Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning.
What Is Known and Objectives: The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE. Methods: The Emax model was selected fo...
| Publicado en: | Journal of Clinical Pharmacy & Therapeutics Vol. 47; no. 11; pp. 1845 - 1851 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160116961&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160116961 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02694727 EV4 jtl: Journal of Clinical Pharmacy & Therapeutics issn: 02694727 maglogo: Y pubinfo: dt: Nov2022 vid: 47 iid: 11 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 160116961 159225959 160116961 160116961 10.1111/jcpt.13778 160116961 ppf: 1845 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Predicting the effect of sirolimus on disease activity in patients with systemic lupus erythematosus using machine learning. aug: au: Wang, Dong‐Dong Li, Ya‐Feng Zhang, Cun He, Su‐Mei Chen, Xiao affil: Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou Jiangsu,, China sug: subj: Sirolimus Therapeutic Use Lupus Erythematosus, Systemic Drug Therapy Machine Learning Sirolimus Administration and Dosage Disease Attributes Human Descriptive Statistics Simulations Risk Assessment ab: What Is Known and Objectives: The present study aimed to predict the effect of sirolimus on disease activity in patients with systemic lupus erythematosus (SLE) using machine learning and to recommend appropriate sirolimus dosage regimen for patients with SLE. Methods: The Emax model was selected for machine learning, where the evaluation indicator was the change rate of systemic lupus erythematosus disease activity index from baseline value. Results: A total 103 patients with SLE were included for modelling, where the Emax, ET50 were −53.9%, 1.53 months in the final model respectively, and the evaluation of the final model was good. Further simulation found that the follow‐up time to achieve 25%, 50%, 75% and 80% (plateau) Emax of sirolimus effecting on disease activity in patients with SLE were 0.51, 1.53, 4.59 and 6.12 months, respectively. In addition, the sirolimus dosage was flexible and adjusted according to drug concentration, where the intersection of sirolimus concentration range included in this study was about 8–10 ng/ml. What Is New and Conclusions: This study was the first time to predict the effect of sirolimus on disease activity in patients with SLE and in order to achieve better therapeutic effect maintaining a concentration of 8–10 ng/ml sirolimus for at least 6.12 months was necessary. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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