Omadacycline is an aminomethylcycline antimicrobial approved by the US Food and Drug Administration in 2018 for community-acquired bacterial pneumonia and acute bacterial skin and skin structure infections. It has in vitro activity against nontuberculous mycobacteria, including Mycobacterium abscessus complex, but clinical data for this indication are lacking. Omadacycline use was reviewed at an 804-bed academic medical center. Patients were included if they received omadacycline for culture-proven M abscessus disease in 2019. Four patients received omadacycline for the treatment of culture-positive M abscessus disease in 2019. Two patients had cutaneous disease, 1 had pulmonary disease, and 1 had osteomyelitis and bacteremia. The patients received omadacycline for a median duration of 166 days (range, 104–227) along with a combination of other antimicrobial agents. Omadacycline-containing regimens were associated with a clinical cure in 3 of 4 patients, with 1 patient improving on ongoing treatment. Omadacycline’s tolerability was acceptable for patients with M abscessus disease, with 1 patient discontinuing therapy in month 6 due to nausea. Omadacycline is a novel oral option for the treatment of M abscessus disease, for which safe and effective options are needed. Although this case series is promising, further data are required to determine omadacycline’s definitive role in the treatment of M abscessus disease.

Aim: To evaluate the efficiency of Bayesian modeling software and first-order pharmacokinetic (PK) equations to calculate vancomycin area under the concentration-time curve (AUC) estimations. Methods: Unblinded, crossover, quasi-experimental study at a tertiary care hospital for patients receiving intravenous vancomycin. Vancomycin AUC monitoring was compared using Bayesian modeling software or first-order PK equations. The primary endpoint was the time taken to estimate the AUC and determine regimen adjustments. Secondary endpoints included the percentage of vancomycin concentrations usable for AUC calculations and acute kidney injury (AKI). Results: Of the 124 patients screened, 34 patients had usable vancomycin concentrations that led to 44 AUC estimations. Without electronic health record (EHR) integration, the time from assessment to intervention in the Bayesian modeling platform was a median of 9.3 min (quartiles Q₁–Q₃ 7.8–12.4) compared to 6.8 min (Q₁–Q₃ 4.8–8.0) in the PK equations group (p = 0.004). With simulated Bayesian software integration into the EHR, however, the median time was 3.8 min (Q₁–Q₃ 2.3–6.9, p = 0.019). Vancomycin concentrations were usable in 88.2% in the Bayesian group compared to 48.3% in the PK equation group and there were no cases of AKI. Conclusion: Without EHR integration, Bayesian software was more time-consuming to assess vancomycin dosing than PK equations. With simulated integration, however, Bayesian software was more time efficient. In addition, vancomycin concentrations were more likely to be usable for calculations in the Bayesian group.

Nontuberculous mycobacteria (NTM) can cause a variety of infections, including serious pulmonary disease. Treatment encompasses polypharmacy, with a targeted regimen of 2–5 active medications, depending on site of infection, species, and clinical characteristics. Medications may include oral, intravenous, and inhalational routes. Medication acquisition can be challenging for numerous reasons, including investigational status, limited distribution models, and insurance prior authorization. Additionally, monitoring and managing adverse reactions and drug interactions is a unique skill set. While NTM is primarily medically managed, clinicians may not be familiar with the intricacies of medication selection, procurement, and monitoring. This review offers insights into the pharmacotherapeutic considerations of this highly complex disease state, including regimen design, medication acquisition, safety monitoring, relevant drug–drug interactions, and adverse drug reactions.

Current vancomycin guidelines recommend early and frequent area-under-the-curve monitoring in patients with obesity. Vancomycin's volume of distribution is likely altered in patients with obesity, which may result in lower serum concentrations initially but lead to accumulation with continued use. The objective of this study was to evaluate the incidence of vancomycin accumulation in patients with obesity and identify potential factors associated with accumulation. This was a single-center, retrospective, observational study at a tertiary academic medical center. Adult patients with a body mass index (BMI) ≥ 30 kg/m² and ≥ 2 vancomycin serum trough concentrations drawn in 2019 were screened for inclusion. The major endpoint was the incidence of vancomycin accumulation defined as ≥ 20% increase in trough concentration within the first 10 days of therapy. Key minor endpoints included incidence of acute kidney injury (AKI) and factors associated with accumulation. Of the 443 patients screened, 162 were included. The median age was 56.5 years (interquartile range [IQR], 43–65.3), and 62.3% were male. The median weight was 112.7 kg (IQR, 99.8–122.6) and the median BMI was 36.8 kg/m² (IQR, 33.1–41). The total daily dose median at initiation was 28.7 mg/kg per day (IQR, 25.4–31.2). Accumulation occurred in 99 of 162 patients (61.1%) and AKI occurred in 20 of 140 patients (14.3%). No specific factors were found to be associated with accumulation. Patients with obesity are likely to experience vancomycin accumulation within the first 10 days of therapy. Clinicians should use frequent monitoring of vancomycin and use caution when interpreting early concentrations in patients with obesity.

The rapid pace of the COVID-19 pandemic precluded traditional approaches to evaluating clinical research and guidelines. We highlight notable successes and pitfalls of clinicians’ new approaches to managing evidence amidst an unprecedented crisis. In “Era 1” (early 2020), clinicians relied on anecdote and social media, which democratized conversations on guidelines, but also led clinicians astray. “Era 2” (approximately late 2020 to early 2021) saw preprints that accelerated new interventions but suffered from a surfeit of poor-quality data. In the current era, clinicians consolidate the evidentiary gains of Era 2 with living, online clinical guidelines, but the public suffers from misinformation. The COVID-19 pandemic is a laboratory on how clinicians adapt to an absence of clinical guidance amidst an informational and healthcare crisis. Challenges remain as we integrate new approaches to innovations made in the traditional guideline process to confront both the long tail of COVID-19 and future pandemics.