Electronic Theses and Dissertation

Stroke merupakan penyebab utama disabilitas dan penyebab kematian ketiga di dunia setelah penyakit jantung dan kanker. Diagnosis cepat dan tepat sangat penting untuk menentukan jenis stroke dan menentukan tata laksana yang sesuai. Di Indonesia, telah dikembangkan beberapa sistem skoring klinis untuk membantu diagnosis stroke, termasuk Algoritma Stroke Gadjah Mada dan Skala Stroke Syiah Kuala. Namun, validitas skala terbaru, yaitu Skala Stroke Syiah Kuala, masih terbatas dan perlu dibandingkan dengan algoritma yang telah lebih dahulu teruji. Penelitian ini bertujuan untuk membandingkan validitas diagnostik Skala Stroke Syiah Kuala dengan Algoritma Stroke Gadjah Mada dalam menentukan jenis stroke. Penelitian ini menggunakan desain potong lintang (cross-sectional) dengan total 62 pasien stroke di ruang rawat inap saraf RSUD dr. Zainoel Abidin Banda Aceh. Data diperoleh melalui wawancara dan status pasien. Analisis dilakukan untuk menilai sensitivitas, spesifisitas, nilai prediksi positif (PPV), nilai prediksi negatif (NPV), dan akurasi kedua sistem skoring terhadap hasil diagnosis CT-Scan sebagai standar emas. Hasil penelitian ini, Skala Stroke Syiah Kuala menunjukkan sensitivitas 76,4%, spesifisitas 57,1%, PPV 93,3%, NPV 23,5%, dan akurasi 74,2%. Sedangkan Algoritma Stroke Gadjah Mada memiliki sensitivitas 72,7%, spesifisitas 57,1%, PPV 93,0%, NPV 21,1%, dan akurasi 71,0%. Analisis uji Kappa menunjukkan tingkat kesesuaian substansial antara kedua sistem skoring. Kesimpulannya Kedua sistem skoring memiliki kemampuan diagnostik yang cukup baik dalam membedakan stroke iskemik dan hemoragik, dengan Skala Stroke Syiah Kuala menunjukkan akurasi sedikit lebih tinggi. Hasil ini menunjukkan bahwa Skala Stroke Syiah Kuala berpotensi menjadi alat bantu diagnosis yang sederhana dan aplikatif di layanan kesehatan primer, meskipun pemeriksaan penunjang seperti CT-Scan tetap diperlukan untuk memastikan diagnosis yang akurat.
Kata kunci: Stroke, Skala Stroke Syiah Kuala, Algoritma Stroke Gadjah Mada, CT-Scan, Uji Diagnostik

Stroke is the leading cause of disability and the third leading cause of death worldwide, after heart disease and cancer. Rapid and accurate diagnosis is crucial for determining the type of stroke and appropriate management. In Indonesia, several clinical scoring systems have been developed to aid in stroke diagnosis, including the Gadjah Mada Stroke Algorithm and the Syiah Kuala Stroke Scale. However, the validity of the newer scale, the Syiah Kuala Stroke Scale, is still limited and needs to be compared with the algorithm that has been validated for a longer time. This study aims to compare the diagnostic validity of the Syiah Kuala Stroke Scale with the Gadjah Mada Stroke Algorithm in determining stroke type. This study used a cross-sectional design involving a total of 62 stroke patients in the neurological inpatient ward of dr. Zainoel Abidin Hospital in Banda Aceh. Data were obtained through interviews and patient medical records. The analysis was conducted to assess the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of both scoring systems against the CT-Scan diagnosis as the gold standard. The results of this study showed that the Syiah Kuala Stroke Scale had a sensitivity of 76.4%, a specificity of 57.1%, a PPV of 93.3%, an NPV of 23.5%, and an accuracy of 74.2%. In contrast, the Gadjah Mada Stroke Algorithm had a sensitivity of 72.7%, a specificity of 57.1%, a PPV of 93.0%, an NPV of 21.1%, and an accuracy of 71.0%. The Kappa test analysis showed a substantial level of agreement between the two scoring systems. In conclusion, both scoring systems possess a reasonably good diagnostic ability in differentiating between ischemic and hemorrhagic stroke, with the Syiah Kuala Stroke Scale showing a slightly higher accuracy. This finding suggests that the Syiah Kuala Stroke Scale has the potential to be a simple and applicable diagnostic aid in primary healthcare settings, although supporting examinations like CT-Scan remain necessary to ensure an accurate diagnosis. Keywords: Stroke, Syiah Kuala Stroke Scale, Gadjah Mada Stroke Algorithm, CTScan, Diagnostic Test