A Nomogram to Predict Lifestyle Factors for Recurrence of Large-Vessel Ischemic Stroke

Lifestyle, clinical, and occupational risk factors of recurrent stroke among the working-age group: A systematic review and meta-analysis

Background

Stroke recurrence is increasing in the working-age population. This study aimed to summarize and evaluate the risk factors for recurrent stroke among the working-age population.

Methods

Relevant studies were extracted from several databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Fixed- or random-effects estimates of the pooled odds ratio (OR) and 95% confidence interval (CI) of risk factors for recurrent stroke were generated based on heterogeneity. Subgroup and publication bias analyses were also performed.

Results

Seventeen studies were included in the meta-analysis. Pooled effects results revealed that the risk of recurrent stroke in the working-age group was as follows: Diabetes (OR = 1.85, 95% CI: 1.47, 2.32), hypertension (OR = 1.27, 95% CI: 1.12, 1.44), smoking (OR = 1.52, 95% CI: 1.27, 1.81), history of cardiac disease (OR = 2.86, 95% CI: 2.22, 3.67), history of stroke (OR = 2.45, 95% CI: 1.81, 3.31), and National Institutes of Health stroke severity score (OR = 1.09, 95% CI: 1.03, 1.15).

Conclusion

These findings suggest that several factors, such as diabetes, hypertension, smoking, history of cardiac disease and stroke, and severity of a stroke, are potential risk factors for recurrent stroke in the working-age group. Therefore, strategies to reduce those risk factors should be adopted and attention paid to prevent recurrent stroke among working-age populations.

Prediction of Ischemic Stroke Recurrence Based on COX Proportional Risk Regression Model and Evaluation of the Effectiveness of Patient Intensive Care Interventions

With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients' ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients.

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.