Development of a Human Immunodeficiency Virus Risk Prediction Model Using Electronic Health Record Data From an Academic Health System in the Southern United States.

BACKGROUND: Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) is underutilized in the southern United States. Rapid identification of individuals vulnerable to diagnosis of HIV using electronic health record (EHR)-based tools may augment PrEP uptake in the region. METHODS: Using machine learning, we developed EHR-based models to predict incident HIV diagnosis as a surrogate for PrEP candidacy. We included patients from a southern medical system with encounters between October 2014 and August 2016, training the model to predict incident HIV diagnosis between September 2016 and August 2018. We obtained 74 EHR variables as potential predictors. We compared Extreme Gradient Boosting (XGBoost) versus least absolute shrinkage selection operator (LASSO) logistic regression models, and assessed performance, overall and among women, using area under the receiver operating characteristic curve (AUROC) and area under precision recall curve (AUPRC). RESULTS: Of 998 787 eligible patients, 162 had an incident HIV diagnosis, of whom 49 were women. The XGBoost model outperformed the LASSO model for the total cohort, achieving an AUROC of 0.89 and AUPRC of 0.01. The female-only cohort XGBoost model resulted in an AUROC of 0.78 and AUPRC of 0.00025. The most predictive variables for the overall cohort were race, sex, and male partner. The strongest positive predictors for the female-only cohort were history of pelvic inflammatory disease, drug use, and tobacco use. CONCLUSIONS: Our machine-learning models were able to effectively predict incident HIV diagnoses including among women. This study establishes feasibility of using these models to identify persons most suitable for PrEP in the South.

The Role of Cone-Beam CT in Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

PURPOSE: To review available evidence for use of cone-beam CT during transcatheter arterial chemoembolization in hepatocellular carcinoma (HCC) for detection of tumor and feeding arteries. MATERIALS AND METHODS: Literature searches were conducted from inception to May 15, 2016, in PubMed (MEDLINE), Scopus, and Cochrane Central Register of Controlled Trials. Searches included "cone beam," "CBCT," "C-arm," "CACT," "cone-beam CT," "volumetric CT," "volume computed tomography," "volume CT," AND "liver," "hepatic*," "hepatoc*." Studies that involved adults with HCC specifically and treated with transcatheter arterial chemoembolization that used cone-beam CT were included. RESULTS: Inclusion criteria were met by 18 studies. Pooled sensitivity of cone-beam CT for detecting tumor was 90% (95% confidence interval [CI], 82%-95%), whereas pooled sensitivity of digital subtraction angiography (DSA) for tumor detection was 67% (95% CI, 51%-80%). Pooled sensitivity of cone-beam CT for detecting tumor feeding arteries was 93% (95% CI, 91%-95%), whereas pooled sensitivity of DSA was 55% (95% CI, 36%-74%). CONCLUSIONS: Cone-beam CT can significantly increase detection of tumors and tumor feeding arteries during transcatheter arterial chemoembolization. Cone-beam CT should be considered as an adjunct tool to DSA during transcatheter arterial chemoembolization treatments of HCC.

Utility of CBCT and AVD for intraprocedural diagnosis and treatment of lower GI bleeding.

BACKGROUND: Intraprocedural Cone Beam CT (CBCT) is assessed to examine if use improves diagnosis and embolization rates of acute lower GI bleed (LGIB) and if automatic vessel detection (AVD) software can identify feeding vessels (FV) for embolization. METHODS: Patients with inconclusive DSA findings had CBCT and retrospective analysis with AVD software (Innova 3100, GE Company, USA). Technical success was defined as the ability to detect a lower GIB site while clinical success was defined as successful embolization without evidence of rebleeding or death within 30 days. AVD technical success was defined by the ability to identify the FV on both CTA and CBCT upon independent review by 3 blinded IRs, who also assigned a degree of certainty on a 5-point Likert scale. RESULTS: 74 patients in total were treated for lower GI bleed of which 34 had indeterminate DSA. Of those, 10 patients received DSA only, of which 1 was super selective. 24 patients with GIB on pre-procedural CTA and inconclusive DSA underwent CBCT. Use of CBCT identified 9 bleeds not seen on DSA and an additional source artery in 1 case representing a 42% change in intraprocedural management as all findings were embolized. When a bleed could not be identified on CBCT, but the FV could be identified on CTA, the same suspected FV could be selected on AVD 62% of the time with an average certainty of 4.0. CONCLUSION: CBCT is useful in the intraprocedural detection of GIB when DSA is indeterminate. Furthermore, AVD software can feasibly be utilized to accurately identify FVs for empiric treatment when intraprocedural imaging is inconclusive. LEVEL OF EVIDENCE: Level III, therapeutic study.

Increased Body Mass Index and Metabolic Syndrome Are Associated with Poor Outcomes in SARS-CoV-2-Positive Emergency Department Patients.

Background: Increased body mass index (BMI) and metabolic syndrome (MetS) have been associated with adverse outcomes in viral syndromes. We sought to examine associations of increased BMI and MetS on several clinical outcomes in patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: The registry of suspected COVID-19 in emergency care (RECOVER) is an observational study of SARS-CoV-2-tested patients (n=27,051) across 155 United States emergency departments (EDs). We used multivariable logistic regression to test for associations of several predictor variables with various clinical outcomes. Results: We found that a BMI ≥30 kg/m2 increased odds of SARS-CoV-2 test positivity (odds ratio [OR], 1.30; 95% confidence interval [CI], 1.23-1.38), while MetS reduced odds of testing positive for SARS-CoV-2 (OR, 0.76; 95% CI, 0.71-0.82). Adjusted multivariable analysis found that MetS was significantly associated with the need for admission (OR, 2.11; 95% CI, 1.89-2.37), intensive care unit (ICU) care (OR, 1.58; 95% CI, 1.40-1.78), intubation (OR, 1.46; 95% CI, 1.28-1.66), mortality (OR, 1.29; 95% CI, 1.13-1.48), and venous thromboembolism (OR, 1.51; 95% CI, 1.07-2.13) in SARS-CoV-2-positive patients. Similarly, BMI ≥40 kg/m2 was significantly associated with ICU care (OR, 1.97; 95% CI, 1.65-2.35), intubation (OR, 2.69; 95% CI, 2.22-3.26), and mortality (OR, 1.50; 95% CI, 1.22-1.84). Conclusion: In this large nationwide sample of ED patients, we report a significant association of both high BMI and composite MetS with poor outcomes in SARS-CoV-2-positive patients. Findings suggest that composite MetS profile may be a more universal predictor of adverse disease outcomes, while the impact of BMI is more heavily modulated by SARS-CoV-2 status.

Virtual 2D angiography from four-dimensional digital subtraction angiography (4D-DSA): A feasibility study.

BACKGROUND: Digital subtraction angiography (DSA) remains the gold standard for angiographic evaluation of cerebrovascular pathology, however, multiple acquisitions requiring additional time and radiation are often needed. In contrast, 3D-DSA provides volumetric information from a single injection but neglects temporal information. Four-dimensional-DSA (4D-DSA) combines temporal information of 2D-DSA with volumetric information of 3D-DSA to provide time-resolved tomographic 3D reconstructions, potentially reducing procedure time and radiation. This work evaluates the diagnostic quality of virtual single-frame 4D-DSA relative to 2D-DSA images by assessing clinicians' ability to evaluate cerebrovascular pathology. METHODS: Single-frame images of four projections from 4D-DSA and their corresponding 2D-DSA images (n = 15) were rated by two neurointerventional radiologists. Images were graded based on diagnostic quality (0 = non-diagnostic, 1 = poor, 2 = acceptable, 3 = good). Dose area product (DAP) for each case was recorded for all 2D-DSA, 4D-DSA acquisitions, and the overall procedure. RESULTS: The mean diagnostic quality of all four 4D-DSA projections from both raters was 1.75 while the mean of 2D-DSA projections was 2.8. Student's t-test revealed significant difference in diagnostic quality between 4D-DSA and 2D-DSA at all four projections (p < 0.001). On average 4D-DSA acquisitions accounted for 30% dose compared to the overall average aggregated dose per procedure. CONCLUSIONS: The difference in image quality between virtual single-frame 4D-DSA and their respective 2D-DSA images is statistically significant. Furthermore, 4D-DSA acquisitions require less radiation dose than conventional procedures with 2D-DSA acquisitions.

Brain tumors disrupt the resting-state connectome.

Brain tumor patients often experience functional deficits that extend beyond the tumor site. While resting-state functional MRI (rsfMRI) has been used to map such functional connectivity changes in brain tumor patients, the interplay between abnormal tumor vasculature and the rsfMRI signal is still not well understood. Therefore, there is an exigent need for new tools to elucidate how the blood­oxygenation-level-dependent (BOLD) rsfMRI signal is modulated in brain cancer. In this initial study, we explore the utility of a preclinical model for quantifying brain tumor-induced changes on the rsfMRI signal and resting-state brain connectivity. We demonstrate that brain tumors induce brain-wide alterations of resting-state networks that extend to the contralateral hemisphere, accompanied by global attenuation of the rsfMRI signal. Preliminary histology suggests that some of these alterations in brain connectivity may be attributable to tumor-related remodeling of the neurovasculature. Moreover, this work recapitulates clinical rsfMRI findings from brain tumor patients in terms of the effects of tumor size on the neurovascular microenvironment. Collectively, these results lay the foundation of a preclinical platform for exploring the usefulness of rsfMRI as a potential new biomarker in patients with brain cancer.

Assessment of a photon-counting detector for a dual-energy C-arm angiographic system.

PURPOSE: This article presents the implementation and assessment of photon-counting dual-energy x-ray detector technology for angiographic C-arm systems in interventional radiology. METHODS: A photon-counting detector was successfully integrated into a clinical C-arm CT system. Detector performance was assessed using image uniformity metrics in both 2D projections and 3D cone-beam computed tomography (CBCT) images. Uniform exposure fields were acquired to analyze projection images and scans of a homogeneous cylinder phantom were taken to analyze 3D reconstructions. Image uniformity was assessed over a broad range of imaging parameters. RESULTS: Detector calibration greatly improved image uniformity, reducing image variation from 8.8% to 0.5% in an ideal scenario, but image uniformity degraded when imaging parameters varied strongly from values set at calibration: the tube voltage, low-high energy threshhold, and tube current had the greatest impact. Material discrimination and dynamic angiography capabilities were successfully demonstrated in separate phantom and in vivo experiments. CONCLUSION: The uniformity results identified major factors degrading image quality. The quantitative results will guide selection of calibration points to mitigate the loss of uniformity. The unique combination of dual-energy and fluoroscopy imaging capabilities with a flat-panel photon-counting detector may enable new applications in interventional radiology.