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

Prediction of early bladder outcomes after spinal cord injury: The HALT score

AIMS

Neurogenic bladder (NB) is a prevalent and debilitating consequence of spinal cord injury (SCI). Indeed, the accurate prognostication of early bladder outcomes is crucial for patient counseling, rehabilitation goal setting, and personalized intervention planning.

METHODS

A retrospective exploratory analysis was conducted on a cohort of consecutive SCI patients admitted to a rehabilitation facility in China from May 2016 to December 2022. Demographic, clinical, and electrophysiological data were collected within 40 days post-SCI, with bladder outcomes assessed at 3 months following SCI onset.

RESULTS

The present study enrolled 202 SCI patients with a mean age of 40.3 ± 12.3 years. At 3 months post-SCI, 79 participants exhibited complete bladder emptying. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses identified the H-reflex of the soleus muscle, the American Spinal Injury Association Lower Extremity Motor Score (ASIA-LEMS), and the time from lesion to rehabilitation facility (TLRF) as significant independent predictors for bladder emptying. A scoring system named HALT was developed, yielding a strong discriminatory performance with an area under the receiver operating characteristics curve (aROC) of 0.878 (95% CI: 0.823-0.933). A simplified model utilizing only the H-reflex exhibited excellent discriminatory ability with an aROC of 0.824 (95% CI: 0.766-0.881). Both models demonstrated good calibration via the Hosmer-Lemeshow test and favorable clinical net benefits through decision curve analysis (DCA). In comparison to ASIA-LEMS, both the HALT score and H-reflex showed superior predictive accuracy for bladder outcome. Notably, in individuals with incomplete injuries, the HALT score (aROC = 0.973, 95% CI: 0.940-1.000) and the H-reflex (aROC = 0.888, 95% CI: 0.807-0.970) displayed enhanced performance.

CONCLUSION

Two reliable models, the HALT score and the H-reflex, were developed to predict bladder outcomes as early as 3 months after SCI onset. Importantly, this study provides hitherto undocumented evidence regarding the predictive significance of the soleus H-reflex in relation to bladder outcomes in SCI patients.

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.

Development, validation, and visualization of a novel nomogram to predict stroke risk in patients

Background

Stroke is the second leading cause of death worldwide and a major cause of long-term neurological disability, imposing an enormous financial burden on families and society. This study aimed to identify the predictors in stroke patients and construct a nomogram prediction model based on these predictors.

Methods

This retrospective study included 11,435 participants aged >20 years who were selected from the NHANES 2011-2018. Randomly selected subjects (n = 8531; 75%) and the remaining subjects comprised the development and validation groups, respectively. The least absolute shrinkage and selection operator (LASSO) binomial and logistic regression models were used to select the optimal predictive variables. The stroke probability was calculated using a predictor-based nomogram. Nomogram performance was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve with 1000 bootstrap resample validations. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram.

Results

According to the minimum criteria of non-zero coefficients of Lasso and logistic regression screening, older age, lower education level, lower family income, hypertension, depression status, diabetes, heavy smoking, heavy drinking, trouble sleeping, congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris and myocardial infarction were independently associated with a higher stroke risk. A nomogram model for stroke patient risk was established based on these predictors. The AUC (C statistic) of the nomogram was 0.843 (95% CI: 0.8186-0.8430) in the development group and 0.826 (95% CI: 0.7811, 0.8716) in the validation group. The calibration curves after 1000 bootstraps displayed a good fit between the actual and predicted probabilities in both the development and validation groups. DCA showed that the model in the development and validation groups had a net benefit when the risk thresholds were 0-0.2 and 0-0.25, respectively.

Discussion

This study effectively established a nomogram including demographic characteristics, vascular risk factors, emotional factors and lifestyle behaviors to predict stroke risk. This nomogram is helpful for screening high-risk stroke individuals and could assist physicians in making better treatment decisions to reduce stroke occurrence.