Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction.

Quick, efficient, fully automated open-source programs to segment muscle and adipose tissues from computed tomography (CT) images would be a great contribution to body composition research. This study examined the concordance of cross-sectional areas (CSA) and densities for muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) from CT images at the third lumbar (L3) between an automated neural network (test method) and a semi-automatic human-based program (reference method). Concordance was further evaluated by disease status, sex, race/ethnicity, BMI categories. Agreement statistics applied included Lin's Concordance (CCC), Spearman correlation coefficient (SCC), Sorensen dice-similarity coefficient (DSC), and Bland−Altman plots with limits of agreement (LOA) within 1.96 standard deviation. A total of 420 images from a diverse cohort of patients (60.35 ± 10.92 years; body mass index (BMI) of 28.77 ± 7.04 kg/m2; 55% female; 53% Black) were included in this study. About 30% of patients were healthy (i.e., received a CT scan for acute illness or pre-surgical donor work-up), while another 30% had a diagnosis of colorectal cancer. The CCC, SCC, and DSC estimates for muscle, VAT, SAT were all greater than 0.80 (>0.80 indicates good performance). Agreement analysis by diagnosis showed good performance for the test method except for critical illness (DSC 0.65−0.87). Bland−Altman plots revealed narrow LOA suggestive of good agreement despite minimal proportional bias around the zero-bias line for muscle, SAT, and IMAT CSA. The test method shows good performance and almost perfect concordance for L3 muscle, VAT, SAT, and IMAT per DSC estimates, and Bland−Altman plots even after stratification by sex, race/ethnicity, and BMI categories. Care must be taken to assess the density of the CT images from critically ill patients before applying the automated neural network (test method).

Validation of skeletal muscle and adipose tissue measurements using a fully automated body composition analysis neural network versus a semi-automatic reference program with human correction in patients with lung cancer.

Rationale and objectives: To validate skeletal muscle and adipose tissues cross sectional area (CSA) and densities between a fully automated neural network (test program) and a semi-automated program requiring human correction (reference program) for lumbar 1 (L1) and lumbar 2 (L2) CT scans in patients with lung cancer. Materials and methods: Agreement between the reference and test programs was measured using Dice-similarity coefficient (DSC) and Bland-Altman plots with limits of agreement within 1.96 standard deviation. Results: A total of 49 L1 and 47 L2 images were analyzed from patients with lung cancer (mean age = 70.51 ± 9.48 years; mean BMI = 27.45 ± 6.06 kg/m2; 71% female, 55% self-identified as Black and 96% as non-Hispanic ethnicity). The DSC indicates excellent overlap (>0.944) or agreement between the two measurement methods for muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) CSA and all tissue densities at L1 and L2. The DSC was lowest for intermuscular adipose tissue (IMAT) CSA at L1 (0.889) and L2 (0.919). Conclusion: The use of a fully automated neural network to analyze body composition at L1 and L2 in patients with lung cancer is valid for measuring skeletal muscle and adipose tissue CSA and densities when compared to a reference program. Further validation in a more diverse sample and in different disease and health states is warranted to increase the generalizability of the test program at L1 and L2.

Analgesia Use in Children with Acute Long Bone Fractures in the Pediatric Emergency Department.

BACKGROUND: Practice variation exists in pain management of children with long bone fractures (LBFs). OBJECTIVE: The objectives of this study were to describe current pain management in children with LBFs and the factors associated with the undertreatment of pain. METHODS: We retrospectively studied children (aged 0-18 years) with a diagnosis of LBF in a pediatric emergency department (PED) from November 2015 through August 2016. Demographic characteristics and quality measures were noted. We determined the impact of PED crowding using the National Emergency Department Overcrowding Scale. RESULTS: A total of 905 patients (63% male, 48% African American) were enrolled. Median age was 6 years (interquartile range [IQR] 7 years), 72% had upper extremity injuries, falls were the most common mechanism (74%), and the majority were discharged (77%). Median time to pain score was 6 min (IQR 14 min). Seventy-two percent received analgesia with a median time to order of 63 min and medication receipt of 87 min. Ibuprofen was the analgesia prescribed most commonly. There were no identified factors associated with oligoanalgesia. Nonuse of narcotics was associated with African-American race, public insurance, single fractures, and arrival via private vehicle. Ambulance arrivals, lower extremity fractures, and disaster mode were associated with receiving analgesia within 60 min. CONCLUSIONS: In our study, 28% of children with LBFs did not receive pain medications, especially during normal PED volumes. Additional studies are required to explore triage as a venue for analgesia delivery for LBFs.