Publication: ASSESSMENT of EFFECTIVE BLOOD CONCENTRATION READINGS from CLINICAL DATA on PATIENTS with HEART FAILURE DISEASES after DIGOXIN INTAKE: A PROJECTION BASED on the INVERSE PROBLEM ALGORITHM
Issued Date
2019-12-01
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ISSN
02195194
Other identifier(s)
2-s2.0-85076714157
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Mechanics in Medicine and Biology. Vol.19, No.8 (2019)
Suggested Citation
Ya Hui Lin, Kai Yu Hsiao, Yu Teng Chang, Samrit Kittipayak, Lung Fa Pan, Lung Kwang Pan ASSESSMENT of EFFECTIVE BLOOD CONCENTRATION READINGS from CLINICAL DATA on PATIENTS with HEART FAILURE DISEASES after DIGOXIN INTAKE: A PROJECTION BASED on the INVERSE PROBLEM ALGORITHM. Journal of Mechanics in Medicine and Biology. Vol.19, No.8 (2019). doi:10.1142/S021951941940061X Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50820
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Title
ASSESSMENT of EFFECTIVE BLOOD CONCENTRATION READINGS from CLINICAL DATA on PATIENTS with HEART FAILURE DISEASES after DIGOXIN INTAKE: A PROJECTION BASED on the INVERSE PROBLEM ALGORITHM
Abstract
© 2019 World Scientific Publishing Company. In this study, a projection of effective blood concentration (EBC) readings of digoxin is made using the inverse problem algorithm based on clinical data for patients with heart failure diseases. Seven factors, including body surface area (BSA), blood urine nitrogen (BUN), creatinine, sodium (Na), potassium (K), magnesium (Mg) ion readings, and mean arterial pressure (MAP) were compiled with nonlinear regression fit to develop a projection function having 29 terms obtained from an inverse problem algorithm via the default function run in STATISTICA. Accordingly, data collected from the clinical 168 heart failure patients were normalized to be included in same domain range (-1 to +1), and then calculated by the specific algorithm to optimize the numerical solution to evaluate EBC readings of digoxin. The evaluated first-order regression fit owned an optimal loss function (φ=2.1746) coupled with correlation coefficient r2 = 0.892 and variance of 89.20%. Furthermore, 45 patients having similar clinical syndromes were also adopted to verify the projection and implied with high agreement. The BUN factor dominated the projection and defined as the most significant coefficient in the analysis, and K ion, MAP, BSA, and Mg ion factors exhibited minor contributions to the projection. The repeated trials to lower number of factors from seven to a smaller number (namely 6, 5, 4, 3, 2, and 1) for simplifying method but resulting with unaccepted outcomes, with high loss function values and low linearity. However, the algorithm held its accuracy to handle the verified data that were out of the original bounds. The proposed algorithm demonstrated a useful analysis to handle the drug administration in pharmaceutical field.