Background
To better understand the mechanisms of infection with nontuberculous mycobacteria (NTM) in patients with cystic fibrosis (CF), we explore different risk factors associated with NTM positivity in a meta‐analysis.
Methods
Studies published before 31 July 2019 were selected from MEDLINE. Combined odds ratios (ORs) were calculated by pooling the ORs of each study. The weighted mean difference (WMD) was used for continuous numerical measurements. Summary data were pooled using fixed‐ or random‐effects models according to the presence of heterogeneity (P < .1 or I2 > 50%).
Results
Nineteen studies with a total of 23 418 patients, of whom 1421 (6%) were diagnosed as NTM positive, were included. Older age was significantly associated with NTM positivity (WMD = 2.12, 95% confidence interval [CI]: 1.11‐3.13; P < .01, fixed‐effects model). The OR for Staphylococcus aureus colonization was 1.66 (95% CI: 1.21‐2.26; P = .001) in 11 studies (8091 patients), the OR for Aspergillus fumigatus colonization was 3.59 (95% CI: 3.05‐4.23; P < .001) in 11 studies (20 480 patients), and the OR for Stenotrophomonas maltophilia colonization was 3.41 (95% CI: 2.66‐4.39; P < .01) in seven studies (14 935 patients). Oral corticosteroids were significantly associated with NTM positivity (OR = 1.98, 95% CI: 1.24‐3.16; P < .01, 6 studies, 1936 patients). No other factor showed a significant association.
Conclusion
Older age, S. aureus, S. maltophilia, and A. fumigatus chronic colonization, and oral corticosteroids were significantly associated with an increased risk of NTM positivity. CF patients with more severe conditions should be closely monitored for NTM.
Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC24h < 666 mg.h/L) and 31.9% of patients were overexposed (Cmin > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = −0.13 ± 5.16 vs. −1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.
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