Abstrak
Stroke merupakan salah satu penyakit tidak menular yang menjadi masalah kesehatan masyarakat di dunia termasuk di Indonesia. Sekumpulan faktor risiko yang dapat berinteraksi bersama terdiri dari obesitas sentral, kadar trigliserida tinggi, kadar kolesterol HDL rendah, kadar GDP tinggi, dan hipertensi dikenal dengan istilah sindrom metabolik (IDF, 2006). Seseorang yang mengalami sindrom metabolik mempunyai peluang 3 kali untuk mengalami serangan jantung dan stroke (IDF, 2006). Sementara, menurut IDF (2006)diestimasi bahwa 20-25% penduduk dewasa di dunia mengalami sindrom metabolik. Penelitian ini bertujuan untuk mengetahui hubungan antara sindrom metabolik dengan kejadian stroke pada penduduk berusia ≥ 15 tahun di Indonesia setelah dikontrol oleh variabel kovariat. Desain studi penelitian yaitu potong lintang (cross sectional) dengan menggunakan data Riskesdas 2018. Sampel penelitian yang memenuhi kriteria inklusi dan eksklusi diperoleh sebesar 24.451 responden. Berdasarkan hasil analisis, diperoleh proporsi stroke berdasarkan diagnosis dokter sebesar 1,2%. Proporsi sindrom metabolik diperoleh sebesar 24,4%. Hasil analisis multivariat diperoleh hubungan yang signifikan antara sindrom metabolik dengan kejadian stroke (nilai p = 0,000) dengan aPOR sebesar 2,415 (95% CI: 1,883-3,099) dan diperoleh adanya variabel confounding yaitu variabel jenis kelamin dan usia. Sindrom metabolik dapat menjadi faktor yang penting untuk diperhatikan dalam upaya pencegahan dan pengendalian stroke di Indonesia.
Kata Kunci: Sindrom Metabolik; Stroke; Riskesdas 2018
Abstract
Stroke is a non-communicable disease that becomes one of public health problems in the world, including in Indonesia. A group of risk factors that can be interacted together including central obesity, high triglyceride levels, low HDL levels, high GDP levels, and hypertension are known as metabolic metabolism (IDF, 2006). The person who has metabolic syndrome has a chance 3 times to have heart attacks and strokes (IDF, 2006). Meanwhile, according to IDF (2006) it is estimated that 20-25% of the adult population in the world having metabolic syndrome. This research aims to study the relationship between metabolic syndrome and stroke event in population aged ≥ 15 years old in Indonesia after being controlled by covariate variables. The design study of this research is cross sectional using data from Riskesdas 2018. The sample of this research that met the inclusion and exclusion criteria was 24,451 respondents. Based on the result of the analysis, the proportion of strokes based on the doctor's diagnosis is 1.2%. The proportion of metabolic syndrome obtained is 24.4%. The results of multivariate analysis obtained a significant relationship between metabolic syndrome and stroke event (p = 0,000) with aPOR of 2,415 (95% CI: 1,883-3,099) and obtained confounding variables such as gender and age. Metabolic syndrome can be an important factor to consider in efforts to prevent and control stroke event in Indonesia.
Keywords: Metabolic Syndrome; Stroke; Riskesdas 2018
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Stroke is the second leading cause of death in the world. Stroke can be categorized into ischemic stroke and hemorrhagic stroke. Globally, about 4,6 million of 12,2 million new stroke cases are hemorrhagic strokes. Hemorrhagic stroke has a higher mortality rate than ischemic stroke despite having fewer cases. This study aims to determine the correlation of hemorrhagic stroke risk factors with hemorrhagic stroke occurrence in 2019 at 34 provinces of Indonesia. The research uses an ecological study design and secondary data from the 2018 Riskesdas, BPS, and research by Widyasari, Rahman, and Ningrum (2023). Bivariate analysis uses correlation and linear regression. Results showed that an increase of hemorrhagic stroke incidence rate in a province was correlated with a decrease in the prevalence of hypertension (Ï = -0.201), decrease in the prevalence of diabetes mellitus (Ï = -0.291), decrease in the proportion of obesity (Ï = -0.161), and an increase in the male population (Ï = 0.250). A province’s increase of hemorrhagic stroke prevalence is correlated with an increase in the proportion of obesity (R = 0.167) and the male population (R = 0.308). Correlation is not statistically significant, but it can be taken into consideration for further studies.
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Post-stroke condition can lead to depression and social isolation that significanlly decrease self efficacy and quality of life among stroke patient. This study aimed at identifying the effects of Audiovisual-based Stroke Self Management Education (SSME) on self efficacy and quality of life of post-stroke patients. The method applied is quasi-experimental with total of 36 respondents. Concecutive sampling techniques was implemented when choosing the research subject and divided into two groups (intervention group and control group). The results of independent t-test showed there is significant mean differences of self efficacy (p value <0.0001; α 0.05) and quality of life scores(p value = 0.001; α 0.05) between the intervention and control groups. To conclude, Audiovisual-based SSME affect significantly on self efficacy and quality of life among post-stroke patients. Audiovisual based SSME is recommended to improve self efficacy and quality of life among post-stroke patients.
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Stroke merupakan salah satu penyakit dengan risiko kematian dan kecacatan yang tinggi. Secara umum, stroke diklasifikasikan menjadi dua jenis, yaitu stroke iskemik dan stroke hemoragik. Klasifikasi jenis stroke secara cepat dan tepat diperlukan untuk menentukan jenis pengobatan dan tindakan yang tepat guna mencegah terjadinya dampak yang lebih fatal pada pasien stroke. Pada penelitian ini, klasifikasi stroke dilakukan menggunakan pendekatan machine learning. Adapun data penelitian yang digunakan adalah data stroke yang terdiri atas pemeriksaan laboratorium. Pada data penelitian tersebut, terdapat berbagai komponen pemeriksaan laboratorium yang dicatat serta memungkinkan adanya suatu pemeriksaan yang kurang relevan atau informatif dalam mengklasifikasi stroke. Apabila data tersebut tidak ditangani, akan mempengaruhi kinerja serta waktu komputasi model dalam mengklasifikasi stroke. Oleh karena itu, pada penelitian ini, Random Forest (RF) dengan seleksi fitur Recursive Feature Elimination (RFE) digunakan dalam mengklasifikasi data stroke. Dengan menerapkan metode tersebut, diperoleh kinerja model yang lebih baik saat melakukan klasifikasi menggunakan sejumlah fitur yang diperoleh dari hasil seleksi fitur, dibandingkan menggunakan keseluruhan fitur dalam data stroke. Selain itu, pada penerapan metode tersebut, diperoleh kinerja model yang baik dalam mengklasifikasi data kelas stroke iskemik, akan tetapi tidak cukup baik dalam mengklasifikasi data kelas stroke hemoragik. Hal ini dikarenakan proporsi jumlah data pada kelas stroke iskemik lebih banyak dibandingkan stroke hemoragik. Dalam hal ini dibutuhkan suatu metode penanganan agar kinerja model tetap optimal dalam mengklasifikasi data kelas stroke iskemik dan stroke hemoragik. Pada penelitian ini, Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan kedua kelas data stroke guna memperoleh kinerja model yang optimal dalam mengklasifikasi kedua kelas data stroke. Berdasarkan penerapan metode RF dengan RFE serta SMOTE dalam mengklasifikasi data stroke, diperoleh kinerja model yang lebih baik dibandingkan melakukan klasifikasi pada data stroke yang tidak diseimbangkan dengan SMOTE.
Stroke is one of the diseases with the high risk of death and disability. Stroke generally can be classified into two types, namely ischemic stroke and hemorrhagic stroke. A quick and accurate stroke classification is needed to find the right treatment to prevent a dangerous effect on the stroke patients. In this study, the stroke classification was applied using a machine learning approach. The data used in this study is stroke data that consists of laboratory examinations. The data consists of various laboratory examination components, therefore, it might be possible that some of the components are less relevant and has less informative related in classifying stroke. If the data is not well handled, it might affect the performance and computation time of the model in classifying stroke. Therefore, in this study, Random Forest (RF) with Recursive Feature Elimination (RFE) method is used to classify the stroke data. The result showed that by applying the method in classifying several amounts of features obtained from the feature selection results has better performance rather than classifying the method using all features in stroke data. Moreover, based on applying this method, the result showed that the model has better performance in classifying ischemic stoke class data but not good enough in classifying hemorrhagic stroke class data. This result might occur because the proportion of numbers the ischemic stroke more than hemorrhagic stroke class data. Therefore, the handling method is needed to obtain optimal model performance in classifying ischemic stroke and hemorrhagic stroke class data. In this study, Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the two classes of stroke data so optimal performance of the classification model can be obtained. Based on the application of the RF with RFE methods and SMOTE in the classification of stroke data, better model performance is obtained compared to classifying the stroke data that is not balanced with SMOTE.
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