DEVELOPMENT OF EARLY DIAGNOSIS ALGORITHM USING MATHEMATICAL RATIO MODELS WITH BIOCHEMICAL PARAMETERS IN SMA TYPE I DISEASE
Objective: Early diagnosis is life-saving in spinal muscular atrophy (SMA), a disease that is defined as a rare disease in the community and affects one person in ten thousand. When laboratory tests are examined from the diagnostic tests, it should be checked whether there is a homozygous deletion of the survival motor neuron 1 (SMN1) gene as the first diagnostic parameter for a patient who is thought to have spinal muscular atrophy (SMA). In order t o be sure of the diagnosis, it is necessary to look at the creatine kinase (CK) value of the patient and the nerve conduction results that will be obtained from the results of electrophysiological tests such as electromyography (EMG). It was aimed to develop an early diagnosis algorithm by using a mathematical ratio model to diagnose spinal muscular atrophy disease early, by proportioning the length of the electrocardiogram (ECG) frequency with the creatine kinase value being higher than normal.
Material and Method: A group of people with SMA disease and a group of healthy people were used in the study. Serum creatine kinase values of all patients were measured and the length of the electrocardiogram (ECG) frequency was measured. These two values are proportioned.
Result and Discussion: As a result of literature studies, the frequencies of electrocardiogram tremors and creatine kinase (CK) values of healthy people were compared. Electrocardiogram (ECG) frequencies and creatine kinase values of sick people were also compared. The range value of healthy people and the range values of sick people were compared and a range of values specific to sick people was determined. Based on this range, patients with vibration frequency / creatine kinase values between 0.125-0.175 can be diagnosed. This study will contribute to the development of computer-based android applications for diagnosis for patients with spinal muscular atrophy (SMA) with codes 0 and 1, using the inference that the patient is healthy as the value approaches 1 and the value approaches 0.
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