Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room.

BACKGROUND: Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. METHODS: We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. RESULTS: The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). CONCLUSIONS: The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.

Local large language models for privacy-preserving accelerated review of historic echocardiogram reports.

OBJECTIVES: The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. MATERIALS AND METHODS: Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists. RESULTS: The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations. CONCLUSION: The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data.

Children and adolescents with all forms of shoulder instability demonstrate differences in their movement and muscle activity patterns when compared to age- and sex-matched controls.

BACKGROUND: Shoulder instability is a complex impairment and identifying biomarkers which differentiate subgroups is challenging. There is limited fundamental movement and muscle activity data for identifying different mechanisms for shoulder instability in children and adolescents which may inform subgrouping and treatment allocation. HYPOTHESIS: Children and adolescents with shoulder instability (irrespective of etiology) have differences in their movement and muscle activity profiles compared to age- and sex-matched controls (two-tailed). METHODS: Young people between eight to 18 years were recruited into two groups of shoulder instability (SI) or and age- and sex-matched controls (CG). All forms of SI were included and young people with co-existing neurological pathologies or deficits were excluded. Participants attended a single session and carried out four unweighted and three weighted tasks in which their movements and muscle activity was measured using 3D-movement analysis and surface electromyography. Statistical parametric mapping was used to identify between group differences. RESULTS: Data was collected for 30 young people (15 SI (6M:9F) and 15 CG (8M:7F)). The mean (SD) age for all participants was 13.6 years (3.0). The SI group demonstrated consistently more protracted and elevated sternoclavicular joint positions during all movements. Normalized muscle activity in Latissimus dorsi was lower in the SI group and had the most statistically significant differences across all movements. Where differences were identified, the SI group also had increased normalized activity of their middle trapezius, posterior deltoid and biceps muscles whilst activity of their latissimus dorsi, triceps and anterior deltoid were decreased compared to the CG group. No statistically significant differences were found for pectoralis major across any movements. Weighted tasks produced fewer differences in muscle activity patterns compared to unweighted tasks. DISCUSSION: Young people with SI may adapt their movements to minimize glenohumeral joint instability. This was demonstrated by reduced variability in acromioclavicular and sternoclavicular joint angles, adoption of different movement strategies across the same joints and increased activity of the scapular stabilizing muscles, despite achieving similar arm positions to the CG. CONCLUSION: Young people with shoulder instability demonstrated consistent differences in their muscle activity and movement patterns. Consistently observed differences at the shoulder girdle included increased sternoclavicular protraction and elevation accompanied by increased normalized activity of the posterior scapula stabilizing muscles. Existing methods of measurement may be used to inform clinical decision making, however, further work is needed evaluate the prognostic and clinical utility of derived 3D and sEMG data for informing decision making within shoulder instability.

Systematic Review and Meta-analysis of the Prevalence of Levator Ani Avulsion with Obstetric Anal Sphincter Injury and its Effects on Pelvic Floor Dysfunction.

INTRODUCTION AND HYPOTHESIS: The objective was to evaluate the prevalence of levator ani avulsion (LAA) among primiparous women with obstetric anal sphincter injury (OASI) and how this association could affect future pelvic floor dysfunction. METHODS: Three electronic databases (MEDLINE/PubMed/EMBASE) were searched in December 2018 and again in October 2022. Nine full-text articles were included in the analysis. The exclusion criteria were language other than English, studies not based on primiparous women only, conference abstracts, and evaluation without ultrasound or MRI. RESULTS: The overall prevalence of LAA was 24% (95% CI: 18-30%). Those with OASI, were at a higher risk of LAA, OR 3.49 (95% CI: 1.46 to 8.35). In women with LAA + OASI versus OASI alone, Three of Five studies showed worsened AI symptoms. Three of Five studies assessing urinary incontinence (UI) reported no significant difference in UI, whereas two reported increased UI. All studies that looked at pelvic organ prolapse reported a higher incidence of symptomatic prolapse and reduced pelvic floor muscle strength in women with LAA + OASI compared with those without LAA. CONCLUSION: Levator ani avulsion is prevalent following vaginal birth and is strongly associated with OASI. Incidence of AI does not increase in women with LAA and OASI, but they had greater symptom bother. OASI with LAA appears to increase the incidence of pelvic floor weakness and pelvic organ prolapse. There is no consensus agreement on the effect of LAA + OASI on UI.

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. METHODS: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. RESULTS: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. CONCLUSION: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.

Quantification of long-term nonlinear stress relaxation of bovine trabecular bone.

The reliability of computational models in orthopedic biomechanics depends often on the accuracy of the bone material properties. It is widely recognized that the mechanical response of trabecular bone is time-dependent, yet it is often ignored for the sake of simplicity. Previous investigations into the viscoelastic properties of trabecular bone have not explored the relationship between nonlinear stress relaxation and bone mineral density. The inclusion of this behavior could enhance the accuracy of simulations of orthopedic interventions, such as of primary fixation of implants. Although methods to quantify the viscoelastic behavior are known, the time period during which the viscoelastic properties should be investigated to obtain reliable predictions is currently unclear. Therefore, this study aimed to: 1) Investigate the duration of stress relaxation in bovine trabecular bone; 2) construct a material model that describes the nonlinear viscoelastic behavior of uniaxial stress relaxation experiments on trabecular bone; and 3) implement bone density into this model. Uniaxial compressive stress relaxation experiments were performed with cylindrical bovine femoral trabecular bone samples (n = 16) with constant strain held for 24 h. Additionally, multiple stress relaxation experiments with four ascending strain levels with a holding time of 30 min, based on the results of the 24-h experiment, were executed on 18 bovine bone cores. The bone specimens used in this study had a mean diameter of 12.80 mm and a mean height of 28.70 mm. A Schapery and a Superposition model were used to capture the nonlinear stress relaxation behavior in terms of applied strain level and bone mineral density. While most stress relaxation happened in the first 10 min (up to 53 %) after initial compression, the stress relaxation continued even after 24 h. Up to 69 % of stress relaxation was observed at 24 h. Extrapolating the results of 30 min of experimental data to 24 h provided a good fit for accuracy with much improved experimental efficiency. The Schapery and Superposition model were both capable of fitting the repeated stress relaxation in a sample-by-sample approach. However, since bone mineral density did not influence the time-dependent behavior, only the Superposition model could be used for a group-based model fit. Although the sample-by-sample approach was more accurate for an individual specimen, the group based approach is considered a useful model for general application.

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

Importance: Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective: Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design: Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting: MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants: Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures: Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures: Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results: The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance: aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.

A National Institutes of Health Approach for Advancing Research to Improve Youth Mental Health and Reduce Disparities.

Even before the COVID-19 pandemic, mental health challenges were the leading cause of disability and poor health outcomes in youth. Challenges are even greater for youth from racially and ethnically minoritized groups in the United States. Racially and ethnically minoritized youth are more vulnerable to mental health problems than White adolescents, yet are less likely to use mental health services. In late 2021, the National Institutes of Health (NIH) sponsored a virtual conference to examine the state of the science around youth mental health disparities (YMHD), focusing on youth from racially and ethnically minoritized populations and the intersection of race and ethnicity with other drivers of mental health disparities. Key findings and feedback gleaned from the conference have informed strategic planning processes related to YMHD, which has included the development of a strategic framework and funding opportunities, designed to reduce YMHD. This commentary briefly describes the collaborative approach used to develop this framework and other strategies implemented across the NIH to address YMHD and serves as an urgent call to action.

A Facile Graphene Conductive Polymer Paper Based Biosensor for Dopamine, TNF-α, and IL-6 Detection.

Paper-based biosensors are a potential paradigm of sensitivity achieved via microporous spreading/microfluidics, simplicity, and affordability. In this paper, we develop decorated paper with graphene and conductive polymer (herein referred to as graphene conductive polymer paper-based sensor or GCPPS) for sensitive detection of biomolecules. Planetary mixing resulted in uniformly dispersed graphene and conductive polymer ink, which was applied to laser-cut Whatman filter paper substrates. Scanning electron microscopy and Raman spectroscopy showed strong attachment of conductive polymer-functionalized graphene to cellulose fibers. The GCPPS detected dopamine and cytokines, such as tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6) in the ranges of 12.5-400 µM, 0.005-50 ng/mL, and 2 pg/mL-2 µg/mL, respectively, using a minute sample volume of 2 µL. The electrodes showed lower detection limits (LODs) of 3.4 µM, 5.97 pg/mL, and 9.55 pg/mL for dopamine, TNF-α, and IL-6 respectively, which are promising for rapid and easy analysis for biomarkers detection. Additionally, these paper-based biosensors were highly selective (no serpin A1 detection with IL-6 antibody) and were able to detect IL-6 antigen in human serum with high sensitivity and hence, the portable, adaptable, point-of-care, quick, minute sample requirement offered by our fabricated biosensor is advantageous to healthcare applications.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS: 130 000 critical care admissions across both health systems. INTERVENTION: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences.

Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning-Based Clinical Decision Support Systems.

BACKGROUND: Machine learning (ML)-based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the implementation of ML-based CDSS is critical to ensure CDSS is acceptable and useful to clinicians and helps them deliver high-quality health care. Malnutrition is a common and underdiagnosed condition among hospital patients, which can have serious adverse impacts. Early identification and treatment of malnutrition are important. OBJECTIVE: This study aims to evaluate the implementation of an ML tool, Malnutrition Universal Screening Tool (MUST)-Plus, that predicts hospital patients at high risk for malnutrition and identify best implementation practices applicable to this and other ML-based CDSS. METHODS: We conducted a qualitative postimplementation evaluation using in-depth interviews with registered dietitians (RDs) who use MUST-Plus output in their everyday work. After coding the data, we mapped emergent themes onto select domains of the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. RESULTS: We interviewed 17 of the 24 RDs approached (71%), representing 37% of those who use MUST-Plus output. Several themes emerged: (1) enhancements to the tool were made to improve accuracy and usability; (2) MUST-Plus helped identify patients that would not otherwise be seen; perceived usefulness was highest in the original site; (3) perceived accuracy varied by respondent and site; (4) RDs valued autonomy in prioritizing patients; (5) depth of tool understanding varied by hospital and level; (6) MUST-Plus was integrated into workflows and electronic health records; and (7) RDs expressed a desire to eventually have 1 automated screener. CONCLUSIONS: Our findings suggest that continuous involvement of stakeholders at new sites given staff turnover is vital to ensure buy-in. Qualitative research can help identify the potential bias of ML tools and should be widely used to ensure health equity. Ongoing collaboration among CDSS developers, data scientists, and clinical providers may help refine CDSS for optimal use and improve the acceptability of CDSS in the clinical context.

PROSPECT: 4- and 6-year follow-up of a randomised trial of surgery for vaginal prolapse.

INTRODUCTION AND HYPOTHESIS: Our aim was to compare the mid-term results of native tissue, biological xenograft and polypropylene mesh surgery for women with vaginal wall prolapse. METHODS: A total of 1348 women undergoing primary transvaginal repair of an anterior and/or posterior prolapse were recruited between January 2010 and August 2013 from 35 UK centres. They were randomised by remote allocation to native tissue surgery, biological xenograft or polypropylene mesh. We performed both 4- and 6-year follow-up using validated patient-reported outcome measures. RESULTS: At 4 and 6 years post-operation, there was no clinically important difference in Pelvic Organ Prolapse Symptom Score for any of the treatments. Using a strict composite outcome to assess functional cure at 6 years, we found no difference in cure among the three types of surgery. Half the women were cured at 6 years but only 10.3 to 12% of women had undergone further surgery for prolapse. However, 8.4% of women in the mesh group had undergone further surgery for mesh complications. There was no difference in the incidence of chronic pain or dyspareunia between groups. CONCLUSIONS: At the mid-term outcome of 6 years, there is no benefit from augmenting primary prolapse repairs with polypropylene mesh inlays or biological xenografts. There was no evidence that polypropylene mesh inlays caused greater pain or dyspareunia than native tissue repairs.

Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model.

BACKGROUND AND AIM: We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point. METHODS: This retrospective multisite study utilized ED data from the Mount Sinai Health System, NY, during 2015-2019. Data included tabular clinical features and free-text triage notes represented using bag-of-words. A full gradient boosting model, trained on data available at different time points (30, 60, 90, 120, and 150 min), was compared to single models trained exclusively at data available at each time point. This was conducted by concatenating the rows of data available at each time point to one data matrix for the full model, where each row is considered a separate case. RESULTS: The cohort included 1,043,345 ED visits. The full model showed comparable results to the single models at all time points (AUCs 0.84-0.88 for different time points for both the full and single models). CONCLUSION: A full model trained on data concatenated from different time points showed similar results to single models trained at each time point. An ML-based prediction model can use used for identifying hospital admission.

Inflammatory bowel disease-associated malignancies and considerations for radiation impacting bowel: a scoping review.

Background: Inflammatory bowel disease (IBD), subdivided into Crohn's disease (CD) and ulcerative colitis (UC), is an auto-inflammatory gastrointestinal condition with an established increased risk of certain malignancies. Compared to sporadic cancers in the general population, IBD-associated malignancies present unique challenges to providing quality care. Radiation therapy (RT) targeting IBD-associated malignancies may directly impact inflamed bowel, with special considerations for the risk of toxicities. Historically, patients with IBD have been less likely to receive radiotherapy in proximity to bowel due to a poor understanding of the potential for acute and chronic toxicities and unclear treatment outcomes. We present a scoping review, to more fully assess IBD-associated malignancies and their treatment. As opposed to a systematic review, this approach allows us to analyze the broadest range of literature, including experimental and non-experimental research, and reflect on current guidelines and practices. Methods: Literature search: a systematic, scoping search of published literature was conducted using applicable PRISMA scoping review (ScR) guidelines. The literature search was conducted on PubMed and was searched systematically by screening all publications from January 1990 to June 2021. Citations from the included articles were also manually searched. Relevant National Comprehensive Cancer Network guidelines were reviewed. Final query was December 2021 in editing. Articles were selected for full text reading if the abstract reported on malignancy in IBD or bowel toxicities. Results: The pelvic malignancies found in the IBD patient population, including colorectal carcinoma, anal carcinoma, lymphoma, small bowel adenocarcinoma (SBA), and prostate cancer (PCa) are outlined in this scoping review. Additional cancers that have a contested relationship with IBD, including cervical, bladder, and upper GI cancers, are also explored. This review provides literature guided recommendations on the eligibility of patients with IBD to receive RT, management of IBD during and after treatment, and counseling for radiation-induced toxicities. Conclusions: After review of the literature, IBD should not be considered an absolute contraindication to radiation therapy, given the lack of evidence for increased toxicities, and the evolution of RT techniques which limit radiation dose to the bowel.

Pharmacy and Physician Assistant Dual Degree: An Analysis of Its Value and Implications on the Profession.

The pharmacy and physician assistant dual degree is one of the newest programs offered and has been predicted to have a high likelihood of growth in the future. With only an additional year of education, the PharmD-PA dual degree holder will have prescriptive authority upon graduation to expand their clinical roles. Additionally, by combining both medical and pharmacotherapeutics education, these mid-level practitioners could potentially improve healthcare shortages and allow for improvements in patient care. While there are established PharmD-PA dual degree programs, there is low enrollment coupled by rigorous curriculums and financial burdens that students must endure. Despite its limitations, this novel dual degree program offers pharmacy students another method to provide clinical care apart from the post-graduate opportunities. Schools of Pharmacy should look into the development of PharmD-PA dual degree programs as a unique marketing opportunity for admissions and as a non-traditional method of career advancement.

Machine learning to predict in-hospital mortality among patients with severe obesity: Proof of concept study.

Objectives: Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in-hospital mortality among this population. Methods: Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient-boosting machine learning model to identify in-hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held-out data from the fifth hospital. Results: A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in-hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden's index, the model had a sensitivity of 0.77 (95% CI: 0.67-0.86) with a false positive rate of 1:9. Conclusion: A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.

Advancing behavioral interventions for African American/Black and Latino persons living with HIV using a new conceptual model that integrates critical race theory, harm reduction, and self-determination theory: a qualitative exploratory study.

BACKGROUND: Rates of participation in HIV care, medication uptake, and viral suppression are improving among persons living with HIV (PLWH) in the United States. Yet, disparities among African American/Black and Latino PLWH are persistent, signaling the need for new conceptual approaches. To address gaps in services and research (e.g., insufficient attention to structural/systemic factors, inadequate harm reduction services and autonomy support) and improve behavioral interventions, we integrated critical race theory, harm reduction, and self-determination theory into a new conceptual model, then used the model to develop a set of six intervention components which were tested in a larger study. The present qualitative study explores participants' perspectives on the study's acceptability, feasibility, and impact, and the conceptual model's contribution to these experiences. METHODS: Participants in the larger study were African American/Black and Latino PLWH poorly engaged in HIV care and with non-suppressed HIV viral load in New York City (N = 512). We randomly selected N = 46 for in-depth semi-structured interviews on their experiences with and perspectives on the study. Interviews were audio-recorded and professionally transcribed verbatim, and data were analyzed using directed qualitative content analysis. RESULTS: On average, participants were 49 years old (SD = 9) and had lived with HIV for 19 years (SD = 7). Most were male (78%) and African American/Black (76%). All had taken HIV medication previously. Challenging life contexts were the norm, including poverty, poor quality/unstable housing, trauma histories exacerbated by current trauma, health comorbidities, and substance use. Participants found the study highly acceptable. We organized results into four themes focused on participants' experiences of: 1) being understood as a whole person and in their structural/systemic context; 2) trustworthiness and trust; 3) opportunities for self-reflection; and 4) support of personal autonomy. The salience of nonjudgment was prominent in each theme. Themes reflected grounding in the conceptual model. Participants reported these characteristics were lacking in HIV care settings. CONCLUSIONS: The new conceptual model emphasizes the salience of systemic/structural and social factors that drive health behavior and the resultant interventions foster trust, self-reflection, engagement, and behavior change. The model has potential to enhance intervention acceptability, feasibility, and effectiveness with African American/Black and Latino PLWH.