Improving outcomes in emergency general surgery: Construct of a collaborative quality initiative.

BACKGROUND: Emergency general surgery conditions are common, costly, and highly morbid. The proportion of excess morbidity due to variation in health systems and processes of care is poorly understood. We constructed a collaborative quality initiative for emergency general surgery to investigate the emergency general surgery care provided and guide process improvements. METHODS: We collected data at 10 hospitals from July 2019 to December 2022. Five cohorts were defined: acute appendicitis, acute gallbladder disease, small bowel obstruction, emergency laparotomy, and overall aggregate. Processes and inpatient outcomes investigated included operative versus nonoperative management, mortality, morbidity (mortality and/or complication), readmissions, and length of stay. Multivariable risk adjustment accounted for variations in demographic, comorbid, anatomic, and disease traits. RESULTS: Of the 19,956 emergency general surgery patients, 56.8% were female and 82.8% were White, and the mean (SD) age was 53.3 (20.8) years. After accounting for patient and disease factors, the adjusted aggregate mortality rate was 3.5% (95% confidence interval [CI], 3.2-3.7), morbidity rate was 27.6% (95% CI, 27.0-28.3), and the readmission rate was 15.1% (95% CI, 14.6-15.6). Operative management varied between hospitals from 70.9% to 96.9% for acute appendicitis and 19.8% to 79.4% for small bowel obstruction. Significant differences in outcomes between hospitals were observed with high- and low-outlier performers identified after risk adjustment in the overall cohort for mortality, morbidity, and readmissions. The use of a Gastrografin challenge in patients with a small bowel obstruction ranged from 10.7% to 61.4% of patients. In patients who underwent initial nonoperative management of acute cholecystitis, 51.5% had a cholecystostomy tube placed. The cholecystostomy tube placement rate ranged from 23.5% to 62.1% across hospitals. CONCLUSION: A multihospital emergency general surgery collaborative reveals high morbidity with substantial variability in processes and outcomes among hospitals. A targeted collaborative quality improvement effort can identify outliers in emergency general surgery care and may provide a mechanism to optimize outcomes. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

Petascale pipeline for precise alignment of images from serial section electron microscopy.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.