scholarly journals LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

2020 ◽  
Author(s):  
Xiao-Xue Zhang ◽  
Meng Wei ◽  
Lu-Xiang Shang ◽  
Yan-Mei Lu ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multivariate Logistic Regression ◽  
Principal Component Regression Analysis ◽  
Independent Risk Factors

Abstract Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS occurred. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

scholarly journals LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

2020 ◽  
Author(s):  
Xiao-Xue Zhang ◽  
Meng Wei ◽  
Lu-Xiang Shang ◽  
Yan-Mei Lu ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multivariate Logistic Regression ◽  
Principal Component Regression Analysis ◽  
Independent Risk Factors

Abstract Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

scholarly journals LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-Xue Zhang ◽  
Meng Wei ◽  
Lu-Xiang Shang ◽  
Yan-Mei Lu ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multivariate Logistic Regression ◽  
Principal Component Regression Analysis ◽  
Independent Risk Factors

Abstract Background This study explored the relationships between the low−/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients. Methods NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS. Results The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile (≥2.73) was 16.23-fold that of the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients. Conclusions LDL-C/HDL-C > 1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

scholarly journals LDL-C/HDL-C is Associated With Ischaemic Stroke in Patients With Non-Valvular Atrial Fibrillation: A Case-Control Study

2020 ◽  
Author(s):  
Xiao-Xue Zhang ◽  
Meng Wei ◽  
Lu-Xiang Shang ◽  
Yan-Mei Lu ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multivariate Logistic Regression ◽  
Principal Component Regression Analysis ◽  
Independent Risk Factors

Abstract Background This study explored relationships between low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in non-valvular atrial fibrillation (NVAF) patients in Xinjiang, which could provide a theoretical and therapeutic basis for patients with NVAF. Methods NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into the stroke (798 patients) and control (2671 patients) groups according to whether acute IS occurred. Univariate and multivariate logistic regression analysis was used to examine the independent risk factors for IS in NVAF patients. We used factor analysis and principal component regression analysis to analyse the main influencing factors of IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the optimal cut-off value of LDL-C/HDL-C in predicting IS. Results Multivariate logistic regression showed that the risk of IS in the highest quartile of LDL-C/HDL-C (≥ 2.73) was 16.23-fold that in the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients (BMI ≥ 28 kg/m2) than in normal-weight subjects; IS risk was 3.15-fold higher in smoking than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, optimal critical value was 2.33, sensitivity was 63.53%, and specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients. Conclusions LDL-C/HDL-C > 1.22, smoking and BMI ≥ 24 kg/m2 were independent risk factors for IS in NVAF patients, of which LDL-C/HDL-C was the main risk factor.

scholarly journals Nomogram for Predicting Persistent Organ Failure with Acute Pancreatitis in Pregnancy

2022 ◽  
Author(s):  
Chengcheng Sheng ◽  
Zongxu Xu ◽  
Jun Wang
Keyword(s):  
Risk Factors ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Organ Failure ◽  
Multivariate Logistic Regression Analysis ◽  
Concordance Index ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
In Pregnancy

Abstract Background: Acute pancreatitis in pregnancy (APIP) with persistent organ failure (POF) poses a high risk of death for mother and fetus. This study sought to create a nomogram model for early prediction of POF with APIP patients.Methods: We conducted a cross-sectional study on APIP patients with organ failure (OF) between January 2012 and March 2021 in a university hospital. 131 patients were collected. Their clinical courses and pregnancy outcomes were obtained. Risk factors for POF were identified by univariate and multivariate logistic regression analysis. Prediction models with POF were built and nomogram was plotted. The performance of the nomogram was evaluated by using a bootstrapped-concordance index and calibration plots.Results: Hypertriglyceridemia was the most common etiology in this group of APIP patients, which accounted for 50% of transient organ failure (TOF) and 72.3% of POF. All in-hospital maternal death was in the POF group (P<0.05), which also had a significantly higher perinatal mortality rate than the TOF group (P<0.05). Univariate and multivariate logistic regression analysis determined that lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were independent risk factors for predicting POF in APIP. A nomogram for POF was created by using the four indicators. The area under the curve was 0.875 (95% confidence interval 0.80–0.95). The nomogram had a bootstrapped-concordance index of 0.85 and was well-calibrated.Conclusions: Hypertriglyceridemia was the leading cause of organ failure-related APIP. Lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were the independent risk factors of POF in APIP. Our nomogram model showed an effective prediction of POF with the four indicators in APIP patients.

scholarly journals Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhaoli Meng ◽  
Wei Fang ◽  
Mei Meng ◽  
Jicheng Zhang ◽  
Qizhi Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Maternal Mortality ◽  
Predictive Models ◽  
Multivariate Logistic Regression Analysis ◽  
Fetal Mortality ◽  
Multivariate Logistic Regression ◽  
Acute Fatty Liver ◽  
Independent Risk Factors

Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.

scholarly journals Analysis of Risk Factors For Sepsis And Indicators of Prognosis In Patients With Sepsis: A Retrospective Observational Study

2021 ◽  
Author(s):  
Huifeng Wang ◽  
Zhiling Zhao ◽  
Zhao-hui Tong
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Albumin Level ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Abstract Background: To investigate the independent risk factors for sepsis and the prognostic indicators of sepsis-related mortality to guide clinical practice.Methods: Adult patients diagnosed with sepsis in the respiratory intensive care unit (RICU), emergency ICU (EICU), and surgical ICU (SICU) of Beijing Chao-Yang Hospital, Capital Medical University, from January 2016 to April 2021 were enrolled. Comorbidities, complications, and laboratory indicators were retrospectively analyzed. Variables with a p value < 0.05 in the univariate analysis were entered into multivariate logistic regression analysis to identify the independent risk factors for sepsis. Receiver operating characteristic curve (ROC) analysis was used for those variables with P < 0.05 in multivariate regression to evaluate the fit of the predictive model and its prognostic efficacy. Results: A total of 123 adult patients with sepsis were enrolled, with 80 males and 43 females and a mean age of 61.56 ± 17.12 years. Acute respiratory distress syndrome (ARDS) occurred in 84 patients (68.3%), acute kidney injury (AKI) occurred in 28 patients (22.8%), acute myocardial injury (AMI) occurred in 6 patients (4.9%), disseminated intravascular coagulation (DIC) occurred in 14 patients (11.4%), septic shock occurred in 40 patients (32.5%), and 41 patients (33.3%) died. Multivariate logistic regression analysis showed that mean arterial pressure (MAP), acute physiology and chronic health evaluation II (APACHE II) score, albumin level, and the presence of DIC were independent risk factors for sepsis (P < 0.05). The area under the ROC curve for the model including MAP, albumin, and APACHE II score was the highest at 0.890.Conclusion: The MAP, APACHE II score, albumin level, and DIC were independent risk factors for sepsis. The inclusion of the MAP, albumin level, and APACHE II score in the model yielded the most accurate prediction of the risk of mortality.

Risk Factors for Wound Infection in Outpatients With Lower Extremity Burns

2020 ◽  
pp. 000313482095238
Author(s):  
Husayn A. Ladhani ◽  
Brian T. Young ◽  
Sarah E. Posillico ◽  
Charles J. Yowler ◽  
Christopher P. Brandt ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lower Extremity ◽  
Wound Infection ◽  
Logistic Regression Analysis ◽  
Total Body Surface Area ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Background We sought to evaluate risk factors for wound infection in patients with lower extremity (LE) burn. Methods Adults presenting with LE burn from January 2014 to July 2015 were included. Data regarding demographics, injury characteristics, and outcomes were obtained. The primary outcome was wound infection. Multivariate logistic regression analysis was performed to identify independent risk factors for wound infection. Results 317 patients were included with a mean age of 43 years and median total body surface area of .8%; 22 (7%) patients had a component of full-thickness (FT) burn; and 212 (67%) patients had below-the-knee (BTK) burn. The incidence of wound infection was 15%. The median time to infection was 5 days, and majority (61%) of the patients developed wound infection by day 5. Patients who developed wound infection were more likely to have an FT burn (22% vs. 5%, P < .001) and BTK burn (87% vs. 64%, P = .002), without a difference in other variables. Multivariate logistic regression analysis showed age (Odds ratio (OR) 1.02 and CI 1.00-1.04), presence of FT burn (OR 5.33 and CI 2.09-13.62), and BTK burn (OR 3.42 and CI 1.37-8.52) as independent risk factors for wound infection (area under the curve = .72). Conclusion Age, presence of FT burn, and BTK burn are independent risk factors for wound infection in outpatients with LE burns.

Clinical risk predictors for bradycardia, supraventricular tachycardia and epilepsy necessitating therapy in patients with unexplained syncope monitored by insertable cardiac monitor

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Onuki ◽  
M Shoji ◽  
M Kikuchi ◽  
T Asano ◽  
H Suzuki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Factor ◽  
Independent Risk Factor ◽  
Sick Sinus Syndrome ◽  
Unexplained Syncope ◽  
Independent Risk Factors

Abstract Background Insertable cardiac monitors (ICMs) allow for lengthy monitoring of cardiac rhythm and improve diagnostic yield in patients with unexplained syncope. In most cardiac syncope cases, sick sinus syndrome, atrioventricular block, and paroxysmal supraventricular tachycardia (SVT) are detected using ICMs. On the other hand, epileptic seizures are sometimes diagnosed as unexplained syncope because in these situations, the loss of consciousness is a similar manifestation. Thus, the population of patients with unexplained syncope monitored by ICMs includes epileptic patients. Clinical risk factors for bradycardia, SVT and epilepsy that necessitate therapy in patients with unexplained syncope are not well known. If these risks can be clarified, clinicians could provide more specific targeted monitoring. Purpose We aimed to identify these predictors. Methods We retrospectively reviewed medical records of consecutive patients who received ICMs to monitor unexplained syncope in three medical facilities. We performed Cox's stepwise logistic regression analysis to identify significant independent risk factors for bradycardia, SVT, and epilepsy. Results One hundred thirty-two patients received ICMs to monitor unexplained syncope. During the 17-month follow-up period, 19 patients (10 patients had sick sinus syndrome and 9 had atrioventricular block) needed pacemaker for bradycardia; 8 patients (3 had atrial flutter, 4 had atrial tachycardia, and 1 had paroxysmal atrial fibrillation) needed catheter ablation for SVT; and 9 patients needed antiepileptic agents from the neurologist.Stepwise logistic regression analysis indicated that syncope during effort (odds ratio [OR] = 3.41; 95% confidence interval [CI], 1.21 to 9.6; p=0.02) was an independent risk factor for bradycardia. Palpitation before syncope (OR = 9.46; 95% CI, 1.78 to 50.10; p=0.008) and history of atrial fibrillation (OR = 10.1; 95% CI, 1.96 to 52.45; p=0.006) were identified as significant independent prognostic factors for SVT. Syncope while supine (OR = 11.7; 95% CI, 1.72 to 79.7; p=0.01) or driving (OR = 15.6; 95% CI, 2.10 to 115.3; p=0.007) was an independent factor for epileptic seizure. Conclusions ICMs are useful devices for diagnosing unexplained syncope. Palpitation, atrial fibrillation and syncope during effort were independent risk factors for bradycardia and for SVT. Syncope while supine or driving was an independent risk factor for epilepsy. We should carefully follow up of patients with these risk factors. Funding Acknowledgement Type of funding source: None

scholarly journals Diagnostic Utility of Combined 2D Ultrasonography and Contrast-Enhanced Ultrasonography in Evaluation of Carotid Plaque Vulnerability for Predicting Recurrent Ischemic Strokes

2021 ◽  
Author(s):  
Fuyong Ye ◽  
Yuwen Yang ◽  
Yinting Liang ◽  
Jianhua Liu
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography ◽  
Independent Risk Factors

Abstract Objective: To evaluate the sensitivity and specificity of combined 2D ultrasonography (USG) and contrast-enhanced ultrasonography (CEUS) in analyzing the carotid plaque vulnerability for predicting the recurrent ischemic strokes (IS). Methods: One hundred and fifteen patients with first IS were studied by 2D USG and CEUS. The carotid plaques were then classified on the basis of echogenicity (2D USG) and neovascularization (CEUS). The presence or absence of recurrent IS was considered as the dependent variable. Age, gender, body mass index (BMI), hypertension, hyperglycemia, hyperlipidemia, history of smoking and drinking, type of plaque echogenicity, and grade of plaque neovascularization were considered as independent variables. The risk factors of recurrent IS were analyzed by both univariate and multivariate logistic regression analysis. Finally, the sensitivity and specificity of combined 2D USG and CEUS in the diagnosis of recurrent IS was evaluated by receiver operating characteristic curve. Results: Univariate logistic regression analysis revealed that hypertension, echogenicity type, and grade of plaque neovascularization were predictors of recurrent IS. Further, multivariate logistic regression analysis revealed that the echogenicity type (OR=0.282, P=0.012) and grade of plaque neovascularization (OR=7.408, P<0.0001) were independent risk factors for recurrent IS. The sensitivity, specificity, and area under the curve of combined method were 0.865, 0.769, and 0.817, respectively (95%CI: 0.733-0.902, P<0.0001), which were higher than both 2D USG and CEUS.Conclusions: The echogenicity type and grade of plaque neovascularization are independent risk factors for recurrent IS. The combination of two methods has high sensitivity and specificity in predicting the recurrent IS.

scholarly journals Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao-ran Zhang ◽  
Ming-you Xu ◽  
Xiong-gang Yang ◽  
Feng Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

IntroductionVenous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.MethodsWe retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.ResultsIn the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.ConclusionThe prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

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