Current Diagnosis Research Articles

Current Diagnosis of von Willebrand Disease in Italy: 3 Years Following the Release of the International Guidelines.

The American Society of Hematology-International Society on Thrombosis and Haemostasis-National Hemophilia Foundation-World Federation of Hemophilia 2021 International Guidelines (IGL) on von Willebrand disease (VWD) have pointed out many challenges, mainly in the diagnostic approach of VWD patients. To determine the impact of these IGL on the current clinical and laboratory diagnosis of Italian VWD patients, we have recently conducted a survey among 43 centers affiliated with the Italian Association of Hemophilia Centers (AICE). Directors and colleagues responsible for the management of VWD patients were invited to report in a detailed questionnaire how IGL recommendations about the assessment of the specific activities of von Willebrand Factor (VWF) could be applied at their local sites. Results from such a survey showed that bleeding assessment tools, VWF antigen, and factor VIII procoagulant are currently in use in all centers. The automated assays for platelet-dependent VWF activity with or without ristocetin described in IGL have been used since 2021 in 37/43 (86%) centers. Among other laboratory tests, VWF collagen binding, ristocetin-induced platelet agglutination, multimeric analysis, VWF propeptide, VWF:FVIII binding assay were available in 49, 63, 26, 7, and 28% of AICE, respectively. Analyses of VWF gene defects are available only at 3/43 (7%) centers. Desmopressin (DDAVP) infusion trials at diagnosis, with measurements of VWF activities at 1 and 4 hours post-DDAVP, is currently performed at 38/43 (88%) centers. Based on this information, a simplified clinical diagnosis using a few automated tests before and after DDAVP has been proposed. Such a diagnostic approach will be validated prospectively in a large cohort of Italian VWD patients.

Intelligent Diagnosis Method of Transformer Based on Oil Chromatographic Data

Abstract As one of the most important equipment in the operation of power system, ensuring the safe and stable operation of power transformer is a prerequisite for ensuring the normal supply of power grid. The failure of the power transformer will lead to the interruption of the power supply of the power grid and cause huge losses to the national economy. With the rapid development of China’s power grid scale and the continuous improvement of the intelligent construction of modern power system, the number of power equipment such as transformers is increasing. Therefore, it is necessary to make timely and accurate judgment on the status of key power transformation and distribution equipment in the power system, and grasp the operation of electrical equipment in real time, so as to ensure the reliability of power supply in the power system. This paper takes 220kV oil-immersed transformer as the research object. This paper considers the problems and characteristics of the current transformer fault diagnosis methods. By making full use of dissolved gas analysis (DGA) information in oil, a method for diagnosing faults in electrical transformers based on particle swarm optimization and generalized regression neural networks has been proposed. The simulation comparison experiment is carried out to select the most suitable transformer intelligent diagnosis method based on oil chromatographic data.

A framework for exclusion of alternative diagnoses in sarcoidosis

Sarcoidosis is a multisystem granulomatous syndrome that arises from a persistent immune response to a triggering antigen(s). There is no “gold standard” test or algorithm for the diagnosis of sarcoidosis, making the diagnosis one of exclusion. The presentation of the disease varies substantially between individuals, in both the number of organs involved, and the manifestations seen in individual organs. These qualities dictate that health care providers diagnosing sarcoidosis must consider a wide range of possible alternative diagnoses, from across a range of presentations and medical specialties (infectious, inflammatory, cardiac, neurologic). Current guideline-based diagnosis of sarcoidosis recommends fulfillment of three criteria: 1) compatible clinical presentation and/or imaging 2) demonstration of granulomatous inflammation by biopsy (when possible) and, 3) exclusion of alternative causes, but do not provide guidance on standardized strategies for exclusion of alternative diagnoses. In this review, we provide a summary of the most common differential diagnoses for sarcoidosis involvement of lung, eye, skin, central nervous system, heart, liver, and kidney. We then propose a framework for testing to exclude alternative diagnoses based on pretest probability of sarcoidosis, defined as high (typical findings with sarcoidosis involvement confirmed in another organ), moderate (typical findings in a single organ), or low (atypical/findings suggesting of an alternative diagnosis). This work highlights the need for informed and careful exclusion of alternative diagnoses in sarcoidosis.

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning.

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

Pregnancy Diagnosis on Beef Cattle Today

This publication is directed to beef producers, farmworkers, and Extension agents with the purpose of providing information regarding current pregnancy diagnosis methods for beef cattle. Written by Maria Camila Lopez Duarte, Kalyn Waters, Nick Simmons, and Angela Gonella and published by the UF/IFAS Department of Animal Sciences, May 2024.

Posttraumatic Stress Disorder and Obstructive Sleep Apnea in Twins

Obstructive sleep apnea (OSA) is a common condition in older adult (aged >65 years) populations, but more mechanistic research is needed to individualize treatments. Previous evidence has suggested an association between OSA and posttraumatic stress disorder (PTSD) but is limited by possible selection bias. High-quality research on this association with a careful evaluation of possible confounders may yield important mechanistic insight into both conditions and improve treatment efforts. To investigate the association of current PTSD symptoms and PTSD diagnosis with OSA. This cross-sectional study of twin pairs discordant for PTSD, which allows for adjustment for familial factors, was conducted using in-laboratory polysomnography from March 20, 2017, to June 3, 2019. The study sample comprised male veteran twins recruited from the Vietnam Era Twin Registry. The data analysis was performed between June 11, 2022, and January 30, 2023. Symptoms of PTSD in twins who served in the Vietnam War. Diagnosis of PTSD was a secondary exposure. Obstructive sleep apnea was assessed using the apnea-hypopnea index (AHI) (≥4% oxygen saturation criterion as measured by events per hour) with overnight polysomnography. Symptoms of PTSD were assessed using the PTSD Checklist (PCL) and structured clinical interview for PTSD diagnosis. A total of 181 male twins (mean [SD] age, 68.4 [2.0] years) including 66 pairs discordant for PTSD symptoms and 15 pairs discordant for a current PTSD diagnosis were evaluated. In models examining the PCL and OSA within pairs and adjusted for body mass index (BMI) and other sociodemographic, cardiovascular, and psychiatric risk factors (including depression), each 15-point increase in PCL was associated with a 4.6 (95% CI, 0.1-9.1) events-per-hour higher AHI. Current PTSD diagnosis was associated with an adjusted 10.5 (95% CI, 5.7-15.3) events-per-hour higher AHI per sleep-hour. Comparable standardized estimates of the association of PTSD symptoms and BMI with AHI per SD increase (1.9 events per hour; 95% CI, 0.5-3.3 events per hour) were found. This cross-sectional study found an association between PTSD and sleep-disordered breathing. The findings have important public health implications and may also enhance understanding of the many factors that potentially affect OSA pathophysiology.

Unsupervised transfer learning for fault diagnosis across similar chemical processes

Fault diagnosis plays a crucial role in chemical processes to prevent major accidents. Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities significantly. However, the success of deep learning diagnosis models primarily relies on access to the extensive labeled dataset of faults. Another limitation is the variation of ambient temperature and raw material components contributing to diverse operating conditions. These challenges lead to the current fault diagnosis techniques being unsuitable for universal application across different devices. In this paper, a novel unsupervised transfer learning method called multiple source domain adaptation network (MSDAN) is proposed for industrial fault diagnosis across similar chemical processes. Owing to the limited availability of fault data in industrial manufacturing, fault samples for model training are expanded via an efficient generative adversarial network variant. Categorical features of various source domains are precisely extracted through distinct channels by an integration model of Transformer and multiscale convolutional neural network. Multi-channel Domain adaptation based on polynomial kernel-induced maximum mean discrepancy aligns the joint distributions of multiple domains and facilitates subsequent classification tasks. The effectiveness and robustness of the proposed method are demonstrated through experiments conducted on the multimode Tennessee Eastman process and the real-world fluid catalytic cracking process across different units.

Biliary tract cancer

BackgroundBiliary tract cancers comprise a heterogeneous collection of malignancies usually described as cholangiocarcinoma of the intra- or extrahepatic bile duct, including perihilar cholangiocarcinoma and gallbladder cancer. MethodsA review of pertinent parts of the ESSO core curriculum for the UEMS diploma targets (Fellowships exam, EBSQ), based on updated and available guidelines for diagnosis, surgical treatment and oncological management of cholangiocarcinoma. ResultsFollowing the outline from the ESSO core curriculum we present the epidemiology and risk factors for cholangiocarcinoma, as well as the rationale for the current diagnosis, staging, (neo-)adjuvant treatment, surgical management, and short- and long-term outcomes. The available guidelines and consensus reports (i.e. NCCN, BGS and ESMO guidelines) are referred to. Recognition of biliary tract cancers as separate entities of the intrahepatic biliary ducts, the perihilar and distal bile duct as well as the gallbladder is important for proper management, as they each provide distinct clinical, molecular and treatment profiles to consider. ConclusionCore competencies in knowledge to the diagnosis, management and outcomes of biliary tract cancers are presented.

A novel fault diagnosis method for Bayesian networks fusing models and data

Data-driven methods are the mainstream methods applied in current fault diagnosis research; however, in natural mechanical systems, fault samples are difficult or even impossible to obtain, and physical model-based methods cannot easily model the faults of complex systems. To address the respective limitations of data-driven and model diagnosis methods in fault diagnosis, in this paper, a Bayesian network fault diagnosis method based on a joint model and data modelling is proposed. Conditional Gaussian Bayesian networks are used to fuse discrete and continuous information, and the discrete information in the residuals is used to compensate for the insufficient feature information problem caused by small amounts of data. To address the exponential modeling complexity growth in the case of multiple fault types cases, a single classification method is chosen for fault classification. Meanwhile, the T2 statistics distance suppression method is introduced into the Bayesian network to improve the diagnostic capabilities for new types of faults. By comparing the mainstream methods that address small sample diagnosis and the existing fusion methods, the experimental results show that in cases with small numbers of samples, the method in this paper has good fault diagnosis capabilities by effectively utilizing the model’s diagnostic results to compensate for the diagnostic errors caused by a lack of data, and it has an improved ability to recognize unknown faults.

Traumatic Brain Injury and Posttraumatic Stress Disorder Are Associated with Physical Health Burden among Post-9/11 Women Veterans.

Background: Little research focuses on physical health outcomes of traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) among post-9/11 women veterans (WVs). This study examined lifetime TBI, current PTSD, and their associations with biomarkers of cardiometabolic health, sleep, pain, and functional disability among post-9/11 WVs. Methods: WVs (n = 90) from the Translational Research Center for TBI and Stress Disorders longitudinal cohort study were included in this study. Gold standard clinician administered interviews assessed lifetime TBI (Boston Assessment of TBI-Lifetime) and current PTSD symptoms (Clinician-Administered PTSD Scale-IV). Objective measures of health included waist-hip ratio (WHR) and fasted blood biomarker (high density lipoprotein [HDL], low density lipoprotein [LDL], blood glucose, triglycerides) levels. Self-reported surveys assessed sleep, pain, and functional disability. Results: Just under two-thirds (58.9%) of WVs experienced a lifetime TBI, and just over half (53.3%) of this sample had a current PTSD diagnosis at the time of testing. Lifetime TBI was significantly associated with higher WHR, triglycerides levels, and worse pain and sleep (ps = <0.01 to 0.02; ds = 0.01 to 1.12). Current PTSD was significantly associated with higher WHR, lower HDL, and worse pain and sleep (ps = <0.01 to 0.02; ds = 0.009 to 1.19). PTSD was significantly associated with lower total functioning and each of its subdomains (βs = -0.58 to 0.63; ps = <0.001 to 0.02). Lifetime TBI was significantly associated with total functioning, mobility, and life/work (βs = -0.20 to 0.30; ps = <0.01 to 0.02). Conclusions: These findings highlight the importance of screening for lifetime TBI and cardiovascular disease for WVs and support transdiagnostic treatment approaches targeting physical health outcomes.

Excavation of Biomarker Candidates for the Diagnosis of Talaromyces marneffei Infection via Genome-Wide Prediction and Functional Annotation of Secreted Proteins.

Talaromyces marneffei is the third most common infectious pathogen in AIDS patients and leads to the highest death rate in Guangxi, China. The lack of reliable biomarkers is one of the major obstacles in current clinical diagnosis, which largely contributes to this high mortality. Here, we present a study that aimed at identifying diagnostic biomarker candidates through genome-wide prediction and functional annotation of Talaromyces marneffei secreted proteins. A total of 584 secreted proteins then emerged, including 382 classical and 202 nonclassical ones. Among them, there were 87 newly obtained functional annotations in this study. The annotated proteins were further evaluated by combining RNA profiling and a homology comparison. Three proteins were ultimately highlighted as biomarker candidates with robust expression and remarkable specificity. The predicted phosphoinositide phospholipase C and the galactomannoprotein were suggested to play an interactive immune game through metabolism of arachidonic acid. Therefore, they hold promise in developing new tools for clinical diagnosis of Talaromyces marneffei and also possibly serve as molecular targets for future therapy.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development

BackgroundThe global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms. MethodsTwenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC. ResultsStatistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features. ConclusionsChildren with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.

Influenza A, Influenza B, human respiratory syncytial virus and SARSCoV-2 molecular diagnostics and epidemiology in the post COVID-19 era

BackgroundThe concurrent circulation of SARS-CoV-2 with other respiratory viruses is unstoppable and represents a new diagnostic reality for clinicians and clinical microbiology laboratories. Multiplexed molecular testing on automated platforms that focus on the simultaneous detection of multiple respiratory viruses in a single tube is a useful approach for current and future diagnosis of respiratory infections in the clinical setting.MethodsTwo time periods were included in the study: from February to April 2022, an early 2022 period, during the gradual lifting of COVID-19 prevention measures in the country, and from October 2022 to April 2023, the 2022/23 respiratory infections season. We analysed a total of 1,918 samples in the first period and 18,131 respiratory samples in the second period using a multiplex molecular assay for the simultaneous detection of Influenza A (Flu-A), Influenza B (Flu-B), Human Respiratory Syncytial Virus (HRSV) and SARS-CoV-2.ResultsThe results from early 2022 showed a strong dominance of SARS-CoV-2 infections with 1,267/1,918 (66.1%) cases. Flu-A was detected in 30/1,918 (1.6%) samples, HRSV in 14/1,918 (0.7%) samples, and Flu-B in 2/1,918 (0.1%) samples. Flu-A/SARS-CoV-2 co-detections were observed in 11/1,267 (0.9%) samples, and HRSV/SARS-CoV-2 co-detection in 5/1,267 (0.4%) samples. During the 2022/23 winter respiratory season, SARS-CoV-2 was detected in 1,738/18,131 (9.6%), Flu-A in 628/18,131 (3.5%), Flu-B in 106/18,131 (0.6%), and HRSV in 505/18,131 (2.8%) samples. Interestingly, co-detections were present to a similar extent as in early 2022.ConclusionThe results show that the multiplex molecular approach is a valuable tool for the simultaneous laboratory diagnosis of SARS-CoV-2, Flu-A/B, and HRSV in hospitalized and outpatients. Infections with Flu-A/B, and HRSV occurred shortly after the COVID-19 control measures were lifted, so a strong reoccurrence of various respiratory infections and co-detections in the post COVID-19 period was to be expected.

Diagnostic efficiency of conventional ultrasound, shear wave elastography, and superb microvascular imaging in evaluating ulnar neuropathy at the elbow.

The current diagnosis of ulnar neuropathy at the elbow (UNE) relies mainly on the clinical presentation and nerve electrodiagnostic (EDX) testing, which can be uncomfortable and yield false negatives. The aim of this study was to investigate the diagnostic value of conventional ultrasound, shear wave elastography (SWE), and superb microvascular imaging (SMI) in diagnosing UNE. We enrolled 40 patients (48 elbows) with UNE and 48 healthy volunteers (48 elbows). The patients were categorized as having mild, moderate or severe UNE based on the findings of EDX testing. The cross-sectional area (CSA) was measured using conventional ultrasound. Ulnar nerve (UN) shear wave velocity (SWV) and SMI were performed in a longitudinal plane. Based on the EDX findings, UNE severity was graded as mild in 4, moderate in 10, and severe in 34. The patient group showed increased ulnar nerve CSA and stiffness at the site of maximal enlargement (CSA mean at the site of max enlargement [CSAmax] and SWV mean at the site of max enlargement [SWVmax]), ulnar nerve CSA ratio, and stiffness ratio (elbow-to-upper arm), compared with the control group (p < .001). Furthermore, the severe UNE group showed higher ulnar nerve CSAmax and SWVmax compared with the mild and moderate UNE groups (p < .001). The cutoff values for diagnosis of UNE were 9.5 mm2 for CSAmax, 3.06 m/s for SWVmax, 2.00 for CSA ratio, 1.36 for stiffness ratio, and grade 1 for SMI. Our findings suggest that SWE and SMI are valuable diagnostic tools for the diagnosis and assessment of severity of UNE.

Molecular Markers in Canine Mammary Tumors

Abstract Canine mammary tumors (CMTs) are one of the most common neoplasms in female dogs. Unfortunately, the current diagnosis often occurs in later stages, and there is a pressing need for more comprehensive data on treatment options to improve overall prognosis. Consequently, the early detection of these tumors is critical for improving treatment outcomes and survival rates. As such, biomarkers are essential for improving the diagnosis, treatment, and prognosis of CMT, the reason for which further research is required to enhance our understanding of the disease. The most studied biomarkers for CMT are evaluated from serum and tissue samples using different molecular approaches and relate to proliferation and cell cycle. Several biomarkers were also described regarding cell damage, autophagy and apoptotic-related pathways, hypoxia, angiogenesis, EMT, invasion, metastasis or cancer stem features. Overall, biomarkers have shown the potential to be used as a tool for the early detection of mammary tumors in dogs. However, more research is needed to validate these biomarkers and to develop sensitive and specific diagnostic tests. In this regard, we aimed to review known biomarkers and their role in CMT comprehensively. We also encouraged further investigations of reliable biomarkers that could improve treatment outcomes and survival rates for dogs with this disease.

Women’s insights on sexual health after breast cancer (WISH-BREAST).

12132 Background: Sexual health is a prevalent and distressing toxicity in women after cancer treatment. Sexual health needs and treatment require multidisciplinary management and despite the existence of guidelines on the management of sexual health, are often overlooked in the clinical setting. Methods: An anonymous online survey regarding sexual health for people with a current or prior breast cancer diagnosis was distributed through a social media platform (Instagram) and e-mail. Questions included demographics, breast cancer history and treatment, sexual health symptoms, and experience with medical care for sexual health symptoms. Results: Out of 1775 respondents, 1462 answered the question of whether or not sexual health changed after breast cancer diagnosis or treatment &amp; were included in this analysis. Mean age: 47.7 years. 87.3% of respondents identified as white and 1.6% as Black. Stage: 0 (8.5%), 1 (38.4%), 2 (34.2%), 3 (14.8%) &amp; 4 (2.9%). Estrogen receptor positive (81.5%), HER2 positive (27%), triple negative (12.9%). Breast cancer diagnosis or treatments caused a moderate to great deal of change to sexual health in 89.5% of respondents and a moderate to great deal of distress in 85%. The sexual health concerns are summarized in the table. 73% of participants did not receive information about sexual health from their healthcare team. Among participants who reported a change to sexual health, only 12% were satisfied with how their sexual health concerns were addressed. Treatment was not offered for sexual health concerns to 39% of respondents and 63% had not been referred to any specialists for sexual health concerns. 46% of participants had not been offered non-hormonal treatments for genitourinary concerns, 55% were told vaginal hormones were not an option, and 83% were not offered treatment for low libido despite lower sexual desire. 80% of respondents received information about sexual health from social media. Of those, 62% stated that their primary source of information on social media were health care professional accounts. Conclusions: In this online survey, sexual health concerns were highly prevalent and distressing after breast cancer diagnosis. There was a very low level of discussion with medical professionals and treatments were infrequently offered to patients, despite the existence of medical guidelines on sexual health care for women after cancer. Patients are turning to social media, &amp; specifically to medical professional accounts, for information. Future efforts to address sexual needs for breast cancer survivors and thrivers are warranted. [Table: see text]

Financial toxicity screening preferences in patients with breast cancer.

e23132 Background: Financial toxicity is an important issue for cancer patients that affects quality of life and treatment adherence. Screening can help identify patients at risk but consensus on appropriate timing or methods for screening is lacking. We conducted a survey to assess preferences for financial toxicity screening among a cohort of cancer patients. Methods: An anonymous cross-sectional survey was sent to email newsletter subscribers of a breast cancer-specific philanthropic organization with a nationwide reach in July 2023. Participants were asked about financial toxicity screening preferences; all questions were optional. Frequencies, percentages, means, and standard deviations were calculated for categorical and continuous variables, respectively. Comparative analyses, including chi-square and t-tests, were used to identify differences across demographic groups. Statistical significance was determined at p &lt; 0.05. Results: Of 5774 potential participants who clicked on the email link, 825 respondents with confirmed prior or current cancer diagnosis participated in the survey (14.3% response rate). These participants were 84% female (n = 690) with a median age of 50 years (IQR 44-57), were 51% non-Hispanic White (n = 418), 18% Black or African-American (n = 149), 8.2% Hispanic (n = 68). 84% confirmed a breast cancer (n = 689), and 53% were currently undergoing treatment (n = 441). Most participants were not asked about financial stressors by their care team (52%, n = 429) and most did not receive assistance from their care team (61%, n = 501). Most preferred for their providers to reach out to them regarding financial needs (75%, n = 615). Most wished for these discussions to take place early on in their treatment course (when first diagnosed [40%, n = 334] or when treatment course was selected [33%, n = 275]), and for these questions to be asked frequently (each appointment [38%, n = 312] or once per month [32%, n = 268]). Participants felt most comfortable discussing financial needs with a social worker or patient/financial navigator (82%, n = 679). Most wished to discuss financial concerns in person (67%, n = 553) or via telephone (58%, n = 479). Conclusions: Patients with cancer desire financial screening to occur early, often, and to be initiated by their providers. Patient preferences can guide optimal implementation of financial toxicity screening practices. Continued work refining best practices for financial toxicity screening should incorporate these patient preferences.

Evaluation of a web-based, interactive guide to enhance patient-clinician electronic communication.

e13659 Background: Secure messaging (SM) enables patients to ask clinicians questions asynchronously. Benefits include enhancing patient engagement, contributing to oral chemotherapy adherence, and lowering symptom distress. However, patients are unsure how to address concerns effectively, and clinicians find it challenging to decipher patients’ needs within messages. Coupled with clinicians not having adequate time to respond to messages, breakdowns in communication are common. We developed an interactive guide to help patients with cancer craft effective secure messages. It featured an oncologist providing strategies to write messages with specificity, expressing concerns, stating needs, being direct, and included a downloadable tip sheet. Methods: Patients 18+ with a current cancer diagnosis who were enrolled in the portal at a large health center were recruited. Between October 2023 and January 2024, participants were sent a link to interact with the guide, followed by a survey. The survey included several measures: 1) Communication and Attitudinal Self-Efficacy scale for cancer (CASE-cancer); 2) Digital Behavior Change Intervention (DBCI) Engagement Scale; 3) Website User Satisfaction questionnaire (WUS); 4) Acceptability of Intervention Measure (AIM); 5) Intervention Appropriateness Measure (IAM). We used R version 4.3.2 for the descriptive analysis. The Wilcoxon rank-sum test compared groups by demographic categories. The local IRB approved this study. Results: One hundred patients participated. Median age was 64 years (range=21-86), 54% male, 83% white, 35% diagnosed with prostate cancer, 35% lung cancer, and 23% hematologic cancer. Respondents reported high levels of self-efficacy based on CASE-cancer (mean 43.3 of 48). Respondents averaged 15 minutes using the guide. 96% thought the guide’s content was presented clearly, 95% found it easy to understand, 90% enjoyed using it, 89% thought the guide was suitable, and 88% could rely on its information. Results by demographic categories found that non-white respondents (M = 43.0) were significantly more engaged than white respondents (M = 28.4), P = 0.038 , and those with a bachelor’s degree+ (M = 96.5) had higher satisfaction than those without (M = 91.6), P = 0.025. Conclusions: Patients with cancer embraced an interactive guide that provided strategies to craft secure messages. They found it engaging, satisfying, acceptable, and appropriate, with racially minoritized patients being most engaged. As SM is a widely used tool for patient-clinician communication, it is crucial to concentrate on the way messages are constructed. We found potential to improve patients’ message-writing techniques, which could result in clinicians writing more complete answers to questions. Additionally, there is the possibility to enhance underserved patients’ communication skills and make them more engaged in their care.