VARIABLES IMPACTING GFR ESTIMATION METHOD FOR DRUG DOSING IN CKD: ARTIFICIAL NEURAL NETWORK PREDICTION MODEL (Record no. 11249)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200214105359.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200214b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12252 |
| Author | Aljasmi, Saba M. |
| 245 ## - TITLE STATEMENT | |
| Title | VARIABLES IMPACTING GFR ESTIMATION METHOD FOR DRUG DOSING IN CKD: ARTIFICIAL NEURAL NETWORK PREDICTION MODEL |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.11(12) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | M P |
| Name of publisher, distributor, etc. | Innovare Academic Sciences Pvt Ltd |
| Year | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 5-9p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Objective: <br/>This study aimed to measure concordance between different renal function estimates in terms of drug doses and determine the p<br/>otential <br/>significant clinical differences.<br/>Method<br/>s: <br/>Around <br/>one hundred and eighty patients (<br/>≥ 1<br/>8 y<br/>) with chronic kidney disease (CKD) were eligible for inclusion in this study. A paired<br/>-<br/>proportion cohort design was utilized using an artificial intelligence model. CKD patients refined into those who have drugs adju<br/>sted for renal function. <br/>For superiority of Cockcroft<br/>-Gault (CG) vs. modified diet in renal disease (MDRD) guided with references for concordance or discordance of the two <br/>equations and determined the dosing tiers of each drug. Validated artificial neural networks (ANN) was one outcome of interest. Variable impacts and <br/>performed reassignments were compared to evaluate the factors that affect the accuracy in estimating the kidney function for <br/>a better drug dosing.<br/>Result<br/>s: <br/>The best ANN model classified most <br/>cases to CG as the best dosing method (79 vs. 72). The probability was 85% and the top performance <br/>was slightly above 93%. Creatinine levels and CKD staging were the most important factors in determining the best dosing meth<br/>od<br/> of CG versus <br/>MDRD. Ideal and actual body weights were second (24%). Whereas drug class or the specific drug was an important third factor <br/>(14%).<br/>Conclusio<br/>n: <br/>Among many variables that affect the optimal dosing method, the top three are probably CKD staging, w<br/>eight, and the drug. The <br/>contrasting CKD stages from the different methods can be used to recognize patterns, identify and predict the best dosing tac<br/>tics in CKD patients. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4639 |
| Topical term or geographic name entry element | PHARMACEUTICS |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12253 |
| Co-Author | Khan, Amer H. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Bhopal Innovare Academic Sciences Pvt Ltd |
| International Standard Serial Number | 2656-0097 |
| Title | International journal of pharmacy and pharmaceutical science |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://innovareacademics.in/journals/index.php/ijpps/article/view/35688/21238 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Pharmacy | School of Pharmacy | Archieval Section | 14/02/2020 | 2020964 | 14/02/2020 | 14/02/2020 | Articles Abstract Database |