Retraction Note: Risk factors, clinical presentations and predictors of stroke among adult patients admitted to stroke unit of Jimma university medical center, south west Ethiopia: prospective observational study

Pharmacotherapy Pattern and Treatment Outcomes of Stroke Patients Admitted to Jimma University Medical Center, Ethiopia

Background

Despite the fact that stroke has been reported as one of the top three leading causes of death and morbidity in Ethiopia, there are limited data regarding the management of stroke and clinical outcomes. Hence, the present study aimed to evaluate the pharmacotherapy of stroke and factors associated with poor treatment outcomes.

Methods

A retrospective cross-sectional study was conducted at Jimma University Medical Center (JUMC) among adult stroke patients managed from 2014 to 2017. Clinical characteristics, treatment, and outcomes data were analyzed by using SPSS version 21. Multivariable logistic regression was performed to identify the predictors of poor treatment outcomes. Two-sided P < 0.05 was accepted as statistically significant.

Results

A total of 153 illegible patient cases were included in this study. The majority, 111 (72.5%), were male and the mean age of the patients was 57±13.7 years. Among 153 stroke patients, 112 (73.2%) patients presented with ischemic stroke. Aspirin and statins (78.6%) were the most commonly used treatment among ischemic stroke patients, whereas enalapril was used in about (43%) of stroke patients to treat high blood pressure. About 61 (40%) stroke patients had poor treatment outcomes; of this, 36 (23.5%) died in hospital. Older age (AOR = 1.034; 95% CI: 1.003–1.069), history of heart failure (AOR = 4.26; 95% CI: 1.58–11.48), loss of consciousness diabetes (AOR = 3.05 95% CI: 1.25–7.44), and aspiration pneumonia (AOR = 5.94; 95% CI: 2.46–14.32) were significantly associated with poor treatment outcomes.

Conclusion

Overall, treatment of stroke patients was sub-optimal and almost half of the patients had poor treatment outcomes. Availing of thrombolytic therapy, devising appropriate preventive measures of risk factors (hypertension), and decreasing preventable complication such as aspiration pneumonia could improve patient outcomes.

The outcome in patients with brain stroke: A deep learning neural network modeling

Background:

The artificial intelligence field is obtaining ever-increasing interests for enhancing the accuracy of diagnosis and the quality of patient care. Deep learning neural network (DLNN) approach was considered in patients with brain stroke (BS) to predict and classify the outcome by the risk factors.

Materials and Methods:

A total of 332 patients with BS (mean age: 77.4 [standard deviation: 10.4] years, 50.6% – male) from Imam Khomeini Hospital, Ardabil, Iran, during 2008–2018 participated in this prospective study. Data were gathered from the available documents of the BS registry. Furthermore, the diagnosis of BS was considered based on computerized tomography scans and magnetic resonance imaging. The DLNN strategy was applied to predict the effects of the main risk factors on mortality. The quality of the model was measured by diagnostic indices.

Results:

The finding of this study for 81 selected models demonstrated that ranges of accuracy, sensitivity, and specificity are 90.5%–99.7%, 83.8%–100%, and 89.8%–99.5%, respectively. Based on the optimal model (tangent hyperbolic activation function with the minimum–maximum hidden units of 10–20, max epochs of 400, momentum of 0.5, and learning rate of 0.1), the most important predictors for BS mortality were time interval after 10 years (accuracy = 92.2%), age category (75.6%), the history of hyperlipoproteinemia (66.9%), and education level (66.9%). The other independent variables are at moderate importance (66.6%) which include sex, employment status, residential place, smoking habits, history of heart disease, cerebrovascular accident type, blood pressure, diabetes, oral contraceptive pill use, and physical activity.

Conclusion:

The best means for dropping the BS load is effective BS prevention. DLNN strategy showed a surprising presentation in the prediction of BS mortality based on the main risk factors with an excellent diagnostic accuracy. Moreover, the time interval after 10 years, age, the history of hyperlipoproteinemia, and education level are the most important predictors for BS.