Acute bacterial skin and skin structure infection trials: the bad is the enemy of the good

Systematic Review and Meta-Analysis To Estimate Antibacterial Treatment Effect in Acute Bacterial Skin and Skin Structure Infection

A systematic literature review and meta-analysis were conducted to estimate the antibacterial treatment effect for linezolid and ceftaroline to inform on the design of acute bacterial skin and skin structure infection (ABSSSI) noninferiority trials. The primary endpoints included an early clinical treatment response (ECTR) defined as cessation of lesion spread at 48 to 72 h postrandomization and the test-of-cure (TOC) response defined as total resolution of the infection at 7 to 14 days posttreatment. The systematic review identified no placebo-controlled trials in ABSSSI, 4 placebo-controlled trials in uncomplicated skin and soft tissue infection as a proxy for placebo in ABSSSI, 12 linezolid trials in ABSSSI, 3 ceftaroline trials in ABSSSI, and 2 trials for nonantibacterial treatment. The ECTR rates at 48 to 72 h and corresponding 95% confidence intervals (CI) were 78.7% (95% CI, 61.1 to 96.3%) for linezolid, 74.0% (95% CI, 69.7 to 78.3%) for ceftaroline, and 59.0% (95% CI, 52.8 to 65.3%) for nonantibacterial treatment. The early clinical treatment effect could not be estimated, given no available placebo or proxy for placebo data for this endpoint. Clinical, methodological, and statistical heterogeneity influenced the selection of trials for the meta-analysis of the TOC treatment effect estimation. The pooled estimates of the TOC treatment response were 31.0% (95% CI, 6.2 to 55.9%) for the proxy for placebo, 88.1% (95% CI, 81.0 to 95.1%) for linezolid, and 86.1% (95% CI, 83.7 to 88.6%) for ceftaroline. The TOC clinical treatment effect estimation was 25.1% for linezolid and 27.8% for ceftaroline. The antibacterial treatment effect estimation at TOC will inform on the design and analysis of future noninferiority ABSSSI clinical trials.

The future of antibiotics

Antibiotic resistance continues to spread even as society is experiencing a market failure of new antibiotic research and development (R&D). Scientific, economic, and regulatory barriers all contribute to the antibiotic market failure. Scientific solutions to rekindle R&D include finding new screening strategies to identify novel antibiotic scaffolds and transforming the way we think about treating infections, such that the goal is to disarm the pathogen without killing it or modulate the host response to the organism without targeting the organism for destruction. Future economic strategies are likely to focus on ‘push’ incentives offered by public-private partnerships as well as increasing pricing by focusing development on areas of high unmet need. Such strategies can also help protect new antibiotics from overuse after marketing. Regulatory reform is needed to re-establish feasible and meaningful traditional antibiotic pathways, to create novel limited-use pathways that focus on highly resistant infections, and to harmonize regulatory standards across nations. We need new antibiotics with which to treat our patients. But we also need to protect those new antibiotics from misuse when they become available. If we want to break the cycle of resistance and change the current landscape, disruptive approaches that challenge long-standing dogma will be needed.

Impact of hospital variables on case mix index as a marker of disease severity

Case mix index (CMI) has become a standard indicator of hospital disease severity in the United States and internationally. However, CMI was designed to calculate hospital payments, not to track disease severity, and is highly dependent on documentation and coding accuracy. The authors evaluated whether CMI varied by characteristics affecting hospitals' disease severity (eg, trauma center or not). The authors also evaluated whether CMI was lower at public hospitals than private hospitals, given the diminished financial resources to support documentation enhancement at public hospitals. CMI data for a 14-year period from a large public database were analyzed longitudinally and cross-sectionally to define the impact of hospital variables on average CMI within and across hospital groups. Between 1996 and 2007, average CMI declined by 0.4% for public hospitals, while rising significantly for private for-profit (14%) and nonprofit (6%) hospitals. After the introduction of the Medicare Severity Diagnosis Related Group (MS-DRG) system in 2007, average CMI increased for all 3 hospital types but remained lowest in public vs. private for-profit or nonprofit hospitals (1.05 vs. 1.25 vs. 1.20; P<0.0001). By multivariate analysis, teaching hospitals, level 1 trauma centers, and larger hospitals had higher average CMI, consistent with a marker of disease severity, but only for private hospitals. Public hospitals had lower CMI across all subgroups. Although CMI had some characteristics of a disease severity marker, it was lower across all strata for public hospitals. Hence, caution is warranted when using CMI to adjust for disease severity across public vs. private hospitals.