Changes in the use of broad-spectrum antibiotics after cefepime shortage: a time series analysis

The original cefepime product was withdrawn from the Swiss market in January 2007 and replaced by a generic 10 months later. The goals of the study were to assess the impact of this cefepime shortage on the use and costs of alternative broad-spectrum antibiotics, on antibiotic policy, and on resistance of Pseudomonas aeruginosa toward carbapenems, ceftazidime, and piperacillin-tazobactam. A generalized regression-based interrupted time series model assessed how much the shortage changed the monthly use and costs of cefepime and of selected alternative broad-spectrum antibiotics (ceftazidime, imipenem-cilastatin, meropenem, piperacillin-tazobactam) in 15 Swiss acute care hospitals from January 2005 to December 2008. Resistance of P. aeruginosa was compared before and after the cefepime shortage. There was a statistically significant increase in the consumption of piperacillin-tazobactam in hospitals with definitive interruption of cefepime supply and of meropenem in hospitals with transient interruption of cefepime supply. Consumption of each alternative antibiotic tended to increase during the cefepime shortage and to decrease when the cefepime generic was released. These shifts were associated with significantly higher overall costs. There was no significant change in hospitals with uninterrupted cefepime supply. The alternative antibiotics for which an increase in consumption showed the strongest association with a progression of resistance were the carbapenems. The use of alternative antibiotics after cefepime withdrawal was associated with a significant increase in piperacillin-tazobactam and meropenem use and in overall costs and with a decrease in susceptibility of P. aeruginosa in hospitals. This warrants caution with regard to shortages and withdrawals of antibiotics.

Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

Heuristic decomposition and LP-based scheduling in make-and-pack production

In this paper, we are concerned about the short-term scheduling of industrial make-and-pack production processes. The planning problem consists in minimizing the production makespan while meeting given end-product demands. Sequence-dependent changeover times, multi-purpose storage units with finite capacities, quarantine times, batch splitting, partial equipment connectivity, material transfer times, and a large number of operations contribute to the complexity of the problem. Known MILP formulations cover all technological constraints of such production processes, but only small problem instances can be solved in reasonable CPU times. In this paper, we develop a heuristic in order to tackle large instances. Under this heuristic, groups of batches are scheduled iteratively using a novel MILP formulation; the assignment of the batches to the groups and the scheduling sequence of the groups are determined using a priority rule. We demonstrate the applicability by means of a real-world production process.

The Blinder-Oaxaca decomposition for linear regression models

The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

Discretionary medical reporting of potentially unfit drivers: a questionnaire-based survey in Southeast Switzerland

In Switzerland, every physician has the right to report a patient that is potentially unfit to drive to the licensing authority without violating medical confidentiality. Verified information regarding physicians' attitudes concerning this discretionary reporting and the frequency of such reports are not available. In order to answer these questions, 635 resident physicians were sent a questionnaire. The response rate was 52%. On average, the responding physicians--for all specialties--reported 0.31 patients (SD 0.64, 95% CI 0.24-0.38) in the year before the survey and 1.00 patient (SD 1.74, 95% CI 0.81-1.20) in the past 5 years. Seventy-nine percent of the responding physicians indicated knowing the current legal requirements for driving in Switzerland. In applied logistic regression analysis, only two factors correlate significantly with reporting: male sex (odds ratio 5.4) and the specialty "general medicine" (odds ratio 3.4). Ninety-seven percent of the physicians were against abolishing medical discretionary reporting and 29% were in favor of introducing mandatory reporting. The great majority of the questioned physicians supported the discretionary reporting of drivers that are potentially unfit to drive as currently practiced in Switzerland. The importance and the necessity of a regular traffic medicine-related continuing education for medical professionals are shown by the low number of reports per physician.