Patient Care Information Research Articles

Single centre experience using vessel Fractional Flow Reserve (vFFR) and diagnostic performance compared with invasive Fractional Flow Reserve (FFR)

Abstract Background Fractional flow reserve (FFR) is a diagnostic gold standard in the functional assessment of coronary artery stenoses (1). Alternative invasive and non-invasive methods have been developed in an attempt to avoid limitations associated with FFR including added procedural risk, patient discomfort, time and cost. Vessel fractional flow reserve (vFFR) is a novel software using two angiographic projections to generate a 3D vessel reconstruction which has shown high diagnostic accuracy in predicting a positive FFR (2-4). Methods All patients undergoing coronary angiography between 01/10/2022 and 08/03/2023, where FFR was performed, were assessed. Patients were included where images met requirements necessary to use vFFR software. vFFR measurements were taken retrospectively by a single investigator and not used to inform patient care. Data were expressed as mean +/- standard deviation and %. The correlation of vFFR to FFR was compared using Pearson’s correlation coefficient. Receiving operator characteristic (ROC) area under the curve (AUC) analysis was used to determine diagnostic performance. Statistical analysis was performed using SPSS v27. Results 28 of 37 patients and 32 of 44 vessels were included and analysed with vFFR software. Excluded vessels were unsuitable due to insufficient frame rate or projection angulation during angiography. Mean age was 63 +/- 3.8 years and 75% of patients were male. Of 32 vessels assessed, 20 were LAD, 5 RCA, 5 LCx and 2 LMS. 29 of 32 vFFR values agreed with positive or negative FFR. Positive vFFR values were seen in all 15 vessels with positive FFR. 3 vessels received a positive vFFR where FFR values were negative. Mean vFFR was 0.76 +/- 0.1 and mean FFR was 0.81 +/- 0.09. vFFR correlated significantly with FFR (R=0.777, p<0.001). vFFR demonstrated good diagnostic accuracy in discriminating "positive" (>0.8) and "negative" (≤0.8) FFR with an AUC of 0.758 (95% CI 0.61-0.91, p<0.01). Conclusion vFFR correlated significantly with FFR and all positive FFR values also received positive vFFR values. vFFR demonstrated good diagnostic accuracy in predicting a positive FFR result. Thus, these data support a strategy of using a negative vFFR to avoid further testing.Pearson correlation of vFFR to invFFRROC curve: vFFR predicting invFFR

Effectiveness of the Emergency Severity Index in Early Identification of Sepsis in a Tertiary Care Hospital of a Low-resource Setting

Background: Sepsis is life-threatening organ dysfunction, and a global issue, with incidence and mortality are around 49 M cases in 2017. Annual sepsis incidence from Pakistan is around 1600-2500 per 100, 000 cases with high mortality of approx. 30%. This huge mortality among sepsis and septic shock cases from low-income settings it is necessary to implement a focused and effective triage tool to prioritize and categorize the sick patients from huge bulk of sick patients who attend the ED and to identify those who need to be seen and manage accordingly. Emergency severity index (ESI), a widely acceptable tool that was developed to assess illness severity, hence able to identify the sick patients that need immediate attention at triage. Objectives: Our objective is to determine the effectiveness of ESI triage acuity in early recognition of patient with sepsis and septic shock at triage in a busy ED. Methods: This was a single center, cross sectional and observational study, we include 240 cases who present to the ED of a tertiary care hospital. We follow ESI v. 4.0. All cases presenting to the ED with suspected diagnosis of sepsis or septic shock were included. Data was collected on a predesigned data sheet. Participants were followed by their electronic medical record number through patient care information software where patient’s medical diagnosis and discharge diagnosis are present. Results: Total of 240 patients in the inclusion criteria, area under curve (AUC) for the ESI score I, for septic shock was 0.943 [0.921 - 0.964] with the optimal cutoff value of 2.0, sensitivity of 88.5% and specificity 100%. Similarly, the receiver operating curve (ROC) with area under the curve of ESI Score II for sepsis is found 0.2 [0.129-0.270] with sensitivity 23% and specificity was 12.7%. Conclusion: ESI is a reliable tool which is both sensitive and specific in screening patients with septic shock in a busy emergency department.

Barriers to obtaining and using interoperable information among non-federal acute care hospitals.

To understand barriers to obtaining and using interoperable information at US hospitals. Using 2023 nationally representative survey data on US hospitals (N = 2420), we examined major and minor barriers to exchanging information with other organizations, and how barriers vary by hospital characteristics and methods used to obtain information. Using a series of regression models, we examined how hospital experiences with barriers relate to routine use of information at responding hospitals. In 2023, most hospitals experienced at least one minor (81%) or major (62%) barrier to exchange, with the most common major barriers relating to different vendors and exchange partners' capabilities. Higher-resourced hospitals and those often using network-based exchange tended to experience more minor barriers whereas lower-resourced hospitals and those often using mail/fax or direct access to outside electronic health records experienced more major barriers. In multivariate regression, hospitals indicating "Patient matching" and "Costs to exchange" were a major or minor barrier had the strongest independent negative association with the likelihood of reporting providers at their hospital frequently use information from outside organizations. Despite progress in interoperable exchange, various barriers remain. The prevalence of barriers varied by hospital type and methods used, with barriers more often preventing exchange for lower-resourced hospitals and those using outdated exchange methods. While several technical and policy efforts are underway to address prevalent barriers, it will be important to monitor whether efforts are successful in ensuring information from outside organizations can be seamlessly exchanged and used to inform patient care.

Artificial Intelligence in Medicine

Artificial intelligence in medicine refers to the use of machine learning models to help process medical data and provide medical professionals with important insights, improving health outcomes and patient experience. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare. Therefore, artificial intelligence algorithms and other AI-powered applications are now used to support medical professionals in clinical settings and in ongoing research. There are several applications of Artificial intelligence in medicine, including applications to help detect and diagnose diseases; applications to treat diseases with the help of an AI-powered virtual assistant; AI applications in medical imaging; applications to increase the efficiency of clinical trials; and applications to accelerate drug development. The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.

Exploration of the potential association between newer antiseizure medications and arrhythmias: Integrating pharmacovigilance and bioinformatics evidence

IntroductionArrhythmias resulting from newer antiseizure medications (ASMs) may significantly impact the safety and quality of life of patients with epilepsy. This study investigated the potential association between new first-line or second-line ASMs and arrhythmias. MethodsPharmacovigilance analysis was conducted using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) from 2004 to 2023. A disproportionality analysis was performed to compare newer ASMs with other drugs, using carbamazepine and valproate as positive controls. Newer ASMs were categorized into sodium channel (SCN) main mechanism, SCN possible mechanism, and non-SCN group. The bioinformatics analysis involved retrieving therapeutic gene targets for ASMs from the DrugBank and OMIM databases, as well as identifying arrhythmia disease targets from the GeneCards database. Additionally, enrichment analysis of gene ontology functions and KEGG pathways was conducted. ResultsA total of 3,457 cases of arrhythmias associated with newer ASMs were identified in the FAERS database. Disproportionality analysis indicates that brivaracetam (IC025 = 0.08), zonisamide (IC025 = 0.13), eslicarbazepine (IC025 = 0.39), and lacosamide (IC025 = 0.84) exhibited a positive signal for arrhythmias, with signals predominantly observed in the SCN main mechanism group. Furthermore, bioinformatics analysis revealed the involvement of adrenergic signaling in cardiac myocytes, as well as the participation of sodium channel genes in ASM-induced arrhythmias. ConclusionOur findings suggest a potential association between SCN-ASMs and arrhythmias, highlighting the importance of monitoring and evaluating the safety profiles of newer ASMs in clinical practice. Further research is necessary to elucidate the underlying mechanisms and inform patient care strategies.

Measurable Residual Disease Testing in Multiple Myeloma Following T-Cell Redirecting Therapies.

Several novel T-cell-based therapies have recently become available for multiple myeloma (MM). These T-cell redirecting therapies (TRTs) include chimeric antigen receptor T-cells (CAR-T) and bispecific antibodies (BiAbs). In both clinical trial and real-world data, these therapies have demonstrated high rates of deep clinical response, and some are now approved for second-line treatment for relapsed MM. The deep and sustained clinical responses these therapies are capable of inducing will require sophisticated response monitoring to provide meaningful information for patient care. Obtaining measurable residual disease (MRD) negativity has been validated as an independent positive prognostic marker for progression-free survival (PFS) and overall survival (OS) in both newly diagnosed and relapsed refractory patients with multiple myeloma. Assessment for MRD negativity was performed in all of the trials for FDA-approved TRT. Here, we summarize pertinent data for MRD assessment following TRT in MM and provide a rationale and structured framework for conducting MRD testing post TRT.

Unintentional poisoning in older Australians: a retrospective audit of New South Wales Poisons Information Centre data

Introduction Poisons information centres provide phone-based risk assessment and management advice on poisonings. Unintentional poisonings are a common reason for consulting a poisons information centre, and older adults are at increased risk of unintentional poisoning and adverse outcomes. We describe patterns of unintentional poisoning in older adults reported to a regional poisons information centre. Methods We conducted a retrospective audit of poisons information centre call records and identified unintentional poisonings involving older adults (≥75 years) over a 12-month period to determine patient demographics and poisoning circumstances (substances, contributing factors, and disposition recommendation). Univariate analyses identified variables associated with hospital referral and multivariate models to identify independent risk factors in home-dwelling older adults. Results We identified 2,757 calls. More exposures occurred in women (62%) and involved therapeutic errors (70.8%). Paracetamol was the most common drug involved (11%), and cardiovascular drugs were the most common drug class (36%). Only 14.3% of the study population was referred to hospital. Independent risk factors for hospital referral in home-dwelling older adults were exposure to cardiovascular, centrally acting and antihyperglycaemics, non-oral route of administration and symptoms at the time of the call. Discussion Unintentional poisoning is not uncommon, and our findings are similar to those in other countries over recent decades. These findings suggest that unintentional poisoning in older adults is inadequately addressed by current medication safety strategies. Our findings indicate the value of timely advice by poisons information centres for preventing potentially unnecessary hospitalizations. Conclusion Further research is needed to identify more effective approaches to medication safety strategies for older adults. Poisons information centre data contribute to pharmacovigilance activities and could inform patient care.

Incidence and clinical course of chronic thromboembolic pulmonary hypertension with or without a history of venous thromboembolism in Denmark

BackgroundA considerable number of patients with chronic thromboembolic pulmonary hypertension (CTEPH) lack a history of venous thromboembolism (VTE). ObjectivesWe aimed to examine the annual incidence and prevalence of CTEPH in Denmark and to compare the rates of VTE, bleeding, and mortality between CTEPH patients with and without a history of VTE. MethodsThe Danish National Patient Registry covering all Danish hospitals was used to identify all CTEPH cases between 2009 and 2018, based on combinations of discharge diagnoses using International Classification of Diseases, 10th Revision codes for CTEPH and relevant diagnostic and/or therapeutic interventions. Incidence rates of CTEPH per 100 000 person-years, rates of VTE and bleeding, and 5-year survival estimates were calculated. ResultsIn total, 509 CTEPH patients were identified, of whom 82% had a history of VTE. The yearly incidence rate of CTEPH was 0.5 to 0.8 per 100 000 person-years during the study period. Patients with a history of VTE experienced a 2.5-fold rate of VTE compared with those without prior VTE (2571 vs 980 per 100 000 person-years), while the rate of bleeding events was lower (5008 vs 7139 per 100 000 person-years). The 5-year survival of CTEPH patients with a VTE history was 65% (95% CI, 58%-71%) compared with 45% (95% CI, 31%-58%) in patients without a history of VTE. ConclusionThe Danish incidence rate of CTEPH was comparable with that of other European countries. We identified notable differences in the prognosis of patients with CTEPH with or without a history of VTE. These findings may support generation of hypotheses regarding the pathophysiology of CTEPH and inform current patient care.

Connective Tissue and Autoimmune Diseases Associated With Postsurgical Breast Augmentation: An Updated Review.

This review provides an updated overview of the association between breast augmentation and connective tissue diseases (CTDs). A narrative review of recent literature was conducted. Various autoimmune disorders, such as Raynaud's syndrome, rheumatoid arthritis (RA), and Sjögren's syndrome, have been reported in association with breast implants, particularly silicone implants. Symptoms can be diverse and systemic, including fatigue, joint stiffness, muscle pain, skin rashes, and neurological and gastrointestinal issues. Explantation has shown promise in alleviating symptoms, but the exact pathogenesis remains unclear. Recent studies emphasize the need for informed consent, vigilant monitoring, and multidisciplinary management. The association between breast implants and CTDs remains contentious. While advancements in implant technology have improved patient outcomes, concerns about long-term health implications persist. Continuous research is necessary to elucidate the mechanisms underlying these potential risks and to develop informed patient care guidelines. In this narrative review, we discuss the history of breast implants, illness associated with breast augmentation, and treatment of CTDs and autoimmune diseases associated with breast augmentation.

Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation.

Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.

Visualizing Nursing Narratives: An Evaluation of Latent Dirichlet Allocation Topic Modeling for Care Reports.

The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: "Patient Well-being," "Patient Mobility and Care Activities," and "Treatment and Pain Management," the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.

Using Functional Genomic Data in Monocytes/Macrophages and Genotyping to Nominate Disease-Driving Single Nucleotide Polymorphisms and Target Genes in Juvenile Idiopathic Arthritis.

GWAS have identified multiple regions that confer risk for juvenile idiopathic arthritis (JIA). However, identifying the single nucleotide polymorphisms (SNPs) that drive disease risk is impeded by the SNPs' that identify risk loci being in linkage disequilibrium (LD) with hundreds of other SNPs. Since the causal SNPs remain unknown, it is difficult to identify target genes and use genetic information to inform patient care. We used genotyping and functional data in primary human monocytes/macrophages to nominate disease-driving SNPs on JIA risk haplotypes and identify their likely target genes. We identified JIA risk haplotypes using Immunochip data from Hinks et al (Nature Gen 2013) and the meta-analysis from McIntosh et al (Arthritis Rheum 2017). We used genotyping data from 3,939 children with JIA and 14,412 healthy controls to identify SNPs that: (1) were situated within open chromatin in multiple immune cell types and (2) were more common in children with JIA than the controls (p< 0.05). We intersected the chosen SNPs (n=846) with regions of bi-directional transcription initiation characteristic of non-coding regulatory regions detected using dREG to analyze GRO-seq data. Finally, we used MicroC data to identify gene promoters interacting with the regulatory regions harboring the candidate causal SNPs. We identified 190 SNPs that overlap with dREG peaks in monocytes and126 SNPs that overlap with dREG peaks in macrophages. Of these SNPs, 101 were situated within dREG peaks in both monocytes and macrophages, suggesting that these SNPs exert their effects independent of the cellular activation state. MicroC data in monocytes identified 20 genes/transcripts whose promoters interact with the enhancers harboring the SNPs of interest. SNPs in JIA risk regions that are candidate causal variants can be further screened using functional data such as GRO-seq. This process identifies a finite number of candidate causal SNPs, the majority of which are likely to exert their biological effects independent of cellular activation state in monocytes. Three-dimensional chromatin data generated with MicroC identifies genes likely to be influenced by these SNPs. These studies demonstrate the importance of investigations into the role of innate immunity in JIA.

Disease progression in chronic heart failure is linear: Insights from multistate modelling.

Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

Exploring strategies promoting interprofessional collaborative practice in spinal cord injury rehabilitation at a private South African hospital group

Background. Implementing an interprofessional collaborative practice (IPCP) in hospitals improves health outcomes, enhances patient safety and reduces length of stay by optimising resource utilisation. Although this approach was desired at a private hospital group in South Africa (SA), the specific strategies for clinical guidelines and capacity management pertaining to spinal cord injury (SCI) patients and the implementation of IPCP were unknown. Objective. To explore strategies for implementing clinical guidelines and capacity management for SCI rehabilitation to promote IPCP in a private hospital group in SA. Method. This exploratory descriptive qualitative study included participants who were selected through purposive sampling. The participants comprised 11 staff from various disciplines and management of the private hospital group, one local and one international expert in IPCP. Three online focus groups (n=13) were conducted, involving three to five participants. The transcriptions were analysed thematically using Braun and Clarke’s framework. Results. Interprofessional communication and tools as well as capacity development were three emergent themes from the data. Strategies pertaining to communication methods, digitisation, visual displays, patient care information, educational information and education of healthcare workers were suggested to promote IPCP. Conclusion. These findings from the emergent themes could assist in implementing and integrating an IPCP approach into the rehabilitation service. Further research assessing the efficacy of implementing the IPCP strategies and digital platform would be beneficial.

62 Prognostic biomarker development using deep learning and spatial proteomics in papillary renal cell carcinoma

Abstract Background Papillary renal cell carcinoma (pRCC) is a prevalent but understudied kidney cancer pathology, constituting 15-20% of cases. While many patients achieve curative therapy using nephrectomy, others go on to develop progressive, metastatic disease with poor overall prognosis. In order to improve the current standards of care for pRCC patients, it is critical to identify new methods to identify high-risk patients likely to require more intense and risk-modulated follow-up. Our goals in this project are two-fold: (1) to identify prognostic patient clusters based on spatial architecture of tumor tissue, and (2) to identify a minimal subset of markers to accurately predict the defined clusters for clinical translation. Methods A tissue microarray of 100 pRCC patients was assayed using multiplex immunofluorescence with a 31-antibody panel with immune- and cancer-related proteins. We have developed novel algorithms to identify spatial neighborhoods and features. These features are then used to identify unsupervised clusters of patients. This new approach to classifying patients allows for a more specific and comprehensive description of each patient tumor’s spatial composition and association with diverse clinical outcomes. Next, we developed attention-based deep learning models capable of predicting these spatially-defined clusters directly from the underlying immunofluorescence images and subsequently profiled these for interpretability by retraining based on individual channels. We assessed each model iteratively on a validation set and a held-out test set for final model selection. We focussed on developing an accurate model with the minimal number of channels to enable development of a cost-efficient biomarker assay for the clinic. Results We performed a systematic analysis of the cellular and spatial phenotypes for each patient in our cohort and identified six (6) major spatial clusters. These clusters are based on the spatial neighborhood composition of the tumors (Fig 1A) and describe the composite effects of cell-cell interactions within a patient tumor. We identified one cluster in particular (cluster #6, mainly associated with spatial interaction of CD163+ macrophages) with a significantly worse prognosis than the other clusters. Using our advanced deep learning models, we have been able to achieve excellent validation accuracy in predicting a variety of clinical phenotypes and spatial features, including patient clusters and histological grade. We wished to then reduce the set of required features and began our efforts in predicting tumor vs. normal based on individual immunofluorescence channels and identified 6 channels with &amp;gt;75% individual predictive accuracy (Fig. 1B). Compositing all 6 markers further improves our accuracy to 90%. This success paves the way for exciting future prospects as we work to extend these efforts to identify a minimal clinically-applicable subset of protein markers. Conclusions The combination of computational spatial proteomics and deep learning models has the potential to identify new biomarkers specific to the spatial organization of patient tumors. We have identified a particular patient cluster with multiple associations with CD163+ macrophages demonstrating poor prognosis; selecting these patients may allow for tailored and more informed patient care. We are also working to enhance our deep learning models to circumvent the need for stepwise spatial analysis and, instead, allow for direct-from-image assessment of patients for particular spatial features indicative of various prognostic indicators. Our identified set of 6 markers serves as a basis for further refinements as we work to identify a minimal set of relevant markers able to predict various clinical outcomes from immunofluorescence imaging, offering a promising new avenue in the field of cancer pathology and biomarker development. DOD CDMRP Funding: yes

Uncovering the Knowledge Landscape of Food-Drug Interactions Among Medical Professionals

Background: Drug-food interactions (DFIs) can alter drug pharmacokinetics and pharmacodynamics, impacting patient outcomes. Health care professionals (HCPs) play a critical role in managing these interactions. Specific Background: Despite their significance, the awareness of DFIs among Iraqi medical professionals is inadequately documented. Knowledge Gap: There is a lack of comprehensive studies evaluating the knowledge of DFIs among medical professionals in Iraq, which is crucial for improving patient care and therapeutic efficacy. Aims: This study aimed to evaluate the knowledge of food-drug interactions among Iraqi medical professionals, including medical students, newly graduated doctors, and junior doctors. Methods: A cross-sectional study was conducted using a self-administered questionnaire with 20 questions evaluating knowledge on common DFIs. Results: The study revealed a concerning lack of knowledge about DFIs among participants, with junior doctors scoring an average of 7.1 out of 15, newly graduated doctors 6.38, and sixth-grade medical students 5.27. Only 13% felt knowledgeable about DFIs, while 31.7% acknowledged their insufficient understanding. Notably, knowledge on interactions such as tyramine-rich foods with antidepressants and grapefruit juice with statins was limited. Novelty: This study provides the first comprehensive assessment of DFI knowledge among Iraqi medical professionals, highlighting significant gaps and educational needs. Implications: The findings underscore the necessity for enhanced educational programs targeting DFIs in medical curricula, ensuring HCPs are equipped to counsel patients effectively, thereby improving patient safety and therapeutic outcomes. These results emphasize the importance of targeted educational interventions to bridge the knowledge gap, ultimately contributing to better health outcomes and informed patient care in Iraq. Highlights: Knowledge Gaps: Iraqi medical professionals lack understanding of common food-drug interactions. Education Needed: Enhanced training on interactions is essential in medical curricula. Patient Safety: Improving knowledge will lead to better patient outcomes. Keywords: Food–Drug Interactions, Knowledge, Drug Interaction, Medical Students

Risk factors for secondary bladder cancer following prostate cancer radiotherapy.

This review investigates the complex landscape of secondary bladder cancer (SBC) after radiotherapy for prostate cancer (PCa). External beam radiotherapy (EBRT) poses an increased risk for SBC, while brachytherapy seems to be associated with smaller increased risks for SBC due to its targeted radiation delivery, sparing the surrounding bladder tissue. Secondary cancers in the bladder are the most frequently diagnosed secondary cancers in the PCa patient population treated with radiotherapy. Patient-related factors are pivotal, with age emerging as a dual-edged factor. While advanced age is a recognized risk for bladder cancer, younger PCa patients exhibit higher susceptibility to radiation-induced cancers. Smoking, a well-established bladder cancer risk factor, increases this vulnerability. Studies highlight the synergistic effect of smoking and radiation exposure, amplifying the likelihood of genetic mutations and SBC. The latency period of SBC, which spans years to decades, remains a critical aspect. There is a strong dose-response relationship between radiation exposure and SBC risk, with higher doses consistently being associated with a higher SBC risk. While specific models for therapeutic radiation-induced SBC are lacking, insights from related studies, like the Atomic Bomb survivor research, emphasize the bladder's sensitivity to radiation-induced cancer. Chemotherapy in combination with radiotherapy, although infrequently used in PCa, emerges as a potential risk for bladder cancer. Bladder cancer's complex epidemiology, encompassing risk factors, treatment modalities, and cancer types, provides a comprehensive backdrop. As research refines understanding, we hope that this review contributes to guide clinicians, inform patient care, and shape preventive strategies on SBC.

Ethical and informative trials: How the COVID-19 experience can help to improve clinical trial design

During the COVID-19 pandemic, the race to find an effective vaccine or treatment saw an ‘extraordinary number’ of clinical trials being conducted. While there were some key success stories, not all trials produced results that informed patient care. There was a significant amount of waste in clinical research during the pandemic which is said to have hampered an evidence-based response. Conducting trials which could have been predicted to fail to answer the research question (e.g. because they are not large enough to provide a definitive result) is not only a waste of resources but also a breach of research participants’ trust and a violation of research ethics. The issues seen in COVID-19 clinical trials are symptomatic of a wider trial design crisis where many trials do not provide informative results. This paper examines the roles of key stakeholders in delivering ethical and informative trials and whether guidance published by ‘The Good Clinical Trials Collaborative’ could be used to align key stakeholder groups and enable a joined-up approach to improve clinical trial design.

The promise and challenges of Artificial Intelligence-Large Language Models (AI-LLMs) in obstetric and gynecology

HIGHLIGHTS 1. The article highlights how Artificial Intelligence with Large Language Models (AI-LLMs) greatly improves diagnosis and treatment personalization in obstetrics &amp; gynecology, and also enhances medical education through interactive simulations and up-to-date learning materials.2. The article also discusses the ethical issues linked to AI, emphasizing the need for cooperation among different stakeholders to use AI responsibly in medicine, focusing on protecting data privacy and minimizing reliance on technology. ABSTRACT The introduction of Artificial Intelligence through Large Language Models (AI-LLM) into medicine holds great promise for improving patient care and medical education, especially in obstetrics and gynecology. AI-LLM can significantly improve diagnostic accuracy and treatment efficiency by utilizing large medical databases, which is especially useful for dealing with rare diseases that are difficult to document or understand by human practitioners alone. In addition, AI-LLM can provide informed patient care recommendations by analyzing large amounts of data and providing insights based on unique patient profiles, with the added benefit of being accessible 24/7 via the internet. This constant availability ensures that patients receive prompt information and assistance as needed. In the field of education, AI-LLMs enhance the learning experience by incorporating interactive simulations into the curriculum, improving medical students' and professionals' practical knowledge. They also ensure that educational materials are always up-to-date reflecting the most recent research and worldwide medical standards. This access latest information from global resources helps to bridge the educational gap, making advanced knowledge more accessible to learners regardless of their geographic location. However, the introduction of AI-LLMs is not without challenges. Ethical issues, such as data privacy and the risk of overreliance on technology, must be addressed. Effective management of these concerns necessitates collaboration among medical professionals, technological experts, academics, hospital committees, and representatives of patients. This multidisciplinary teamwork is vital for upholding ethical norms and preserving patient dignity and respect. AI-LLMs can considerably improve both patient care and medical education in obstetrics and gynecology provided they are appropriately balanced with innovation and ethics.

The Development and Feasibility of a Novel Electronic Patient-Reported Outcome Measures (Eproms) Questionnaire in patients with penile cancer

BackgroundPenile cancer (PeCa) is a rare cancer with surgical options that affect patients’ quality-of-life. Patient-reported outcome measures (PROMs) are uncommonly utilized in this cohort despite their several patient-centered benefits and there are recommendations to further digitalize PROMs. This prospective, population-based study aimed to report the development and feasibility of a novel electronic patient-reported outcome measures (ePROMs) questionnaire for patients with PeCa. Materials and methodsA novel ePROMs questionnaire was developed and sent to patients 3 days before outpatient clinic appointments. The questionnaire included up to 30 items on patient symptomology and quality-of-life, including a self-reported quality-of-life score (rated 0 being worst and 100 being best). Data were collected for patients followed up between August 2021 and May 2022. The primary feasibility outcomes, adherence and engagement, were measured by response and drop-out rates. Differences in responders and nonresponders were also ascertained. Secondary outcomes explored the clinical utility of the questionnaire. Responders were subcategorized into 3 groups: circumcision (Ci), partial penectomy (PP) or total penectomy (TP) and differences were analyzed. This study was approved by the local Trust Governance Panel, including for ethical considerations. Results220 adult males were sent ePROMs questionnaires, and 141 (64%) responded initially. The mean dropout rate of subsequent questionnaires was 56%. The maximum number of questionnaires sent to and completed by a patient was 8 (n = 1). Nonresponders were older (P < .0001), with poorer performance status (P < .0001) and lower body mass index (P = .0288). TP patients reported the lowest median quality-of-life score 68.50 (8-99), followed by the Ci group (72.0, 37-94) and the PP group (76.0, 10-99). ConclusionsPatients initially engaged and adhered to the ePROMs questionnaire but struggled to maintain this over time. Clinical data gathered by the questionnaire may be utilized to inform patient care. The questionnaire requires additional validation, research, and education.