Background: Due to the heterogeneity of critically ill patients, the pharmacokinetics of tigecycline are unclear, and the optimal dosing strategy is controversial. Methods: A single-center prospective clinical study that included critically ill patients who received tigecycline was performed. Blood samples were intensively sampled (eight samples each), and plasma drug concentrations were determined. A population pharmacokinetic (PPK) model was developed and evaluated by goodness-of-fit plots, bootstrap analysis and visual predictive checks. Monte Carlo simulation was conducted to optimize the dosage regimen. Results: Overall, 751 observations from 98 patients were included. The final PPK model was a two-compartment model incorporating covariates of creatinine clearance on clearance (CL), body weight on both central and peripheral volumes of distribution (V1 and V2), γ-glutamyl transferase and total bilirubin on intercompartment clearance (Q), and albumin on V2. The typical values of CL, Q, V1 and V2 were 3.09 L/h, 39.7 L/h, 32.1 L and 113 L, respectively. A dosage regimen of 50 mg/12 h was suitable for complicated intra-abdominal infections, but 100 mg/12 h was needed for community-acquired pneumonia, skin and skin structure infections and infections caused by less-susceptive bacteria. Conclusion: The Tigecycline PPK model was successfully developed and validated. Individualized dosing of tigecycline could be beneficial for critically ill patients.

Previous evidence suggested that sodium-glucose cotransporter 2 inhibitor (SGLT2i)-mediated urinary glucose excretion (UGE) appeared to be reduced with a decrease in glomerular filtration rate. Thus, we conducted a systematic review and meta-analysis to compare SGLT2i-mediated UGE among individuals with different levels of renal function.
We conducted systematic searches in PubMed, Medline, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrial.gov from inception to May 2021. Clinical studies of SGLT2i with reports of UGE changes in predefined different levels of renal function were included. The results were expressed as pooled effect sizes with 95% confidence interval (CI). A random-effects model was used to calculate the pooled effect sizes.
In total, eight eligible studies were included. Significant differences were observed in the post-treatment UGE level among subgroups stratified by renal function (P <0.001 for subgroup difference), which were gradually decreased along with the severity of impaired renal function. Consistently, changes in UGE before and after SGLT2i treatment were also decreased along with the severity of impaired renal function [67.52 g/day (95%CI: 55.58 to 79.47 g/day) for individuals with normal renal function, 52.41 g/day (95%CI: 38.83 to 65.99 g/day) for individuals with mild renal function impairment, 35.11 g/day (95%CI: 19.79 to 50.43 g/day) for individuals with moderate renal function impairment, and 13.53 g/day (95%CI: 7.20 to 19.86 g/day) for individuals with severe renal function impairment; P <0.001 for subgroup differences].
SGLT2i-mediated UGE was renal function dependent, which was decreased with the extent of renal function impairment.

Spot determinations of the urine creatinine concentration are widely used as a substitute for 24-h urine collections. Expressed as the amount excreted per gram of creatinine, urine concentrations in a single-voided sample are often used to estimate 24-h excretion rates of protein, sodium, potassium, calcium, magnesium, urea and uric acid. These estimates are predicated on the assumption that daily creatinine excretion equals 1 g (and that a urine creatinine concentration of 100 mg/dL reflects a 1 L 24-h urine volume). Such estimates are invalid if the serum creatinine concentration is rising or falling. In addition, because creatinine excretion is determined by muscle mass, the assumption that 24-h urine creatinine excretion equals 1 g yields a misleading estimate at the extremes of age and body size. In this review, we evaluate seven equations for the accuracy of their estimates of urine volume based on urine creatinine concentrations in actual and idealized patients. None of the equations works well in patients who are morbidly obese or in patients with markedly decreased muscle mass. In other patients, estimates based on a reformulation of the Cockroft-Gault equation are reasonably accurate. A recent study based on this relationship found a high strength of correlation between estimated and measured urine output with chronic kidney disease (CKD) studied in the African American Study of Kidney Disease (AASK) trial and for the patients studied in the CKD Optimal Management with Binders and NictomidE (COMBINE) trial. However, the equation systematically underestimated urine output in the AASK trial. Hence, an intercept was added to account for the bias in the estimated output. A more rigorous equation derived from an ambulatory Swiss population, which includes body mass index and models the non-linear accelerated decline in creatinine excretion with age, could potentially be more accurate in overweight and elderly patients. In addition to extremes of body weight and muscle mass, decreased dietary intake or reduced hepatic synthesis of creatine, a precursor of creatinine or ingestion of creatine supplements will also result in inaccurate estimates. These limitations must be appreciated to rationally use predictive equations to estimate urine volume. If the baseline urine creatinine concentration is determined in a sample of known volume, subsequent urine creatinine concentrations will reveal actual urine output as well as the change in urine output. Given the constraints of the various estimating equations, a single baseline timed collection may be a more useful strategy for monitoring urine volume than entering anthropomorphic data into a calculator.