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Abstract Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.

Background A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in medically complex patients, to mitigate the effects of adverse events, disease progression, and medical error. This challenging problem requires clinical surveillance, early recognition, timely notification of the appropriate clinicians, and effective intervention.

Introduction Sub-optimal inhaler adherence undermines the efficacy of pharmacotherapy in COPD. Digitalised care pathways are increasingly used to improve inhaler-use behaviour remotely. This review investigated the feasibility and impact of remote electronic inhaler adherence monitoring (EIM) and intervention platforms on clinical outcomes in COPD. Methods A literature search was conducted and studies investigating maintenance inhaler use among people with COPD using digital technology were selected. Pairwise and proportional meta-analyses were employed with heterogeneity assessed using I2 statistics. When meta-analysis was not feasible, a narrative synthesis of outcomes was conducted. Results We included 10 studies including 1432 people with COPD whose maintenance inhaler usage was supported by digital inhalers and apps featuring audiovisual reminders and educational content with or without engagement with healthcare providers (HCPs). Inhaler adherence rate (AR) varied with calculation methods, but an overall suboptimal adherence was observed among people with COPD. HCP-led adherence interventions alongside EIM improved mean AR by 18% (95% CI 9–27) versus passive EIM only. Enhanced AR may reduce COPD-related healthcare utilisation with little impact on health-related quality of life and exacerbation rate. Despite encountering technical issues among 14% (95% CI 5–23%) of participants, 85% (95% CI 76–94%) found digital platforms convenient to use, while 91% (95% CI 79–100%) perceived inhaler reminders as helpful. Conclusion Digitalised interventions can enhance maintenance inhaler adherence in COPD but their overall effect on clinical outcomes remains uncertain. Further work is required to tailor interventions to individuals’ adherence behaviour and investigate their longer-term impact.

Background: The role of biceps tenodesis (BT) during open reduction internal fixation (ORIF) of proximal humerus fractures (PHFs) remains unclear. A subset of patients undergoing ORIF have persistent pain with unclear etiology. The purpose of our study was to compare outcomes of ORIF of PHFs with and without concomitant BT. We hypothesize patients undergoing BT at the time of ORIF will have improved patient-reported outcome scores with fewer secondary procedures related to treatment of the biceps. Methods: In this retrospective cohort study, all patients undergoing ORIF for a PHF at a single level one trauma center from January 2019 to June 2022 were reviewed. Patients under the age of 18 were excluded. Primary outcomes were patient-reported outcomes measurement information system physical function, depression, and pain interference scores at 5 time points up to final follow-up. Secondary outcomes included total operative time, complications, subsequent procedures, steroid injections, and range of motion. Chi-square tests were performed for categorical values and paired t-tests for continuous variables. Results: 71 patients met inclusion criteria: 41 undergoing ORIF without BT and 30 undergoing ORIF with BT. Average follow-up was 11 months. There were no statistically significant differences in patient demographics between groups. There were no differences in patient-reported outcomes measurement information system scores at any time point postoperatively. At final follow-up patients in the ORIF with BT group had higher forward flexion than those who did not undergo BT (142 vs. 123 degrees, respectively, P

Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. The development and independent validation datasets included 62,154 patients from two EDs and 73,453 patients from one ED, respectively. Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. These machine learning models were also compared against an existing clinical risk score. To support clinical actionability, clinician investigators conducted manual chart reviews of the cases identified by the model. Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. The model, which combined DICE and LR, boosted predictive performance while providing well-defined features. The model was relatively robust to sensitivity analyses regarding performance across age, race, and by varying predictor availability but less robust across diagnostic groups. Clinician examination demonstrated discrete model performance characteristics within clinical subtypes of RVA. This machine learning model demonstrated a strong predictive performance for 72- RVA. Despite the limited clinical actionability potentially due to model complexity, the rarity of the outcome, and variable relevance, the clinical examination offered guidance on further variable inclusion for enhanced predictive accuracy and actionability.

Abstract Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

Abstract BackgroundNumerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. ObjectiveTo help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). MethodsWe extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. ResultsA total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). ConclusionsWe developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

Aims: The PARAMEDIC-3 trial evaluates the clinical and cost-effectiveness of an intraosseous first strategy, compared with an intravenous first strategy, for drug administration in adults who have sustained an out-of-hospital cardiac arrest. Methods: PARAMEDIC-3 is a pragmatic, allocation concealed, open-label, multi-centre, superiority randomised controlled trial. It will recruit 15,000 patients across English and Welsh ambulance services. Adults who have sustained an out-of-hospital cardiac arrest are individually randomised to an intraosseous access first strategy or intravenous access first strategy in a 1:1 ratio through an opaque, sealed envelope system. The randomised allocation determines the route used for the first two attempts at vascular access. Participants are initially enrolled under a deferred consent model.The primary clinical-effectiveness outcome is survival at 30-days. Secondary outcomes include return of spontaneous circulation, neurological functional outcome, and health-related quality of life. Participants are followed-up to six-months following cardiac arrest. The primary health economic outcome is incremental cost per quality-adjusted life year gained. Conclusion: The PARAMEDIC-3 trial will provide key information on the clinical and cost-effectiveness of drug route in out-of-hospital cardiac arrest.Trial registration: ISRCTN14223494, registered 16/08/2021, prospectively registered.

The vasopressin type 2 receptor (V2R) is an essential G protein-coupled receptor (GPCR) in renal regulation of water homeostasis. Upon stimulation, the V2R activates Gαs and Gαq/11, which is followed by robust recruitment of β-arrestins and receptor internalization into endosomes. Unlike canonical GPCR signaling, the β-arrestin association with the V2R does not terminate Gαs activation, and thus, Gαs-mediated signaling is sustained while the receptor is internalized. Here, we demonstrate that this V2R ability to co-interact with G protein/β-arrestin and promote endosomal G protein signaling is not restricted to Gαs, but also involves Gαq/11. Furthermore, our data imply that β-arrestins potentiate Gαs/Gαq/11 activation at endosomes rather than terminating their signaling. Surprisingly, we found that the V2R internalizes and promote endosomal G protein activation independent of β-arrestins to a minor degree. These new observations challenge the current model of endosomal GPCR signaling and suggest that this event can occur in both β-arrestin-dependent and -independent manners.

Introduction Antibiotics are time-critical in the management of sepsis. When infectious organisms are unknown, patients are treated with empiric antibiotics to include coverage for gram-negative organisms, such as antipseudomonal cephalosporins and penicillins. However, in observational studies, some antipseudomonal cephalosporins (eg, cefepime) are associated with neurologic dysfunction while the most common antipseudomonal penicillin (piperacillin–tazobactam) is associated with acute kidney injury (AKI). No randomised control trials have compared these regimens. This manuscript describes the protocol and analysis plan for a trial designed to compare the effects of antipseudomonal cephalosporins and antipseudomonal penicillins among acutely ill patients receiving empiric antibiotics.Methods and analysis The Antibiotic Choice On ReNal outcomes trial is a prospective, single-centre, non-blinded randomised trial being conducted at Vanderbilt University Medical Center. The trial will enrol 2500 acutely ill adults receiving gram-negative coverage for treatment of infection. Eligible patients are randomised 1:1 to receive cefepime or piperacillin–tazobactam on first order entry of a broad-spectrum antibiotic covering gram-negative organisms. The primary outcome is the highest stage of AKI and death occurring between enrolment and 14 days after enrolment. This will be compared between patients randomised to cefepime and randomised to piperacillin–tazobactam using an unadjusted proportional odds regression model. The secondary outcomes are major adverse kidney events through day 14 and number of days alive and free of delirium and coma in 14 days after enrolment. Enrolment began on 10 November 2021 and is expected to be completed in December 2022.Ethics and dissemination The trial was approved by the Vanderbilt University Medical Center institutional review board (IRB#210591) with a waiver of informed consent. Results will be submitted to a peer-reviewed journal and presented at scientific conferences.Trial registration number NCT05094154.

Abstract Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p

Background: Six million Americans suffer from atrial fibrillation (AF), a heart rhythm abnormality that significantly increases the risk of stroke. AF is responsible for 15% of ischemic strokes, which lead to permanent disability in 60% of cases and death in up to 20%. Anticoagulation (AC) is the mainstay for stroke prevention in patients with AF. Despite guidelines recommending AC for patients, up to half of eligible patients are not on AC. Clinical decision support tools in the electronic health record (EHR) can help bridge the disparity in AC prescription for patients with AF. Objective: To enhance and assess the effectiveness of our previous rule-based alert on AC initiation and persistence in a diverse patient population from UMass-Memorial Medical Center and University of Florida at Jacksonville. Methods/Results: Using the EHR, we will track AC initiation and persistence. We will interview both patients and providers to determine a measure of satisfaction with AC management. We will track digital crumbs to better understand the alert’s mechanism of effect and further add enhancements. These enhancements will be used to refine the alert and aid in developing an implementation toolkit to facilitate use of the alert at other health systems. Conclusion: If the number of AC starts, the likelihood of persisting on AC, and the frequency alert use are found to be higher among intervention vs control providers, we believe such findings will confirm our hypothesis on the effectiveness of our alert.