A Quality Improvement Initiative to Improve Feeding and Growth of Infants With Cleft Lip and/or Palate.

OBJECTIVE: The purpose of this quality improvement initiative was to improve feeding and growth outcomes in infants with cleft lip and/or palate (CL/P). DESIGN: Institute for Healthcare Improvement quality improvement model. SETTING: Large pediatric academic medical center in the Midwestern United States. PARTICIPANTS: One hundred forty-five infants with nonsyndromic CL/P ages 0 to 12 months. INTERVENTIONS: Key drivers included (1) caregiver education and resources, (2) care coordination and flow, and (3) provider education and training. Interventions were designed around these themes and included targeting improved team communication, increased social work consultations, patient tracking, staff education, improved access to feeding equipment, and the launch of a new cleft palate feeding team. MAIN OUTCOME MEASURE(S): The primary outcome measure was the percentage of new patients with CL/P who met criteria for failure to thrive (FTT) per month. The secondary outcome measure was the frequency of hospitalization for infants with CL/P with a primary reason for admission of feeding difficulties or FTT. RESULTS: The institutional FTT rate for infants with CL/P decreased from 17% to 7% ( P < .003). The frequency of hospitalization for FTT improved from once every 30 days to once every 118 days. CONCLUSIONS: Targeted interventions aimed at improving feeding efficiency and effectiveness, as well as changes in care delivery models, can reliably promote improvements in feeding and growth outcomes for infants with CL/P, even with psychosocial risk factors present.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.