Inter-radiologist agreement for CT scoring of pediatric splenic injuries and effect on an established clinical practice guideline.

BACKGROUND: The American Pediatric Surgical Association (APSA) advocates for the use of a clinical practice guideline to direct management of hemodynamically stable pediatric spleen injuries. The clinical practice guideline is based on the CT score of the spleen injury according to the American Association for the Surgery of Trauma (AAST) CT scoring system. OBJECTIVE: To determine the potential effect of radiologist agreement for CT scoring of pediatric spleen injuries on an established APSA clinical practice guideline. MATERIALS AND METHODS: We retrospectively analyzed blunt splenic injuries occurring in children from January 2007 to January 2012 at a single level 1 trauma center (n = 90). Abdominal CT exams performed at clinical presentation were reviewed by four radiologists who documented the following: (1) splenic injury grade (AAST system), (2) arterial extravasation and (3) pseudoaneurysm. Inter-rater agreement for AAST injury grade was assessed using the multi-rater Fleiss kappa and Kendall coefficient of concordance. Inter-rater agreement was assessed using weighted (AAST injury grade) or prevalence-adjusted bias-adjusted (binary measures) kappa statistics; 95% confidence intervals were calculated. We evaluated the hypothetical effect of radiologist disagreement on an established APSA clinical practice guideline. RESULTS: Inter-rater agreement was good for absolute AAST injury grade (kappa: 0.64 [0.59­0.69]) and excellent for relative AAST injury grade (Kendall w: 0.90). All radiologists agreed on the AAST grade in 52% of cases. Based on an established clinical practice guideline, radiologist disagreement could have changed the decision for intensive care management in 11% (10/90) of children, changed the length of hospital stay in 44% (40/90), and changed the time to return to normal activity in 44% (40/90). CONCLUSION: Radiologist agreement when assigning splenic AAST injury grades is less than perfect, and disagreements have the potential to change management in a substantial number of pediatric patients.

Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs.

BACKGROUND: Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED). MATERIALS AND METHODS: We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other - Infectious, Other - Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement. RESULTS: 1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result. CONCLUSION: At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.

Pancreatic manifestations of von Hippel-Lindau disease-effect of imaging on clinical management.

OBJECTIVE: To assess the impact of imaging on pancreatic lesion management in von Hippel-Lindau disease (VHL). METHODS: We reviewed sequential computed tomography (CT) and magnetic resonance examinations (1997-2008) of 33 patients with VHL who had at least 1 pancreatic lesion. RESULTS: One hundred sixty-seven imaging studies demonstrated innumerable simple pancreatic cysts and 58 complex pancreatic masses: 24 were complex cystic and 34 were solid (30 small [2 cm]). Aggregate annual growth was significant in complex cystic and solid masses (mean, 0.39 cm/y [P = 0.006] and 0.14 cm/y [P = 0.045]). Solid mass growth differed by size (small: 0.06 cm/y [range, -0.09 to 0.31 cm/y]; large: 1.28 cm/y [range, 0-1.98 cm/y]). Thirteen masses were excised. No patient developed metastases. Arterial-phase CT improved (P = 0.0003) solid mass detection, but 28% of studies still underreported the total number. CONCLUSIONS: Most pancreatic masses in VHL do not require annual surveillance. Arterial-phase CT improves mass detection, but many masses remain prospectively missed.

A parametric ribcage geometry model accounting for variations among the adult population.

The objective of this study is to develop a parametric ribcage model that can account for morphological variations among the adult population. Ribcage geometries, including 12 pair of ribs, sternum, and thoracic spine, were collected from CT scans of 101 adult subjects through image segmentation, landmark identification (1016 for each subject), symmetry adjustment, and template mesh mapping (26,180 elements for each subject). Generalized procrustes analysis (GPA), principal component analysis (PCA), and regression analysis were used to develop a parametric ribcage model, which can predict nodal locations of the template mesh according to age, sex, height, and body mass index (BMI). Two regression models, a quadratic model for estimating the ribcage size and a linear model for estimating the ribcage shape, were developed. The results showed that the ribcage size was dominated by the height (p=0.000) and age-sex-interaction (p=0.007) and the ribcage shape was significantly affected by the age (p=0.0005), sex (p=0.0002), height (p=0.0064) and BMI (p=0.0000). Along with proper assignment of cortical bone thickness, material properties and failure properties, this parametric ribcage model can directly serve as the mesh of finite element ribcage models for quantifying effects of human characteristics on thoracic injury risks.

Development and Validation of Statistical Models of Femur Geometry for Use with Parametric Finite Element Models.

Statistical models were developed that predict male and female femur geometry as functions of age, body mass index (BMI), and femur length as part of an effort to develop lower-extremity finite element models with geometries that are parametric with subject characteristics. The process for developing these models involved extracting femur geometry from clinical CT scans of 62 men and 36 women, fitting a template finite element femur mesh to the surface geometry of each patient, and then programmatically determining thickness at each nodal location. Principal component analysis was then performed on the thickness and geometry nodal coordinates, and linear regression models were developed to predict principal component scores as functions of age, BMI, and femur length. The average absolute errors in male and female external surface geometry model predictions were 4.57 and 4.23 mm, and the average absolute errors in male and female thickness model predictions were 1.67 and 1.74 mm. The average error in midshaft cortical bone areas between the predicted geometries and the patient geometries was 4.4%. The average error in cortical bone area between the predicted geometries and a validation set of cadaver femur geometries across 5 shaft locations was 2.9%.

A statistical skull geometry model for children 0-3 years old.

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index.

In this study, we developed a statistical rib cage geometry model accounting for variations by age, sex, stature and body mass index (BMI). Thorax CT scans were obtained from 89 subjects approximately evenly distributed among 8 age groups and both sexes. Threshold-based CT image segmentation was performed to extract the rib geometries, and a total of 464 landmarks on the left side of each subject׳s ribcage were collected to describe the size and shape of the rib cage as well as the cross-sectional geometry of each rib. Principal component analysis and multivariate regression analysis were conducted to predict rib cage geometry as a function of age, sex, stature, and BMI, all of which showed strong effects on rib cage geometry. Except for BMI, all parameters also showed significant effects on rib cross-sectional area using a linear mixed model. This statistical rib cage geometry model can serve as a geometric basis for developing a parametric human thorax finite element model for quantifying effects from different human attributes on thoracic injury risks.

Effects of BMI on the risk and frequency of AIS 3+ injuries in motor-vehicle crashes.

OBJECTIVE: Determine the effects of BMI on the risk of serious-to-fatal injury (Abbreviated Injury Scale ≥ 3 or AIS 3+) to different body regions for adults in frontal, nearside, farside, and rollover crashes. DESIGN AND METHODS: Multivariate logistic regression analysis was applied to a probability sample of adult occupants involved in crashes generated by combining the National Automotive Sampling System (NASS-CDS) with a pseudoweighted version of the Crash Injury Research and Engineering Network database. Logistic regression models were applied to weighted data to estimate the change in the number of occupants with AIS 3+ injuries if no occupants were obese. RESULTS: Increasing BMI increased risk of lower-extremity injury in frontal crashes, decreased risk of lower-extremity injury in nearside impacts, increased risk of upper-extremity injury in frontal and nearside crashes, and increased risk of spine injury in frontal crashes. Several of these findings were affected by interactions with gender and vehicle type. If no occupants in frontal crashes were obese, 7% fewer occupants would sustain AIS 3+ upper-extremity injuries, 8% fewer occupants would sustain AIS 3+ lower-extremity injuries, and 28% fewer occupants would sustain AIS 3+ spine injuries. CONCLUSIONS: Results of this study have implications on the design and evaluation of vehicle safety systems.

Development, validation, and application of a parametric pediatric head finite element model for impact simulations.

In this study, a statistical model of cranium geometry for 0- to 3-month-old children was developed by analyzing 11 CT scans using a combination of principal component analysis and multivariate regression analysis. Radial basis function was used to morph the geometry of a baseline child head finite element (FE) model into models with geometries representing a newborn, a 1.5-month-old, and a 3-month-old infant head. These three FE models were used in a parametric study of near-vertex impact conditions to quantify the sensitivity of different material parameters. Finally, model validation was conducted against peak head accelerations in cadaver tests under different impact conditions, and optimization techniques were used to determine the material properties. The results showed that the statistical model of cranium geometry produced realistic cranium size and shape, suture size, and skull/suture thickness, for 0- to 3-month-old children. The three pediatric head models generated by morphing had mesh quality comparable to the baseline model. The elastic modulus of skull had a greater effect on most head impact response measurements than other parameters. Head geometry was a significant factor affecting the maximal principal stress of the skull (p = 0.002) and maximal principal strain of the suture (p = 0.021) after controlling for the skull material. Compared with the newborn head, the 3-month-old head model produced 6.5% higher peak head acceleration, 64.8% higher maximal principal stress, and 66.3% higher strain in the suture. However, in the skull, the 3-month-old model produced 25.7% lower maximal principal stress and 11.5% lower strain than the newborn head. Material properties of the brain had little effects on head acceleration and strain/stress within the skull and suture. Elastic moduli of the skull, suture, dura, and scalp determined using optimization techniques were within reported literature ranges and produced impact response that closely matched those measured in previous cadaver tests. The method developed in this study made it possible to investigate the age effects from geometry changes on pediatric head impact responses. The parametric study demonstrated that it is important to consider the material properties and geometric variations together when estimating pediatric head responses and predicting head injury risks.

Effects of osteoporosis on AIS 3+ injury risk in motor-vehicle crashes.

Older occupants in motor-vehicle crashes are more likely to experience injury than younger occupants. One possible reason for this is that increasing age is associated with increased prevalence of osteoporosis, which decreases bone strength. Crash-injury data were used with Bayes' Theorem to estimate the conditional probability of AIS 3+ skeletal injury given that an occupant is osteoporotic for the injury to the head, spine, thorax, lower extremities, and upper extremities. This requires the conditional probabilities of osteoporosis given AIS 3+ injury for each of the body regions, which were determined from analysis of the Crash Injury Research and Engineering Network database. It also requires information on probability of osteoporosis in the crash-involved population and the probabilities of AIS 3+ skeletal injury to different body regions in crashes. The latter probabilities were obtained from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) database. The former was obtained by modeling the probability of osteoporosis in the US populations using data from the 2006 National Health Examination Nutrition Survey and applying this model to the estimate of the crash-involved population in NASS-CDS. To attempt to account for the effects of age on injury outcome that are independent of osteoporosis, only data from occupants who were 60 years of age or older were used in all analyses. Results indicate that the only body region that experiences a statistically significant change in fracture injury risk with osteoporosis is the spine, for which osteoporosis increases the risk of AIS 3+ fracture by 3.28 times, or from 0.41% to 1.34% (p<0.0001). This finding suggests that the increase in AIS 3+ injury risk with age for non-spine injuries is likely influenced by factors other than osteoporosis.