Citations

Abstract A 39-year-old man presented with progressive dyspnea and lower extremity edema. Doppler ultrasound demonstrated bilateral leg partially occluded venous thromboses. A V/Q scan revealed a mismatched perfusion defect involving the entire right middle and lower lobes. Subsequent CT pulmonary angiogram revealed a mass lesion occluding the right interlobar pulmonary artery. Endobronchial ultrasound–guided fine-needle aspiration of the mass was concerning for neoplasm. 18 F-FDG PET/CT demonstrated marked hypermetabolism of the mass lesion. Patient underwent transmediastinal right pneumonectomy with histopathologic diagnosis of pulmonary artery angiomatoid fibrous histiocytoma, a rare etiology mimicking large pulmonary artery embolism.

Patient portals have been widely used by patients to enable timely communications with their providers via secure messaging for various issues including transportation barriers. The large volume of portal messages offers an invaluable opportunity for studying transportation barriers reported by patients. In this work, we explored the feasibility of cutting-edge deep learning techniques for identifying transportation issues mentioned in patient portal messages with deep semantic embeddings. The successful creation of annotated corpus and identification of 7 transportation issues showed the feasibility of this strategy. The developed annotated corpus could aid in developing an artificial intelligence tool to automatically identify transportation issues from millions of patient portal messages. The identified specific transportation issues and the analysis of patient demographics could shed light on how to reduce transportation gaps for patients.

1512 Background: In response to the COVID-19 pandemic, many cancer practices adopted telehealth, including telephone and video appointments. Following a period of initial expansion that began in March 2020, sustained telehealth integration has emerged across the Mayo Clinic Cancer Practice (MCCP) in 2021. The primary objective of this study was to identify factors associated with utilization of telehealth appointments. Methods: A cross-sectional, multi-site, retrospective analysis was conducted across MCCP – a multisite, multiregional cancer practice with tertiary referral campuses in Minnesota, Florida, and Arizona, as well as rural, community-based hospitals and clinics throughout the Upper Midwest. Multivariable models were used to examine the association of patient- and provider-level variables with telehealth utilization. Results: Outpatient appointments conducted in July – August 2019 (n = 32,932) were compared with those from 2020 (n = 33,662) and 2021 (n = 35,486). The rate of telehealth appointment utilization increased from <0.01% in 2019 to 11.0% in 2020 and 14.0% in 2021. The strongest provider-level predictor of telehealth utilization was female physician provider type (OR 1.06, 95% CI 1.01 to 1.11; P = 0.0297), a trend consistently observed across career stages, practice locations and settings in 2020 and 2021. Additionally, while the rate of telehealth utilization was not significantly different at referral and community-based campuses in 2020, providers at referral campuses were significantly more likely to utilize telehealth than community-based campuses in 2021 (OR 1.1, 95% CI 1.01 to 1.12; P = 0.0289). Regarding patient-level factors, rural residence (defined by Rural-Urban Commuting Area codes), which accounted for 44.2% of the patient population, was significantly associated with lower telehealth utilization as compared to patients with urban residences, particularly for video appointments (OR 1.04, 95% CI 1.02 to 1.07; P < 0.0001). Notably, the disparity in telehealth utilization between rural and urban populations was found to be less pronounced in 2021 as compared to 2020. Conclusions: Multivariable analysis across a multi-site, multi-regional cancer practice identified several factors associated with increased telehealth utilization. These included female physician provider type, referral-based campuses, and patients residing in urban settings. A detailed understanding of the factors that influence telehealth utilization – a method of care delivery which represents a “new normal” across many cancer practices – will be essential to enable continued equitable access to high-quality, high-impact, patient-centered cancer care.

Abstract Internal validation is the most popular evaluation strategy used for drug–target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug–drug and protein–protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

AbstractOraxol consists of an oral dosage form of the chemotherapeutic agent paclitaxel administered with a novel P‐glycoprotein inhibitor encequidar methanesulfonate monohydrate (formerly named HM30181A), which allows oral treatment of cancers that would otherwise be treated with intravenous paclitaxel. Here we describe the population pharmacokinetics (popPK) analyses for oral paclitaxel in patients with advanced/metastatic solid tumors to characterize pharmacokinetic (PK) profiles and quantify sources of PK variability. The best fit popPK model for oral paclitaxel, based on data from seven clinical studies (197 patients with advanced/metastatic solid tumors), involves a linear two‐compartment structural model containing first‐order absorption with a short lag time and first‐order elimination as well as a log additive error. In this popPK model, lower population estimates of central volume for Asian patients versus Caucasian patients did not translate into clinical meaningful differences in oral paclitaxel exposure. Age, sex, body weight or surface area, mild hepatic impairment, and mild to moderate renal impairment had no clinically meaningful effects on the systemic exposure of oral paclitaxel. Simulations were performed on clinical therapeutic dose (oral paclitaxel 205 mg/m2 once daily ×3 days per week) to predict exposure of oral paclitaxel and to support treatment benefits observed in a pivotal phase III trial.

BACKGROUND: Opioid-induced respiratory depression (OIRD) is common on the medical and surgical wards and is associated with increased morbidity and health care costs. While previous studies have investigated risk factors for OIRD, the role of race remains unclear. We aim to investigate the association between race and OIRD occurrence on the medical/surgical ward. METHODS: This is a post hoc analysis of the PRediction of Opioid-induced respiratory Depression In patients monitored by capnoGraphY (PRODIGY) trial; a prospective multinational observational blinded study of 1335 general ward patients who received parenteral opioids and underwent blinded capnography and oximetry monitoring to identify OIRD episodes. For this study, demographic and perioperative data, including race and comorbidities, were analyzed and assessed for potential associations with OIRD. Univariable χ 2 and Mann-Whitney U tests were used. Stepwise selection of all baseline and demographic characteristics was used in the multivariable logistic regression analysis. RESULTS: A total of 1253 patients had sufficient racial data (317 Asian, 158 Black, 736 White, and 42 other races) for inclusion. The incidence of OIRD was 60% in Asians (N = 190/317), 25% in Blacks (N = 40/158), 43% in Whites (N = 316/736), and 45% (N = 19/42) in other races. Baseline characteristics varied significantly: Asians were older, more opioid naïve, and had higher opioid requirements, while Blacks had higher incidences of heart failure, obesity, and smoking. Stepwise multivariable logistic regression revealed that Asians had increased risk of OIRD compared to Blacks (odds ratio [OR], 2.49; 95% confidence interval [CI], 1.54–4.04; P = .0002) and Whites (OR, 1.38; 95% CI, 1.01–1.87; P = .0432). Whites had a higher risk of OIRD compared to Blacks (OR, 1.81; 95% CI, 1.18–2.78; P = .0067). The model’s area under the curve was 0.760 (95% CI, 0.733–0.787), with a Hosmer-Lemeshow goodness-of-fit test P value of .23. CONCLUSIONS: This post hoc analysis of PRODIGY found a novel association between Asian race and increased OIRD incidence. Further study is required to elucidate its underlying mechanisms and develop targeted care pathways to reduce OIRD in susceptible populations.

OBJECTIVE Cervical spondylotic myelopathy (CSM) is a common progressive spine disorder affecting predominantly middle-aged and elderly populations. With increasing life expectancy, the incidence of CSM is expected to rise further. The outcomes of elderly patients undergoing CSM surgery and especially their quality of life (QOL) postoperatively remain undetermined. This study retrospectively reviewed patients to identify baseline differences and validated postoperative patient-reported outcome (PRO) measures in elderly patients undergoing CSM surgery. METHODS The multi-institutional, neurosurgery-specific NeuroPoint Quality Outcomes Database was queried to identify CSM patients treated surgically at the 14 highest-volume sites from January 2016 to December 2018. Patients were divided into three groups: young (< 65 years), early elderly (65–74 years), and late elderly (≥ 75 years). Demographic and PRO measures (Neck Disability Index [NDI] score, modified Japanese Orthopaedic Association [mJOA] score, EQ-5D score, EQ-5D visual analog scale [VAS] score, arm pain VAS, and neck pain VAS) were compared among the groups at baseline and 3 and 12 months postoperatively. RESULTS A total of 1151 patients were identified: 691 patients (60%) in the young, 331 patients (28.7%) in the early elderly, and 129 patients (11.2%) in the late elderly groups. At baseline, younger patients presented with worse NDI scores (p < 0.001) and lower EQ-5D VAS (p = 0.004) and EQ-5D (p < 0.001) scores compared with early and late elderly patients. No differences among age groups were found in the mJOA score. An improvement of all QOL scores was noted in all age groups. On unadjusted analysis at 3 months, younger patients had greater improvement in arm pain VAS, NDI, and EQ-5D VAS compared with early and late elderly patients. At 12 months, the same changes were seen, but on adjusted analysis, there were no differences in PROs between the age groups. CONCLUSIONS The authors’ results indicate that elderly patients undergoing CSM surgery achieved QOL outcomes that were equivalent to those of younger patients at the 12-month follow-up.