Outcome After Surgical Stabilization of Rib Fractures Versus Nonoperative Treatment in Patients With Multiple Rib Fractures and Moderate to Severe Traumatic Brain Injury (CWIS-TBI)

BACKGROUND Outcomes after surgical stabilization of rib fractures (SSRF) have not been studied in patients with multiple rib fractures and traumatic brain injury (TBI). We hypothesized that SSRF, as compared with nonoperative management, is associated with favorable outcomes in patients with TBI. METHODS A multicenter, retrospective cohort study was performed in patients with rib fractures and TBI between January 2012 and July 2019. Patients who underwent SSRF were compared to those managed nonoperatively. The primary outcome was mechanical ventilation-free days. Secondary outcomes were intensive care unit length of stay and hospital length of stay, tracheostomy, occurrence of complications, neurologic outcome, and mortality. Patients were further stratified into moderate (GCS score, 9–12) and severe (GCS score, ≤8) TBI. RESULTS The study cohort consisted of 456 patients of which 111 (24.3%) underwent SSRF. The SSRF was performed at a median of 3 days, and SSRF-related complication rate was 3.6%. In multivariable analyses, there was no difference in mechanical ventilation-free days between the SSRF and nonoperative groups. The odds of developing pneumonia (odds ratio [OR], 0.59; 95% confidence interval [95% CI], 0.38–0.98; p = 0.043) and 30-day mortality (OR, 0.32; 95% CI, 0.11–0.91; p = 0.032) were significantly lower in the SSRF group. Patients with moderate TBI had similar outcome in both groups. In patients with severe TBI, the odds of 30-day mortality was significantly lower after SSRF (OR, 0.19; 95% CI, 0.04–0.88; p = 0.034). CONCLUSION In patients with multiple rib fractures and TBI, the mechanical ventilation-free days did not differ between the two treatment groups. In addition, SSRF was associated with a significantly lower risk of pneumonia and 30-day mortality. In patients with moderate TBI, outcome was similar. In patients with severe TBI a lower 30-day mortality was observed. There was a low SSRF-related complication risk. These data suggest a potential role for SSRF in select patients with TBI. LEVEL OF EVIDENCE Therapeutic, level IV

Framework of a machine tool configurator for energy efficiency

The energy consumption behavior of installed machine tools and manufacturing systems are nowadays well evaluated and the field of action for comprehensive improvements and optimization activities is open. Despite the possible improvement potential of installed machine tools, its implementation is limited due to constrains such as high investment costs with unknown amortisation time for the machine tool reconfiguration. The planned configuration of components tailored to the machine tool's target use is seen as a promising way to address the expected energetic performance in the configuration phase using detailed multichannel measurements and knowledge of past improvement activities. A modular configuration approach, based on multichannel measurements, in order to improve the energy efficiency of the machine tool, including all auxiliaries, is not present yet. In conjunction with a case study, this paper introduces a concept for a machine tool configurator to integrate the requirements for the energy use during the use phase of the machine tool and its components. Beyond this, the paper provides information for TCO calculations and investment decision. The integrated approach enables machine tool builders to optimize the machine tool configuration, select components in order to increase energy efficiency and achieve cost savings for the machine tool user, in line and validated with the machine tool builder's business case.ISSN:2212-827

Developing energy estimation model based on Sustainability KPI of machine tools

Since eco-efficiency of manufacturing resource has been emphasized, various sensors to measure energy consumption have been developed and machine tool builders also provide data of energy consumption of their own products. Due to the variety and complexity of machine tools, however, an enormous amount of data is generated and can lead to uncertainties in further interpretation. The data relating to energy consumption can be classified into process parameters and machine specifications. In order to estimate the energy use that a new machine tool utilizes, the relationship with various performance indicators of the machine tool and a process plan should be examined. The challenge is how to link the machine specifications and process plan in order to obtain actual energy consumption. This paper proposes an approach for deriving an energy estimation model from general key performance indicators of the sustainability of machine tools. For the detailed application, the proposed methodology is applied to the laser welding process of an automotive assembly line and the milling process of an aircraft part manufacturer. The paper describes the methodology for finding the parameters necessary for calculating energy use and to develop the energy estimation model by utilizing experimental data. (C) 2015 Published by Elsevier B.V