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. 2021 Oct 20;77(2):356–363. doi: 10.1093/jac/dkab381

Table 2.

Multiple linear regression equations to predict aminoglycoside MICs based on the aminoglycoside-resistance genes

Aminoglycoside Interceptb Aminoglycoside-resistance genesa
aac(3)-II aac(3)-IV aac(6′)-Ib aac(6′)-Ib′ ant(2′′)-I aph(3′)-I rmtF
Amikacin βc 5.8 –d 11.1 9.0 −3.7 – 3.6 122.2
95% CI 2.3–9.3 – 0.1–22.1 5.2–12.7 −7.9–0.5 – −0.4–7.5 103.4–141.1
P <0.001 – 0.048 <0.001 0.084 – 0.076 <0.001
final equatione amikacin MIC = 5.8+[11.1×aac(3)-IV]+[9.0×aac(6′)-Ib]+[−3.7×aac(6′)-Ib′]+[3.6×aph(3′)-I]+[122.2×rmtF], aR2 = 0.56
Gentamicin β 2.1 68.1 24.9 – – 41.1 – 57.8
95% CI 0.4–4.6 58.0–78.2 7.8–41.9 – – 27.8–54.4 – 26.9–88.6
P 0.092 <0.001 0.005 – – <0.001 – <0.001
final equation gentamicin MIC = 2.1+[68.1×aac(3)-II]+[24.9×aac(3)-IV]+[41.1×ant(2′′)-I]+[57.8×rmtF], aR2 = 0.64
Plazomicin β 0.3 – – – – – – 127.7
95% CI 0.3–0.2 – – – – – – 127.2–128.2
P <0.0001 – – – – – – <0.001
final equation plazomicin MIC = 0.3+[127.7×rmtF], aR2 = 0.99
Tobramycin β 7.1 12.4 115.0 8.8 – 49.9 – 108.5
95% CI 2.9–11.4 2.5–22.2 98.4–131.7 3.7–13.8 – 36.9–62.9 – 78.3–138.8
P 0.001 0.014 <0.001 0.001 – <0.001 – <0.001
final equation tobramycin MIC = 7.1+[12.4×aac(3)-II]+[115.0×aac(3)-IV]+[8.8×aac(6′)-Ib]+[49.9×ant(2′′)-I]+[108.5×rmtF], aR2 = 0.66
a

aac(6′)-Ib-cr and aph(3′)-II were not retained in any of the multivariate models.

b

Intercept is the aminoglycoside MIC value when none of the aminoglycoside-resistance genes present in the final model is present in an isolate.

c

β is the estimated increase in aminoglycoside MIC (mg/L) caused by the presence of the aminoglycoside-resistance gene.

d

Indicates that this gene was not retained in the multivariate model.

e

Final equations predict the MIC of each aminoglycoside depending on which aminoglycoside-resistance genes are present; input ‘1’ in place of the aminoglycoside-resistance gene name if an isolate has that gene or input a ‘0’ if it does not have the gene.