Yuni Dara Pramita, NIM011311133152 (2017) PROFIL PASIEN DENGAN ANEMIA DI DEPARTEMEN-SMF ILMU PENYAKIT DALAM RSUD DR. SOETOMO SURABAYA PADA TAHUN 2016. Skripsi thesis, Universitas Airlangga.

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Abstract

Background: Anemia is a condition when the number / capacity of red blood cells that carries oxygen were insufficient to meet physiological needs (WHO, 2011) and also the second leading cause of disability in the world and thus one of the most global and serious public health problems. Objective: This research aims to profile patients with anemia in the departement of internal disease in this RSUD Dr. Soetomo Surabaya period 1 January 2016 – 31 december 2016. Methods: Studies performed using patient medical records then assessed general symptoms, epidemiology, diagnosis, complaints, hemoglobin level, erythrocyte index and complete blood tests. The recorded data collected retrospectively, processed in the database, then analyzed descriptively. Result: Acquired 180 patient data, with the most distribution was female (55.6%). Anemia signs are commonly found at 50 to 59 years old (28.9%). Patients with anemia were mostly private workers (42.8%). Based on the history taking data during the first time admitted to the emergency ward, obtained the distribution of most symptoms experienced by the patient were body weakened (43.3%), nausea (38.3%), and shortness of breath (30%). In hemoglobin examination was found to declining with the classification of severe anemia with percentage in women 37.8% and men 27.8%. Based on the erythrocyte index, the majority of normal normochromic normositer (65.6%) were obtained. The main diagnoses were mostly Chronic Kidney Disease stage V (27.8), Diabetes Mellitus with Diabetic Nephropathy (20%), and Hepatic Cirrhosis (11.1%). Distribution of erythrocyte index on the distribution of other diseases resulted Chronic Kidney Disease stage V, Diabetes Mellitus with Diabetic Nephropathy stage V and Hepatic Cirrhosis (A, B, C) were found normocytic normochromic and hypochromic mikrositer morphological features. Conclusion: In this research is still lack of depth so further research needed with more complete data for better data accuracy.

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