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1.
Int J Clin Pharmacol Ther ; 60(7): 299-305, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35536101

RESUMO

OBJECTIVE: To evaluate the possibility of population-based dose optimization with the aid of MwPharm modeling and to find the best model in the Windows version (WIN). MATERIALS: 25 patients repeatedly examined for vancomycin (mean age 63 ± 14 years, body weight 88 ± 21 kg, median dose 1 g/12 h). METHODS: Trough concentrations predicted by WIN models "vancomycin_adult_k_C2", "#vancomycin_adult_C2", "vancomycin_adult_C2", and "vancomycin adult" DOS model (DOS) were compared with the measured value. STATISTICS: The percentage prediction error (%PE) calculated as (predicted - measured)/measured values, Blandt-Altman plot, root mean square error (RMSE), Pearson's coefficient of rank correlation (R). Data is presented as mean ± SD. Student's t-test was used for prediction precision evaluation. RESULTS: The %PE varied from +44.4 ± 65.2% to +76.5 ± 84.3%, p < 0.001. "#vancomycin_adult_C2" model produced the lowest %PE among WIN models as well as the lowest RMSE (79) and Blandt-Altman bias (-4.01 ± 7.59), but the Pearson's correlation (0.6843, p = 0.0002) was less tight. DOS model produced the second lowest RMSE (81), %PE (+45.9 ± 66.6%), and Blandt-Altman bias (-4.83 ± 6.97) and highest Pearson's R (0.7847, p < 0.0001). "vancomycin_adult_C2" produced the third best prediction: RMSE (113), %PE (62.8 ± 92.6%), Blandt-Altman bias (-6.78 ± 11.2) but Pearson's R was the poorest (0.5773, p = 0.0025). CONCLUSION: The lowest %PE and highest Pearson's R were achieved by the "#vancomycin_adult_C2" model. Due to the poor predictive performance of all MwPharm versions and models, we find all of them unsuitable for a priori vancomycin dosing management. Other software should be evaluated for routine use.


Assuntos
Antibacterianos , Vancomicina , Idoso , Humanos , Pessoa de Meia-Idade , Software
2.
Comput Methods Programs Biomed ; 214: 106552, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-34896687

RESUMO

BACKGROUND AND OBJECTIVES: For a long time, the Mw\Pharm software suite (MEDI\WARE, Prague, Czech Republic/ Groningen, Netherlands) has been used for PK/PD modelling in therapeutic drug monitoring (TDM). The aim of this study was to find the best model in the newer Windows Mw\Pharm++ 1.3.5.558 version (WIN). METHODS: 25 patients were repeatedly examined for vancomycin (mean age 63±14 years, body weight 88±21 kg, median dose 1 g/12 h). Trough concentrations predicted a-posteriori by WIN models "vancomycin_adult_k_C2", "#vancomycin_adult_C2", "vancomycin_adult_C2" were compared with the measured value and "vancomycin adult" DOS 3.30 model (DOS). STATISTICS: Percentage prediction error (%PE) calculated as (predicted-measured)/measured values, or WIN-DOS/DOS - data presented as mean±SD, RMSE, Blandt-Altman plot - data presented as bias±SD (95% limits of agreement), Pearson's coefficient of rank correlation (R), Student's t-test. Statistical analysis was performed using GraphPad Prism version 5.00 for Windows. RESULTS: The mean%PE in vancomycin predicted values varied from -4.5% ± 33.6 to -8.2% ± 39.3. The%PE between WIN and DOS models varied from -0.2% ± 24.5% to 4.4 ± 21.4%. Model "vancomycin_adult_C2" was closest both to measured vancomycin trough concentration and DOS model:%PE -4.5 ± 33.6% vs +4.2 ± 20.3%, RMSE 33.7 vs 20.6, Blandt-Altman bias +2.19 ± 6.17 (-9.9 - 14.3) vs -0.29 ± 3.25 (-6.7 - 6.1), resp. "#vancomycin_adult_C2" model produced largest%PE (-8.2%), RMSE (40.0) as well as Blandt-Altman bias +2.82 ± 6.76 (-10.4 - 16.1). The Pearson's R of predicted and measured vancomycin concentration, and of values predicted by WIN and DOS models, varied from 0.5135 to 0.5854, P<0.0001 and from 0.7869 to 0.8462, P<0.0001, resp. CONCLUSIONS: Three Windows vancomycin models and one DOS model in the Mw\Pharm software were compared. The best outcomes, i.e. lowest%PE, RMSE and highest Pearson's R, were reached with "vancomycin_adult_C2" model.


Assuntos
Antibacterianos , Vancomicina , Adulto , Idoso , Viés , Monitoramento de Medicamentos , Humanos , Pessoa de Meia-Idade , Software
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