A high-volume trauma intensive care unit can be successfully staffed by advanced practitioners at night.

PURPOSE: It remains unknown whether critically ill trauma patients can be successfully managed by advanced practitioners (APs). The purpose of this study was to examine the impact of night coverage by APs in a high-volume trauma intensive care unit (ICU) on patient outcomes and care processes. MATERIALS AND METHODS: During the study period, our ICU was staffed by APs during the night shift (7 pm-7 am) from Sunday to Wednesday and by resident physicians (RPs) from Thursday to Saturday. On-call trauma fellows and attending surgeons in house supervised both APs and RPs. Patient outcomes and care processes by APs was compared with those admitted by RPs. RESULTS: A total of 289 patients were identified between July 2013 and February 2014. Median lactate clearance rate within 24 hours of admission was similar between study groups (10.0% vs 9.1%; P = .39). Advanced practitioners and RPs transfused patients requiring massive transfusion with a similar blood product ratio (packed red blood cell:fresh frozen plasma) (2.1:1 vs 1.7:1; P = .32). In a multiple logistic regression analysis, AP coverage was not associated with any clinical outcome differences. CONCLUSIONS: Our data suggest that, with adequate supervision, a high-volume trauma ICU can be safely staffed by APs overnight.

The contemporary management of penetrating splenic injury.

INTRODUCTION: Selective non-operative management (NOM) is standard of care for clinically stable patients with blunt splenic trauma and expectant management approaches are increasingly utilised in penetrating abdominal trauma, including in the setting of solid organ injury. Despite this evolution of clinical practice, little is known about the safety and efficacy of NOM in penetrating splenic injury. METHODS: Trauma registry and medical record review identified all consecutive patients presenting to LAC+USC Medical Center with penetrating splenic injury between January 2001 and December 2011. Associated injuries, incidence and nature of operative intervention, local and systemic complications and mortality were determined. RESULTS: During the study period, 225 patients experienced penetrating splenic trauma. The majority (187/225, 83%) underwent emergent laparotomy. Thirty-eight clinically stable patients underwent a deliberate trial of NOM and 24/38 (63%) were ultimately managed without laparotomy. Amongst patients failing NOM, 3/14 (21%) underwent splenectomy while an additional 6/14 (42%) had splenorrhaphy. Hollow viscus injury (HVI) occurred in 21% of all patients failing NOM. Forty percent of all NOM patients had diaphragmatic injury (DI). All patients undergoing delayed laparotomy for HVI or a splenic procedure presented symptomatically within 24h of the initial injury. No deaths occurred in patients undergoing NOM. CONCLUSIONS: Although the vast majority of penetrating splenic trauma requires urgent operative management, a group of patients does present without haemodynamic instability, peritonitis or radiologic evidence of hollow viscus injury. Management of these patients is complicated as over half may remain clinically stable and can avoid laparotomy, making them potential candidates for a trial of NOM. HVI is responsible for NOM failure in up to a fifth of these cases and typically presents within 24h of injury. Delayed laparotomy, within this limited time period, did not appear to increase mortality nor preclude successful splenic salvage. In clinically stable patients, diagnostic laparoscopy remains essential to evaluate and repair occult DI. As NOM for penetrating abdominal trauma becomes more common, multi-centre data is needed to more accurately define the principles of patient selection and the limitations and consequences of this approach in the setting of splenic injury.

Simultaneous analysis and quality assurance for diffusion tensor imaging.

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource.

Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption-for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and wide-variety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource-VIPER, to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months.