Multi regression analysis of some parameters using COVID 19 patients as a model
Keywords:
COVID-19, Enzymes, CBC, Multi regression analysisAbstract
Multi-regression analysis is an important analysis to identify the serial models of dependent variables and independent variables that are associated with Covid 19 patients'. Objective: The goal of the current study is to Identify number of models that may be associated with COVID 19 patients which used to calculate the dependent variables from independent variables. Methods: A group of 158 patients with confirmed SARS CoV-2 RNA testing were collected from Erbil international hospital and analyzed for biochemical and hematological profiles. Multi regression analysis was applied. Results: Biochemical parameters (Procalcitonin, Fe Iron, TIBC, Transferrin, LDH, ALT, AST, Alkaline Phosphatase, Total Bilirubin, Direct Bilirubin, Total protein, and Albumin) appeared alteration in their levels in COVID 19 patients. Regression analysis showed better prediction models for enzymes AST, and ALT, Where as the rest parameters, did not show fit prediction models. The hematological study appeared to fit the production model in most variables like MID, GRA, HGB, MCH, and RBC, whereas LYM, WBC, and MCHC don't show a fit model. Conclusions: Conclusions: Current study findings imply that in COVID 19 patients the level of ALT can be predicted using AST, ALP, and LDH, whereas AST level can be predicted using Levels of ALP, ALT, and LDH. In hematological studies as well, the level of MID, GRA, HGB, MCH, and RBC can be predicted from other independent variables.
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