Citations

SummaryThe thrombotic risk with haemoglobin C trait (HbAC) or haemoglobin C disease (HbCC) is unclear. However, individuals with HbCC have demonstrated chronic haemolysis, higher blood viscosity and altered rheology when compared to individuals with wild‐type haemoglobin (HbAA). These physiological alterations may theoretically translate to increased risk of thrombosis; therefore, a systematic literature review was performed to investigate the possible association between HbAC and/or HbCC and thrombosis. Twenty‐two studies met inclusion criteria representing 782 individuals with HbAC (n = 694) or HbCC (n = 88). Fifteen studies described the presence/absence of venous thromboembolism (VTE) in patients with HbAC (n = 685) or HbCC (n = 79), while seven studies described patients with HbAC (n = 9) or HbCC (n = 9) and arterial thrombosis. Most (n = 20) studies were case reports or case series; however, two studies suggested a potential increased VTE risk with HbAC compared to HbAA in (i) all patients (OR 2.2, 95% CI: 0.9–5.5) and in (ii) pregnant individuals (RR 3.7, 95% CI 0.9–16). This review is the largest assessment of patients with HbC trait or disease and thrombosis to date; despite its limitations, the findings suggest HbC may be a predisposing risk factor to thrombosis. Prospective cohort studies are warranted to definitively elucidate the risk of thrombosis in this population.

TPS354 Background: Carriers of germline pathogenic variants or mutations in BRCA2 and BRCA1 (referred to here as gBRCA1/2m) are enriched in metastatic prostate cancer (PrCa), and now have FDA-approved options for PARP inhibitors in late-stage disease. The NCCN guidelines recommends germline genetic testing for PrCa patients with metastatic, node positive and high risk localized prostate cancer. Prior studies have shown that gBRCA1/2m carriers with PrCa are more likely to have high-risk disease (6,7) and to develop lethal disease even after curative intent treatment (8,9). In other gBRCA1/2m-related cancers (breast, ovarian and pancreas), platinum-based regimens are part of neoadjuvant and front-line metastatic therapy options. Carboplatin is a well-tolerated, widely available and inexpensive DNA-damaging alkylating agent that is familiar to oncologists. The study hypothesis is that 4 cycles of neoadjuvant carboplatin, followed by radical prostatectomy for patients with localized high/very high risk PrCa and g BRCA1/2m will result in a meaningful pathologic complete response (pCR) rate. Methods: S2210 is a single-arm, open label study of 12 wks neoadjuvant carboplatin (4 cycles of AUC 5 every 21 days), followed by radical prostatectomy. Key eligibility criteria include people with high/very high-risk disease: cT3a or Grade Group 4-5 PrCa and/or PSA>20 ng/mL, have no evidence of distant metastases by conventional imaging (PSMA-PET-detected mets permitted if conventional imaging is negative and surgeon deems PSMA-detected sites resectable), who are candidates for radical prostatectomy and carboplatin, and have documented evidence of gBRCA1/2m through testing in a CLIA-certified lab. ADT prior to registration is permitted but must be discontinued on study. The study hypothesis is that the study treatment will result in ³30% rate of pathologic complete response (pCR, primary endpoint). A two-stage design will be used such that if ³1 of first 20 eligible patients achieves pCR, 20 further evaluable patients will be enrolled. This design has a significance level of 4.7%, and a power of 99%. Secondary endpoints include biochemical recurrence-free (at 2-, 3-, 5-yrs), metastasis-free and overall survival. Translational/correlative studies include analysis of 2nd allele inactivation in tumor, HRD signature, and ctDNA signature for monitoring of recurrence. S2210 was activated in August 2023. Clinical trial information: NCT05806515 .

INTRODUCTION: Chronic pancreatitis (CP) is a progressive fibroinflammatory disorder lacking therapies and biomarkers. Neutrophil gelatinase-associated lipocalin (NGAL) is a proinflammatory cytokine elevated during inflammation that binds fatty acids (FAs) such as linoleic acid. We hypothesized that systemic NGAL could serve as a biomarker for CP and, with FAs, provide insights into inflammatory and metabolic alterations. METHODS: NGAL was measured by immunoassay, and FA composition was measured by gas chromatography in plasma (n = 171) from a multicenter study, including controls (n = 50), acute and recurrent acute pancreatitis (AP/RAP) (n = 71), and CP (n = 50). Peripheral blood mononuclear cells (PBMCs) from controls (n = 16), AP/RAP (n = 17), and CP (n = 15) were measured by cytometry by time-of-flight. RESULTS: Plasma NGAL was elevated in subjects with CP compared with controls (area under the curve [AUC] = 0.777) or AP/RAP (AUC = 0.754) in univariate and multivariate analyses with sex, age, body mass index, and smoking (control AUC = 0.874; AP/RAP AUC = 0.819). NGAL was elevated in CP and diabetes compared with CP without diabetes ( P < 0.001). NGAL + PBMC populations distinguished CP from controls (AUC = 0.950) or AP/RAP (AUC = 0.941). Linoleic acid was lower, whereas dihomo-γ-linolenic and adrenic acids were elevated in CP ( P < 0.05). Linoleic acid was elevated in CP with diabetes compared with CP subjects without diabetes ( P = 0.0471). DISCUSSION: Elevated plasma NGAL and differences in NGAL + PBMCs indicate an immune response shift that may serve as biomarkers of CP. The potential interaction of FAs and NGAL levels provide insights into the metabolic pathophysiology and improve diagnostic classification of CP.

Abstract Objectives Since the 1970s, a plethora of tools have been introduced to support the medication use process. However, automation initiatives to assist pharmacists in prospectively reviewing medication orders are lacking. The review of many medications may be protocolized and implemented in an algorithmic fashion utilizing discrete data from the electronic health record (EHR). This research serves as a proof of concept to evaluate the capability and effectiveness of an electronic prospective medication order review (EPMOR) system compared to pharmacists’ review. Materials and methods A subset of the most frequently verified medication orders were identified for inclusion. A team of clinical pharmacist experts developed best-practice EPMOR criteria. The established criteria were incorporated into conditional logic built within the EHR. Verification outcomes from the pharmacist (human) and EPMOR (automation) were compared. Results Overall, 13 404 medication orders were included. Of those orders, 13 133 passed pharmacist review, 7388 of which passed EPMOR. A total of 271 medication orders failed pharmacist review due to order modification or discontinuation, 105 of which passed EPMOR. Of the 105 orders, 19 were duplicate orders correctly caught by both EPMOR and pharmacists, but the opposite duplicate order was rejected, 51 orders failed due to scheduling changes. Discussion This simulation was capable of effectively discriminating and triaging orders. Protocolization and automation of the prospective medication order review process in the EHR appear possible using clinically driven algorithms. Conclusion Further research is necessary to refine such algorithms to maximize value, improve efficiency, and minimize safety risks in preparation for the implementation of fully automated systems.

Introduction: Warfarin is a commonly prescribed anticoagulant for treating atrial fibrillation, mechanical valves, and venous thromboembolism. Warfarin dose management remains challenging due to dosing variability between patients and warfarin’s narrow therapeutic window. Time in therapeutic range (TTR) is critical to warfarin’s safety and efficacy, but TTR typically remains low (40-50%) in community practices. Specialized anticoagulation clinics and protocolized approaches can increase TTR but have great administrative burdens and health care costs. Aim: To develop standardized optimal warfarin dose decision support using a machine learning model based on time series anticoagulation data and patient demographic characteristics Methods: The dataset included 12,497 warfarin patients monitored in the anticoagulation tracker of electronic medical records across the Mayo Clinic Enterprise. The dataset contained time series anticoagulation data (warfarin dose, INR, INR target range) and patient demographic characteristics. We implemented an offline deep reinforcement learning model (DRL) to predict the cumulative warfarin dose for the days until the next INR test based on a patient’s historical anticoagulation data. Prior approaches utilized traditional supervised learning methods such as regression or Long Short Term Memory (LSTM) neural networks to approximate a function mimicking the behavior of the training data. On the other hand, DRL learned an optimal dosing policy through continuous interaction and feedback from the training data. The key advantage of DRL is the model can learn to behave differently (and potentially better) for suboptimal clinical states in the data such as overdosing or underdosing. To evaluate the DRL model we compared the predicted warfarin doses with the physician-prescribed doses Results: DRL model’s prediction accuracy was 96.96%, outperforming our implementation of a baseline LSTM model with a prediction accuracy of 70.58%. Further evaluation of the DRL model indicated that the model correctly adjusted the warfarin dose at time steps when patients had out-of-range INRs. Conclusions: Offline deep reinforcement learning demonstrates potential in supporting warfarin dose management to maximize TTR.

Objective This pilot study seeks to identify serum immune signatures across clinical stages of patients with chronic pancreatitis (CP). Methods We performed a cross-sectional analysis of prospectively collected serum samples from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translation StuDies-study. CP subjects were categorised into three clinical stages based on the presence/absence of metabolic complications: (1) CP with no diabetes and exocrine pancreatic dysfunction (EPD), (2) CP with either diabetes or EPD, and (3) CP with diabetes and EPD. Blinded samples were analysed using an 80-plex Luminex assay of cytokines/chemokines/adhesion molecules. Group and pairwise comparisons were performed to characterise immune signatures across CP subgroups. Results A total of 135 CP subjects (evenly distributed between clinical stages) and 50 controls were studied. Interleukin-6 (IL-6), interleukin-8 (IL-8), and soluble intercellular adhesion molecule 1 (sICAM-1) were significantly elevated in CP subjects compared to controls. The levels of IL-6 and IL-8 increased with advancing disease stages, with the highest levels observed in CP with diabetes and EPD (clinical stage 3). Furthermore, hepatocyte growth factor and macrophage-derived chemokine were significantly increased in clinical stage 3 compared to controls. Conclusion Our study reveals a progressive elevation in pro-inflammatory cytokines and chemokines with advancing clinical stages of CP. These findings indicate potential targets for the development of disease-modifying interventions.