Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

Machine learning-based prediction model of lower extremity deep vein thrombosis after stroke

This study aimed to apply machine learning (ML) techniques to develop and validate a risk prediction model for post-stroke lower extremity deep vein thrombosis (DVT) based on patients' limb function, activities of daily living (ADL), clinical laboratory indicators, and DVT preventive measures. We retrospectively analyzed 620 stroke patients. Eight ML models-logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), neural network (NN), extreme gradient boosting (XGBoost), Bayesian (NB), and K-nearest neighbor (KNN)-were used to build the model. These models were extensively evaluated using ROC curves, AUC, PR curves, PRAUC, accuracy, sensitivity, specificity, and clinical decision curves (DCA). Shapley's additive explanation (SHAP) was used to determine feature importance. Finally, based on the optimal ML algorithm, different functional feature set models were compared with the Padua scale to select the best feature set model. Our results indicated that the RF algorithm demonstrated superior performance in various evaluation metrics, including AUC (0.74/0.73), PRAUC (0.58/0.58), accuracy (0.75/0.77), and sensitivity (0.78/0.80) in both the training set and test set. DCA analysis revealed that the RF model had the highest clinical net benefit. SHAP analysis showed that D-dimer had the most significant influence on DVT, followed by age, Brunnstrom stage (lower limb), prothrombin time (PT), and mobility ability. The RF algorithm can predict post-stroke DVT to guide clinical practice.

Generation of a Predictive Clinical Model for Isolated Distal Deep Vein Thrombosis (ICMVT) Detection

BACKGROUND Isolated distal deep vein thrombosis (ICMVT) increases the risk of pulmonary embolism. Although predictive models are available, their utility in predicting the risk is unknown. To develop a clinical prediction model for isolated distal calf muscle venous thrombosis, data from 462 patients were used to assess the independent risk variables for ICMVT. MATERIAL AND METHODS The area under curve (AUC) for Model A and Model B were calculated and other risk factors were based on age, pitting edema in the symptomatic leg, calf swelling with least 3 cm larger than the asymptomatic leg, recent bed rest for 3 days or more in the past 4 weeks, requiring general or major surgery with regional anesthesia, sex, and local tenderness distributed along the deep venous system as independent predictors of calf muscle venous thrombosis. Model A includes the risk variables for C-reactive protein and D-dimer. RESULTS The area under ROC curve for Model A training set was 0.924 (95% CI: 0.895-0.952), the area under ROC curve for Model B training set was 0.887 (95% CI: 0.852-0.922), and the AUC difference between the 2 models was statistically significant (P<0.001); the area under ROC curve for Model A obtained in the validation set was 0.902 (95% CI: 0.844-0.961), the area under ROC curve for Model B was 0.842 (95% CI: 0. 0.773-0.910), and the difference between the 2 models was statistically significant (P=0.012). CONCLUSIONS Predictive Model A better predicts isolated calf muscle venous thrombosis and is able to help clinicians rapidly and early diagnose ICMVT, displaying higher utility for missed diagnosis prevention and disease therapy.

Predictors and outcomes of deep venous thrombosis in patients with acute ischemic stroke: results from the Chinese Stroke Center Alliance

BACKGROUND

No large-scale, multicenter studies have explored the incidence rate and predictors of deep vein thrombosis (DVT) in patients with acute ischemic stroke (AIS). We aimed to determine the risk factors of DVT, and assess the association between DVT and clinical outcomes in AIS patients.

METHODS

In total, 106,612 patients with AIS enrolled in the Chinese Stroke Center Alliance between August 2015 and July 2019 were included. The predictors of DVT in AIS patients were screened based on the logistic regression analysis for the comparison of the characteristics and clinical outcomes of patients with and without DVT.

RESULTS

The overall incidence of DVT after AIS was 4.7%. Factors associated with increased incidence of DVT included advanced age, female sex, high admission National Institutes of Health Stroke Scale score, history of cerebral hemorrhage, transient ischemic attack (TIA), dyslipidemia, atrial fibrillation, and peripheral vascular disease, International Normalized Ratio (INR) <0.8 or >1.5, and blood uric acid >420 μmol/L. Ambulation and early antithrombotic therapy were associated with a lower incidence of DVT. Patients with DVT was associated with longer hospital stay (OR=1.44, 95% CI: 1.35-1.54), and higher in-hospital mortality (OR=1.68, 95% CI: 1.25-2.27).

CONCLUSIONS

This large-scale, multi-center study showed that the occurrence of DVT in AIS patients is associated with various modifiable and objective indicators, such as abnormal INR and uric acid >420 μmol/L. Ambulatory status and early antithrombotic therapy can reduce the occurrence of DVT in AIS patients. In AIS patients, DVT may prolong the hospital stay and increase the risk of in-hospital mortality. Future research should focus on the clinical implementation of existing evidence on DVT prevention in AIS patients.

The interaction effect of transfusion history and previous stroke history on the risk of venous thromboembolism in stroke patients: a prospective cohort study

BACKGROUND

Blood transfusion and previous stroke history are two independent risk factors of venous thromboembolism (VTE) in stroke patients. Whether the potential interaction of transfusion history and previous stroke history is associated with a greater risk of VTE remains unclear. This study aims to explore whether the combination of transfusion history and previous stroke history increases the risk of VTE among Chinese stroke patients.

METHODS

A total of 1525 participants from the prospective Stroke Cohort of Henan Province were enrolled in our study. Multivariate logistic regression models were used to explore the associations among transfusion history, previous stroke history and VTE. The interaction was evaluated on both multiplicative and additive scales. The odds ratio (95% CI), relative excess risk of interaction (RERI), attributable proportion (AP), and synergy index (S) of interaction terms were used to examine multiplicative and additive interactions. Finally, we divided our population into two subgroups by National Institutes of Health Stroke Scale (NIHSS) score and re-evaluated the interaction effect in both scales.

RESULTS

A total of 281 (18.4%) participants of 1525 complicated with VTE. Transfusion and previous stroke history were associated with an increased risk of VTE in our cohort. In the multiplicative scale, the combination of transfusion and previous stroke history was statistically significant on VTE in both unadjusted and adjusted models (P<0.05). For the additive scale, the RERI shrank to 7.016 (95% CI: 1.489 ~ 18.165), with the AP of 0.650 (95% CI: 0.204 ~ 0.797) and the S of 3.529 (95% CI: 1.415 ~ 8.579) after adjusting for covariates, indicating a supra-additive effect. In subgroups, the interaction effect between transfusion history and previous stroke history was pronouncedly associated with the increased risk of VTE in patients with NIHSS score > 5 points (P<0.05).

CONCLUSIONS

Our results suggest that there may be a potential synergistic interaction between transfusion history and previous stroke history on the risk of VTE. Besides, the percentage of VTE incidence explained by interaction increased with the severity of stroke. Our findings will provide valuable evidence for thromboprophylaxis in Chinese stroke patients.

A nomogram for individualized prediction of lower extremity deep venous thrombosis in stroke patients: A retrospective study

To develop and validate a nomogram for individualized prediction of lower extremity deep venous thrombosis (DVT) in stroke patients based on extremity function and daily living ability of stroke patients. In this study, 423 stroke patients admitted to the Rehabilitation Medical Center of the First Affiliated Hospital of Nanjing Medical University from December 2015 to February 2019 were taken as the subjects, who were divided into the DVT group (110) and No-DVT group (313) based on the existence of DVT. Inter-group comparison of baseline data was performed by 1-way Analysis of Variance, Kruskal-Wallis rank-sum test, or Pearson chi-square test. Data dimensions and predictive variables were selected by least absolute shrinkage and selection operator (LASSO); the prediction model was developed and the nomogram was prepared by binary logistics regression analysis; the performance of the nomogram was identified by the area under the receiver operating characteristic curve (AUC), Harrell's concordance index, and calibration curve; and the clinical effectiveness of the model was analyzed by clinical decision curve analysis. Age, Brunnstrom stage (lower extremity), and D-dimer were determined to be the independent predictors affecting DVT. The independent predictors mentioned above were developed and presented as a nomogram, with AUC and concordance index of 0.724 (95% confidence interval [CI]: 0.670-0.777), indicating the satisfactory discrimination ability of the nomogram. The P value of the results of the Hosmer-Lemeshow test was 0.732, indicating good fitting of the prediction model. Decision curve analysis showed that the clinical net benefit of this model was 6% to 50%. We developed a nomogram to predict lower extremity deep venous thrombosis in stroke patients, and the results showed that the nomogram had satisfactory prediction performance and clinical efficacy.

Nomogram to predict hemorrhagic transformation for acute ischemic stroke in Western China: a retrospective analysis

Background and purpose

Hemorrhagic transformation (HT) is the most alarming complication of acute ischemic stroke. We aimed to identify risk factors for HT in Chinese patients and attempted to develop a nomogram to predict individual cases.

Methods

A retrospective study was used to collect the demographic and clinical characteristics of ischemic stroke patients at the Second Affiliated Hospital of Chongqing Medical University (development cohort) and Chongqing Sanbo Changan Hospital (validation cohort) from October 2013 to August 2020. Univariate analysis and multivariate analysis were used to identify the risk factors of patients in the development cohort. The nomogram was generated, and internal validation was performed. We used the area under the receiver-operating characteristic curve (AUC-ROC) to assess the discrimination and used the Hosmer–Lemeshow test to calibrate the model. To further verify the predictability and accuracy of the model, we performed an external validation of the patients in the validation cohort.

Results

A total of 570 patients were used to generate the nomogram. After univariate analysis and multivariate logistic regression, the remaining 7 variables (diabetes mellitus, atrial fibrillation, total cholesterol, fibrous protein, cerebral infarction area, NIHSS score and onset-to-treatment) were independent predictors of HT and used to compose the nomogram. The area under the receiver-operating characteristic curve of the model was 0.889 (95% CI, 0.841–0.938), and the calibration was good (P = 0.487 for the Hosmer–Lemeshow test). The model was validated externally with an AUC-ROC value of 0.832 (95% CI, 0.727–0.938).

Conclusions

The nomogram prediction model in this study has good predictive ability, accuracy and discrimination, which can improve the diagnostic efficiency of HT in patients with acute ischemic stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02678-2.

A Nomogram for Individualized Prediction of Calf Muscular Vein Thrombosis in Stroke Patients During Rehabilitation: A Retrospective Study

Objectives: To develop a nomogram for predicting calf muscle veins thrombosis (CMVT) in stroke patients during rehabilitation. Methods: We enrolled 360 stroke patients from the Rehabilitation Medicine Center from December 2015 to February 2019. Of the participants, 123 were included in the CMVT group and 237 in the no CMVT group. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. Performance and clinical utility of the nomogram were generated using the Harrell's concordance index, calibration curve, and decision curve analysis (DCA). Results: Age, Brunnstrom stage (lower extremity), D-dimer, and antiplatelet therapy were associated with the occurrence of CMVT. The prediction nomogram showed satisfactory performance with a concordance index of 0.718 (95% CI: 0.663-0.773) in internal verification. The Hosmer-Lemeshow test, P = .217, suggested that the model was of goodness-of-fit. In addition, the DCA demonstrated that the CMVT nomogram had a good clinical net benefit. Conclusions: We developed a nomogram that could help clinicians identify high-risk groups of CMVT in stroke patients during rehabilitation for early intervention.

Risk factors for relapse and nomogram for relapse probability prediction in patients with minor ischemic stroke

BACKGROUND

The identification of risk factors for recurrence in patients with minor ischemic stroke (MIS) is a critical medical need.

AIM

To develop a nomogram for individualized prediction of in-hospital recurrence in MIS patients.

METHODS

Based on retrospective collection, a single-center study was conducted at the First Affiliated Hospital of Anhui Medical University from January 2014 to December 2019. Univariate and multivariate logistic regression analyses were used to determine the risk factors associated with MIS recurrence. The least absolute shrinkage and selection operator regression was performed for preliminary identification of potential risk factors. Uric acid, systolic blood pressure, serum total bilirubin (STBL), and ferritin were integrated for nomogram construction. The predictive accuracy and calibration of the nomogram model were assessed by the area under the receiver operating characteristic curve (AUC-ROC) and Hosmer-Lemeshow test, respectively.

RESULTS

A total of 2216 MIS patients were screened. Among them, 155 were excluded for intravascular therapy, 146 for unknown National Institutes of Health Stroke Scale score, 195 for intracranial hemorrhage, and 247 for progressive stroke. Finally, 1244 patients were subjected to further analysis and divided into a training set (n = 796) and a validation set (n = 448). Multivariate logistic regression analysis revealed that uric acid [odds ratio (OR): 0.997, 95% confidence interval (CI): 0.993-0.999], ferritin (OR: 1.004, 95%CI: 1.002-1.006), and STBL (OR: 0.973, 95%CI: 0.956-0.990) were independently associated with in-hospital recurrence in MIS patients. Our model showed good discrimination; the AUC-ROC value was 0.725 (95%CI: 0.646-0.804) in the training set and 0.717 (95%CI: 0.580-0.785) in the validation set. Moreover, the calibration between nomogram prediction and the actual observation showed good consistency. Hosmer-Lemeshow test results confirmed that the nomogram was well-calibrated (P = 0.850).

CONCLUSION

Our present findings suggest that the nomogram may provide individualized prediction of recurrence in MIS patients.