Phenotypic and ancestry-related assortative mating in autism.

BACKGROUND: Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. METHODS: To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). RESULTS: We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5). LIMITATIONS: We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. CONCLUSIONS: This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.

Can we screen opportunistically for low BMD using CT scans of the shoulder and artificial intelligence.

OBJECTIVE: To evaluate whether the computed tomography (CT) attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD. MATERIALS AND METHODS: 194 patients aged 50 years or greater (69.2 +/- 9.1 years; 170 females) who had unenhanced shoulder CT scans and dual-energy x-ray absorptiometry (DXA) within 1 year of each other between January 1st, 2010 and December 31st, 2021 were evaluated. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs were obtained using 3D-Slicer. Support vector machines (SVM) and k-nearest neighbors (kNN) were used to predict low BMD. DeLong's test was used to compare the area under the curve (AUCs). RESULTS: A CT attenuation of 195.4 Hounsfield Units (HU) of the clavicle had a sensitivity of 0.577, specificity of 0.781 and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00 and AUC of 0.857; while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = 0.003), but not better than the kNN model (P = 0.098). CONCLUSION: The CT attenuation of the clavicle was best for predicting low BMD, however, a multivariable SVM was superior for predicting low BMD. ADVANCES IN KNOWLEDGE: SVM utilizing the CT attenuations at many sites was best for predicting low BMD.

Sleep and Activity Patterns in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD. Methods: We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data. Results: This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives. Conclusions: The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology.

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

Intravenous tranexamic acid is associated with an increased risk of pulmonary embolism following sarcoma resection.

INTRODUCTION: Tranexamic acid (TXA) is an antifibrinolytic drug that has been shown to reduce blood loss following surgery. The use of TXA during orthopedic procedures has gained widespread acceptance, with multiple clinical studies demonstrating no increase in thrombotic complications. While TXA has been shown to be safe and effective for several orthopedic procedures, its use in orthopedic sarcoma surgery is not well established. Cancer-associated thrombosis remains a significant cause of morbidity and mortality in patients with sarcoma. It is unknown if intraoperative TXA use will increase the risk of developing a postoperative thrombotic complication in this population. This study aimed to compare the risk of postoperative thrombotic complications in patients who received TXA during sarcoma resection to patients who did not receive TXA. METHODS: A retrospective review was performed of 1099 patients who underwent resection of a soft tissue or bone sarcoma at our institution between 2010 and 2021. Baseline demographics and postoperative outcomes were compared between patients who did and did not receive intraoperative TXA. We evaluated 90-day complication rates, including: deep venous thrombosis (DVT), pulmonary embolism (PE), myocardial infarction (MI), cerebrovascular accident (CVA), and mortality. RESULTS: TXA was used more commonly for bone tumors (p < 0.001), tumors located in the pelvis (p = 0.004), and larger tumors (p < 0.001). Patients who received intraoperative TXA were associated with a significant increase in developing a postoperative DVT (odds ratio [OR]: 2.22, p = 0.036) and PE (OR: 4.62, p < 0.001), but had no increase in CVA, MI, or mortality (all p > 0.05) within 90 days of surgery, following univariate analysis. Multivariable analysis confirmed that TXA was independently associated with developing a postoperative PE (OR: 10.64, 95% confidence interval: 2.23-50.86, p = 0.003). We found no association with DVT, MI, CVA, or mortality within 90 days postoperatively, following intraoperative TXA use. CONCLUSION: Our results demonstrate a higher associated risk of PE following TXA use in sarcoma surgery and caution is warranted with TXA use in this patient population.

Automated detection of causal relationships among diseases and imaging findings in textual radiology reports.

OBJECTIVE: Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports. MATERIALS AND METHODS: This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P < 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth. RESULTS: 2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold. DISCUSSION: Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports. CONCLUSION: This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.

A snapshot of artificial intelligence research 2019-2021: is it replacing or assisting physicians?

Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug Administration (FDA)-approved AI models were used to assist or replace healthcare providers. We find that most AI models published in this time period were intended to assist rather than replace healthcare providers, and that most of the published AI models performed tasks that could not be done by healthcare providers.

Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning.

PURPOSE: One or more vertebrae are sometimes excluded from dual-energy X-ray absorptiometry (DXA) analysis if the bone mineral density (BMD) T-score estimates are not consistent with the other lumbar vertebrae BMD T-score estimates. The goal of this study was to build a machine learning framework to identify which vertebrae would be excluded from DXA analysis based on the computed tomography (CT) attenuation of the vertebrae. METHODS: Retrospective review of 995 patients (69.0% female) aged 50 years or greater with CT scans of the abdomen/pelvis and DXA within 1 year of each other. Volumetric semi-automated segmentation of each vertebral body was performed using 3D-Slicer to obtain the CT attenuation of each vertebra. Radiomic features based on the CT attenuation of the lumbar vertebrae were created. The data were randomly split into training/validation (90%) and test datasets (10%). We used two multivariate machine learning models: a support vector machine (SVM) and a neural net (NN) to predict which vertebra(e) were excluded from DXA analysis. RESULTS: L1, L2, L3, and L4 were excluded from DXA in 8.7% (87/995), 9.9% (99/995), 32.3% (321/995), and 42.6% (424/995) patients, respectively. The SVM had a higher area under the curve (AUC = 0.803) than the NN (AUC = 0.589) for predicting whether L1 would be excluded from DXA analysis (P = 0.015) in the test dataset. The SVM was better than the NN for predicting whether L2 (AUC = 0.757 compared to AUC = 0.478), L3 (AUC = 0.699 compared to AUC = 0.555), or L4 (AUC = 0.751 compared to AUC = 0.639) were excluded from DXA analysis. CONCLUSIONS: Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better than the NN for identifying which lumbar vertebra should not be used for opportunistic CT screening analysis.

Opportunistic Screening for Osteoporosis Using CT Scans of the Knee: A Pilot Study.

Knee CT scans are used for planning for total knee arthroplasties in patients who are often simultaneously at risk for frailty fractures due to low bone mineral density. We retrospectively identified 200 patients (85.5% female) with concurrent CT scans of the knee and Dual energy x-ray absorptiometry (DXA). The mean CT attenuation of the distal femur, proximal tibia and fibula, and patella, were calculated using volumetric 3-dimensional segmentation using 3D Slicer. Data were split randomly into training 80% and test 20% datasets. The optimal CT attenuation threshold for the proximal fibula was obtained in the training dataset and evaluated in the test dataset. A support vector machine (SVM) with radial basis function (RBF) using C-classification was trained and tuned using 5-fold cross-validation in the training dataset and then evaluated in the test dataset. The SVM had a higher area-under-the curve (AUC) of 0.937 and better performance to detect osteoporosis/osteopenia than the CT attenuation of the fibula (AUC of 0.717) (P=0.015). Opportunistic screening for osteoporosis/osteopenia could be accomplished using CT scans of the knee.

Opportunistic Screening for Osteoporosis Using Hand Radiographs: A Preliminary Study.

Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis/osteopenia and identify patients who should get formal DXA evaluation.

Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans.

PURPOSE: To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans. METHODS: Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia. RESULTS: Patients with a mean age (range) of 67.9 (50-87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of .791, specificity of .706, and area under the curve (AUC) of .748. The AUC for the SVM with cubic kernel classifier (AUC = .912) was better than the RF classifier (AUC = .683, P < .001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC = .748, P = .025). CONCLUSIONS: Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone.

Direct anterior approach associated with lower dislocation risk after primary total hip arthroplasty in patients with prior lumbar spine fusion.

BACKGROUND: While there has been much interest in the increased dislocation rate in total hip arthroplasty (THA) patients with a lumbar spine fusion (LSF), there is minimal literature comparing the risk based on surgical approach. The purpose of this study was to determine if a direct anterior (DA) approach was protective against dislocation when compared to the anterolateral and posterior approaches in this high-risk patient population. METHODS: A retrospective review was performed of 6554 THAs performed at our institution from January 2011 to May 2021. 294 (4.5%) patients had a prior LSF and were included in the analysis. The surgical approach, timing of LSF in relation to THA, vertebral levels fused, timing of THA dislocation, and the need for revision surgery were recorded for statistical analysis. RESULTS: In total, 39.7.3% of patients underwent a DA approach (n = 117), 25.9% underwent an anterolateral approach (n = 76), and 34.3% underwent a posterior approach (n = 101). There was no difference in number of vertebral levels fused between groups (mean 2.5, all p > 0.05). There was a total of 13 (4.4%) THA dislocation events, with an average time from surgery to dislocation of 5.6 months (0.3-30.5 months). There were fewer dislocations in the DA cohort (0.9%) in comparison to both the anterolateral (6.6%, p = 0.036) and posterior groups (6.9%, p = 0.026). CONCLUSIONS: The DA approach demonstrated a significantly lower THA dislocation rate compared to both the anterolateral and posterior approaches in patients with a concomitant LSF.

Radiomics analysis in medical imaging research.

This article discusses the current research in the field of radiomics in medical imaging with emphasis on its role in fighting coronavirus disease 2019 (COVID-19). This article covers the building of radiomic models in a simple straightforward manner, while discussing radiomic models potential to help us face this pandemic.

Is Heterotopic Ossification Associated With Surgical Approach in Total Hip Arthroplasty?

BACKGROUND: Although risk factors for heterotopic ossification (HO) have been defined, the effect from surgical approach is not fully understood. The primary objective of our study was to evaluate the effect that surgical approach has on the risk for developing severe HO after total hip arthroplasty (THA) and compare this with other known risk factors. We hypothesized that there would be no difference in HO formation based on the surgical approach. METHODS: We retrospectively reviewed all patients who underwent primary THA at our hospital between March 2011 and March 2021. Patients with HO documented in the radiology reports were cross-referenced with our THA data set and manually reviewed to determine Brooker classification. Patient demographics, medical comorbidities, surgical details, and medication information were collected from the electronic medical record and compared. RESULTS: Of 3,427 patients who underwent THA, 677 (19.8%) developed HO postoperatively. A multivariable analysis confirmed that surgical approach was independently associated with increased odds for HO development. The anterolateral (odds ratio [OR], 3.43; P < 0.001) and posterior (OR, 2.24; P < 0.001) approaches had increased odds for developing HO compared with the direct anterior approach. However, only the anterolateral approach (OR, 1.85; P = 0.033) demonstrated an increased association with the development of severe HO (Brooker 3, 4) postoperatively. CONCLUSION: Although the use of the direct anterior approach had the lowest overall OR for developing HO after THA, this is likely only clinically notable when compared with the anterolateral approach. LEVEL OF EVIDENCE: III.

Statistically based nomograms for the minimal needle length required to achieve intra-articular fluoroscopic-guided injections of the shoulder, hip, and knee.

BACKGROUND: Image-guided intra-articular injections are commonly performed to reduce pain in patients with arthritis or other joint-related pathology. Utilizing a needle length that is too short could lead to increased patient discomfort, increased procedural time, and extra-articular injections. OBJECTIVE: To predict the minimum needle length required for fluoroscopic-guided intra-articular injections of the hips, knees, and shoulders based on patient age, gender, height and weight, or body mass index (BMI) and to evaluate whether this varies by gender. STUDY DESIGN: Cross-sectional study. SETTING: Tertiary care academic center. PARTICIPANTS: 600 consecutive patients with available magnetic resonance imaging (MRI) of the hips, knees, and shoulders (100 males and 100 females for each joint). METHODS: The distance from the skin to the joint (glenohumeral, hip and knee) and the thickness of the subcutaneous fat pad (distance from the skin to the muscle) along the injection path were measured. Multivariable linear ridge regression with 10-fold cross-validation was used to predict the distance from the skin to the hip, knee, and glenohumeral joints using age, gender, weight, and height or using age, gender, and BMI. RESULTS: The data show that the subcutaneous fat thickness and the distance from the skin to all joints increase with weight (p < .001) and BMI (p < .001). Subcutaneous fat pads around the anterior shoulder (p < .02) and knee (<.001) are thicker in women than in men. CONCLUSIONS: Patient habitus, in particular weight and BMI, are strong predictors of the thickness of the subcutaneous fat pads and consequently strong predictors of the distance from the skin to the joint. Subcutaneous fat pad thickness around the shoulders and knees varies by gender. Nomograms showing the minimal needle length required to achieve intra-articular injections of the hip, knee and glenohumeral joints are presented.

Racial and Ethnic Diversity Among Students, Graduates, and Faculty in Biostatistics and Epidemiology, 2010-2020.

OBJECTIVE: This study is a follow-up to a study in 2020 that reviewed changes in the racial and ethnic composition of public health students, graduates, and faculty among Association of Schools and Programs of Public Health (ASPPH)-member institutions. In the current study, we evaluated how the racial and ethnic composition among biostatistics and epidemiology students, graduates, and faculty changed from 2010 to 2020. METHODS: We analyzed data on race and ethnicity of enrolled graduate students, graduates (master's and doctoral), and faculty at ASPPH-member institutions by using institutionally reported data from the ASPPH Data Center. We tabulated frequencies, percentages, and percentage-point changes by race and ethnicity. We measured differences between groups by using a test for difference in 2 proportions. RESULTS: The number of enrolled students, graduates, and faculty in all departments increased during the study period, while the number of tenure-track faculty in biostatistics decreased. The percentage of enrolled Hispanic/Latino biostatistics graduate students increased from 5.6% in 2010 to 10.2% in 2020 (P = .007), and the percentage of epidemiology graduates increased from 8.8% to 13.8% (P = .008). We found no differences among other underrepresented racial and ethnic groups. Most biostatistics and epidemiology professors at all ranks were non-Hispanic White, despite substantial decreases. The percentage of underrepresented racial and ethnic minority biostatistics and epidemiology professors was constant across all ranks. CONCLUSION: Although more Hispanic/Latino students are enrolled in and graduating from biostatistics and epidemiology departments at ASPPH-member institutions, we found no change among faculty. More work is needed to recruit and retain other (American Indian/Alaska Native, Black or African American, Native Hawaiian/Other Pacific Islander) underrepresented students and faculty.