow goodness of fit test was used to test the calibration, and p > 0.05 indicated good calibration [16].…”

Section: Methodsmentioning

confidence: 99%

A Risk Prediction Model of Serious Adverse Events After Cardiac Catheterization for Chinese Adults Patients with Moderate and Severe Congenital Heart Disease

Hu¹,

Zhang²,

Zhang³

et al. 2022

Rev. Cardiovasc. Med.

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Background: There are almost 2 million adult patients with congenital heart disease in China, and the number of moderate and severe patients is increasing. However, few studies have investigated the risk of serious adverse events (SAE) after catheterization among them. The aim of this study was to identify risk factors for SAE related to cardiac catheterization and to provide the risk scoring model for predicting SAE. Methods: A total of 690 patients with moderate and severe adult patients with congenital heart disease (ACHD) who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected and subsequently divided into a modeling group and a verification group. A univariate analysis was performed on the identified SAE risk factors, and then significant factors were included in the multivariate logistic regression model to screen for independent predictors of SAE. The receiver operating characteristic curve (ROC) and the Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model, respectively. Results: A SAE occurred in 69 (10.0%) of the 690 catheterization procedures meeting inclusion criteria. The established SAE risk calculation formula was logit(p) = -6.134 + 0.992 × pulmonary artery hypertension (yes) + 1.459 × disease severity (severe) + 2.324 × procedure type (diagnostic and interventional) + 1.436 × cTnI (≥0.028 µg/L) + 1.537 × NT-proBNP (≥126.65 pg/mL). The total score of the final risk score model based on the effect size of each predictor was 0 to 7, involving pulmonary artery hypertension (1 point), disease severity (1 point), procedure type (2 points), cTnI (1 point) and NT-proBNP (2 points), and the score greater than 3 means high risk. The C-statistic of the area under the ROC curve was 0.840 and 0.911 for the derivation and validation cohorts, respectively. According to the Hosmer-Lemeshow test, the p values in the modeling group and the verification group were 0.064 and 0.868, respectively. Conclusions: The risk prediction model developed in this study has high discrimination and calibration, which can provide reference for clinical prediction and evaluation of SAE risk after cardiac catheterization in patients with moderate and severe ACHD.

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Construction and validation of risk prediction model for deep vein thrombosis in acute exacerbations of chronic obstructive pulmonary disease based on serum angiopoietin 2 levels

Cited by 6 publications

References 31 publications

Development of Risk Prediction Model for Muscular Calf Vein Thrombosis with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Development of Risk Prediction Model for Muscular Calf Vein Thrombosis with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

A nomogram to predict postoperative deep vein thrombosis in patients with Femoral Fracture: a retrospective study

A Risk Prediction Model of Serious Adverse Events After Cardiac Catheterization for Chinese Adults Patients with Moderate and Severe Congenital Heart Disease

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