Healthcare Network Research Articles

Communication as a Transmitter of Trust in Healthcare Network During Pandemic

Communication as a Transmitter of Trust in Healthcare Network During Pandemic

Risk of Malignancy in Incidentally Detected Lung Nodules in Patients Aged Younger Than 35 Years.

The risk of malignancy in pulmonary nodules incidentally detected on computed tomography (CT) in patients who are aged younger than 35 years is unclear. The aim of this study was to evaluate the incidence of lung cancer in incidental pulmonary nodules in patients who are 15-34 years old. This retrospective study included patients aged 15-34 years who had an incidental pulmonary nodule on chest CT from 2010 to 2018 at our hospital. Patients with prior, current, or suspected malignancy were excluded. A chart review identified patients with diagnosis of malignancy. Incidental pulmonary nodule was deemed benign if stable or resolved on a follow-up CT at least 2 years after initial or if there was a medical visit in our health care network at least 2 years after initial CT without diagnosis of malignancy.Receiver operating characteristic curve analysis was performed with nodule size. Association of categorical variables with lung cancer diagnosis was performed with Fisher exact test, and association of continuous variables was performed with logistic regression. Five thousand three hundred fifty-five chest CTs performed on patients aged 15-34 years between January 2010 and December 2018. After excluding patients without a reported pulmonary nodule and prior or current malignancy, there were a total of 779 patients. Of these, 690 (89%) had clinical or imaging follow-up after initial imaging. Of these, 545 (70% of total patients) patients had imaging or clinical follow-up greater than 2 years after their initial imaging.A malignant diagnosis was established in 2/779 patients (0.3%; 95% confidence interval, 0.1%-0.9%). Nodule size was strongly associated with malignancy ( P = 0.007), with area under the receiver operating characteristic curve of 0.97. There were no malignant nodules that were less than 10 mm in size. Smoking history, number of nodules, and nodule density were not associated with malignancy. Risk of malignancy for incidentally detected pulmonary nodules in patients aged 15-34 years is extremely small (0.3%). There were no malignant nodules that were less than 10 mm in size. Routine follow-up of subcentimeter pulmonary nodules should be carefully weighed against the risks.

Implementing Formal Mitral Heart Team Improves Multidisciplinary Evaluation Rate and Survival of Patients With Severe Primary Mitral Regurgitation.

Multidisciplinary heart team (HT) evaluation is recommended for patients with severe primary mitral regurgitation to optimize treatment decisions. However, its impact on patient outcomes remains unknown. We evaluated the impact of implementing mitral HT on patient survival. We conducted a retrospective cohort study of patients with new diagnoses of severe primary mitral regurgitation in a large healthcare network echocardiogram database between 2016 and 2020. We compared the incidence of multidisciplinary evaluation by structural cardiology and cardiac surgery services and 2-year survival before and after mitral HT implementation. The 1:1 propensity-score matching between pre- and post-mitral HT used Society of Thoracic Surgeons Predicted Risk of Mortality for mitral repair, age, sex, race, heart failure symptoms, inpatient setting, history of MI, and dementia as covariates. Logistic regression identified variables associated with the likelihood of undergoing multidisciplinary evaluation. Among 70 510 echocardiograms performed, 391 patients had severe primary mitral regurgitation (median age, 77 years; 46% women). Multidisciplinary evaluation increased from 29% to 89% (P<0.001), and intervention increased from 24% to 75% following mitral HT implementation (P<0.001). Among 180 propensity-score matched patients, mortality was lower post-mitral HT at 2 years (19% versus 32%, P=0.04). The multivariable model showed that mitral HT implementation and heart failure symptoms were associated with higher odds of undergoing multidisciplinary evaluation (OR [odds ratio], 18.7 and 2.72, respectively), whereas female sex and older age were associated with lower odds (OR, 0.39 and 0.93, respectively). Implementation of mitral HT was associated with drastic improvement in multidisciplinary evaluation for patients with severe primary mitral regurgitation. This coincided with higher proportions of patients undergoing mechanical correction of MR and improved overall patient survival.

Integrated Community Care Delivered by Public Health-Care and Social-Care Systems: Results from a Realist Synthesis.

Integrated community care (ICC) is defined as an interweaving of health-care and social-care interventions deployed in spatial and relational proximity using an interdisciplinary and cross-sectoral approach. Consideration of territory scale and time scale are at the center of ICC practices. Its deployment in public health and social care networks (HSCN) can be complex due to their broad mandate, the complexity of their management, and accountability. Therefore, we aimed to describe ICC delivered by public HSCN to determine how, why, for whom, and in what circumstances ICC works and produces outcomes. A realist synthesis was conducted consisting of five steps consistent with realist synthesis standards (RAMESES projects) to produce configurations of Context - Mechanism - Outcomes (CMOc) and development of a middle-range explanatory theory of why and how the identified outcomes may have occurred. In total, 26 studies were selected and used, as evidence, to support-either partially or fully-the production of CMOc based on the initial program theory. Nine unique CMO configurations were identified based on the data analyses and team discussion. ICC middle-range theory is informed by the CMO configurations identified. This realist synthesis allowed us to identify the central mechanisms of ICC delivered by public HSCN and to produce a middle range theory. ICC is based on a specific philosophy and deployed by a professional agency oriented toward a community agency within a local system of interdisciplinary and cross-sectoral action. Our middle-range theory will provide a solid analytical framework as a foundation for ICC implementation and future research.

Enhanced decision-making in healthcare cloud-edge networks using deep reinforcement and lion optimization algorithm

Addressing the limitations of traffic-centric approaches in cooperative cloud-edge networks, this paper introduces an adaptive deployment strategy for FDNN using LOA. However, the oversight of network structure hampers load-balancing efficiency in medical data categorization. To overcome this, a novel DRL-LOA approach integrates Deep Reinforcement Learning (DRL) with LOA, considering both network structure and traffic-related data. This DRL-LOA considers both network structure and traffic-related data for optimization tasks. The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. In light of this choice, the FDNN classifier is deployed to categorize the medical information. In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% % decrease in latency on 20 edge nodes in cloud-edge systems. These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks.

Mental health and collective health: an epidemiological analysis of suicides in Brazil

To analyze the temporal behavior of suicides. Cross-sectional, ecological study on suicide registered in the mortality information system. Kernel analysis was used in Quantum Geographic Information System (QGIS), TerraView and the Chi-Square test in R software. In twenty years there were 195,046 suicides, with a mortality rate of 4.94, mainly in the southeast, among males with significant growth, low education, single, aged 20-29, white and by hanging. In the kernel map, the south, southeast and northeast regions concentrate the largest clusters. In part of the South and in the interior of the Southeast there was a reduction in mortality. This study provides information about the worrying scenario of suicide in Brazil over twenty years, identifying the areas and characteristics with the greatest risks. To reduce this suicide scenario, effective public actions and a quality health care network that provides comprehensive, equitable and universal assistance to the mental health of individuals are essential, creating conditions to meet the needs of the social body.

NURSING CARE FOR USERS WITH SICKLE CELL ANEMIA: AN INTEGRATIVE REVIEW

Objective: To present the evidence available in the literature on nursing care for users with sickle cell anemia. Methodology: Integrative, qualitative review, with database research. 15 articles published between 2015-2022 were considered. The descriptors were used: “Sickle cell anemia”, “Nursing Care”, “Nursing” and “Hemotherapy Services”. Results: Two thematic categories were found: Nursing care for students with sickle cell anemia; Nursing care for users with sickle cell anemia in the Health Care Network services. Final considerations: Care for this population stands out, with actions that begin from the newborn stage and are also aimed at the black population, as well such as using approaches beyond conventional ones, such as pain relief strategies and helping people deal with a health problem that will accompany them throughout their lives.

Representation of the hierarchical and functional structure of an ambulatory network of medical consultations through Social Network Analysis, with an emphasis on the role of medical specialties.

Ambulatory Health Care Networks (Amb-HCN) are circuits of patient referral and counter-referral that emerge, explicitly or spontaneously, between doctors who provide care in their offices. Finding a meaningful analytical representation for the organic and hierarchical functioning of an Amb-HCN may have managerial and health policymaking implications. We aimed to characterize the structural and functional topology of an Amb-HCN of a private health insurance provider (PHIP) using objective metrics from graph theory. This is a cross-sectional quantitative study with a secondary data analysis study design. A Social Network Analysis (SNA) was conducted using office visits performed between April 1, 2021 and May 15, 2022, retrieved from secondary administrative claim databases from a PHIP in Belo Horizonte, Southeastern Brazil. Included were beneficiaries of a healthcare plan not restricting the location or physician caring for the patient. A directional and weighted network was constructed, where doctors were the vertices and patient referrals between doctors, within 7-45 days, were the network edges. Vertex-level SNA measures were calculated and grouped into three theoretical constructs: patient follow-up (aimed at assessing the doctor's pattern of patient follow-up); relationship with authorities (which assessed whether the doctor is an authority or contributes to his or her colleague's authority status); and centrality (aimed at positioning the doctor relative to the network graph). To characterize physician profiles within each dimension based on SNA metrics results, a K-means cluster analysis was conducted. The resulting physician clusters were assigned labels that sought to be representative of the observed values of the vertex metrics within the clusters. Overall, 666,263 individuals performed 3,863,222 office visits with 4,554 physicians. A total of 577 physicians (12.7%) had very low consultation productivity and contributed very little to the network (i.e., about 1.1% of all referrals made or received), being excluded from subsequent doctor profiles analysis. Cluster analysis found 951 (23.9%) doctors to be central in the graph and 1,258 (31.6%) to be peripheral; 883 (22.2%) to be authorities and 266 (6.7%) as seeking authorities; 3,684 (92.6%) mostly shared patients with colleagues, with patient follow-up intensities ranging from weak to strong. Wide profile dispersion was observed among specialties and, more interestingly, within specialties. Non-primary-care medical specialties (e.g., cardiology, endocrinology etc.) were associated with central profile in the graph, while surgical specialties predominated in the periphery, along with pediatrics. Only pediatrics was associated with strong and prevalent (i.e., low patient sharing pattern) follow-up. Many doctors from internal medicine and family medicine had unexpectedly weak and shared patient follow-up profiles. Doctor profiles exhibited pairwise relationships with each other and with the number of chronic comorbidities of the patients they treated. For example, physicians identified as authorities were frequently central and treated patients with more comorbidities. Ten medical communities were identified with clear territorial and specialty segregation. Viewing the Amb-HCN as a social network provided a topological and functional representation with potentially meaningful and actionable emerging insights into the most influential actors and specialties, functional hierarchies, factors that lead to self-constituted medical communities, and dispersion from expected patterns within medical specialties.

Evaluation of the telemedical health care network “SAFE BIRTH” for pregnant women at risk, premature and sick newborns and their families: study protocol of a cluster-randomized controlled stepped-wedge trial

BackgroundThe Perinatal Center of the University Hospital Carl Gustav Carus Dresden has initiated the telemedical healthcare network “SAFE BIRTH” to coordinate and improve specialized care in non-metropolitan regions for pregnant women and newborns. The network incorporates five intervention bundles (IB): (1) Multi-professional, inter-disciplinary prenatal care plan; (2) Neonatal resuscitation; (3) Neonatal antibiotic stewardship; (4) Inter-facility transfer of premature and sick newborns; (5) Psycho-social support for parents. We evaluate if the network improves care close to home for pregnant women, premature and sick newborns.MethodsTo evaluate the complex healthcare intervention “SAFE BIRTH” we will conduct a cluster-randomized controlled stepped-wedge trial in five prenatal medical outpatient offices and eight non-metropolitan hospitals in Saxony, Germany. The offices and hospitals will be randomly allocated to five respectively eight sequential steps over a 30-month period to implement the telemedical IB. We define one specific primary process outcome for each IB (for instance IB#1: “Proportion of patients with inclusion criterion IB#1 who have a prenatal care plan and psychosocial counseling within one week”). We estimated a separate multilevel logistic regression model for each primary process outcome using the intervention status as a regressor (control or intervention group). Across all IB, a total of 1,541 and 1,417 pregnant women or newborns need to be included in the intervention and control group, respectively, for a power above 80% for small to medium intervention effects for all five hypothesis tests. Additionally, we will assess job satisfaction and sense of safety of health professionals caring for newborns (questionnaire survey) and we will assess families’ satisfaction, resilience, quality of life and depressive, anxiety and stress symptoms (questionnaire surveys). We will also evaluate the cost-effectiveness of ”SAFE BIRTH” (statutory health insurance routine data, process data) and barriers to its implementation (semi-structured interviews). We use multilevel regression models adjusting for relevant confounders (e.g. socioeconomic status, age, place of residence), as well as descriptive analyses and qualitative content analyses.DiscussionIf the telemedical healthcare network “SAFE BIRTH” proves to be effective and cost-efficient, strategies for its translation into routine care should be developed.Trial registrationGerman clinical trials register. DRKS-ID: DRKS00031482.

Mental health care networks

Mental health care networks

Frequency of SARS-CoV-2 variants identified by real-time PCR in the AUNA healthcare network, Peru.

In Peru, on 11 February 2023, the Ministry of Health registered 4 million patients infected with COVID-19 and around 219,260 deaths. In 2020, the SARS-CoV-2 virus was acquiring mutations that impacted the properties of transmissibility, infectivity, and immune evasion, leading to new lineages. In the present study, the frequency of COVID-19 variants was determined during 2021 and 2022 in patients treated in the AUNA healthcare network. The methodology used to detect mutations and identify variants was the Allplex™ SARS-CoV-2 Variants Assay I, II, and VII kit RT-PCR. The frequency of variants was presented by epidemiological weeks. In total, 544 positive samples were evaluated, where the Delta, Omicron, and Gamma variants were identified. The Delta variant was found in 242 (44.5%) patients between epidemiological weeks 39 and 52 in 2021. In the case of Gamma, it was observed in 8 (1.5%) patients at weeks 39, 41, 43, 45, and 46 of 2021. The Omicron variant was the most frequent with 289 (53.1%) patients during weeks 49 to 52 of 2021 and 1 to 22 of 2022. During weeks 1 through 22 of 2022, it was possible to discriminate between BA. 1 (n = 32) and BA.2 (n = 82). The rapid identification of COVID-19 variants through the RT-PCR methodology contributes to timely epidemiological surveillance, as well as appropriate patient management.

Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments

Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact of their learning environment on their interaction and behavior. Different methods have been used to diagnose ASD in the past, each with their own strengths and weaknesses. Research into ASD diagnostics has largely focused on machine learning algorithms and strategies rather than diagnostic methods. This article discusses many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, and EEG signals. This has led us to conclude that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis. To facilitate and speed up the processing of facial information among children in educational settings, we applied the AlexNet architecture designed for edge computing. A fast method for detecting autism spectrum disorders from the face can be applied to educational settings using this structure. While we have investigated a variety of methods, the face can provide us with appropriate information about the disorder. In addition, it can produce more interpretive features. In order to help students in schools who are suffering from this disease, key factors must be considered: potential clinical and therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, and lack of methodological intervention. The diseases are troublesome, so they should be identified and treated.

Job Satisfaction within the Grassroots Healthcare System in Vietnam's Key Industrial Region-Binh Duong Province: Validating the Vietnamese Version of the Minnesota Satisfaction Questionnaire Scale.

Background: The grassroots healthcare system is the closest and most community-oriented force, working as an extended arm of the primary healthcare network to implement healthcare programs at the household level. Its comprehensive development is a crucial task set by the Vietnamese government. Job satisfaction significantly influences the performance of healthcare staff within this system. Objective: to assess job satisfaction among healthcare staff using the short-form of the Minnesota Satisfaction Questionnaire while also evaluating the Vietnamese translation of this scale. Methods: A descriptive cross-sectional study with analysis based on the responses of 587 healthcare staff using the Vietnamese-translated version of the MSQ short-form scale. The response data from the participants were subjected to CFA, and if the proposed CFA model did not fit the data, EFA was conducted. Results: The results indicate that the new model, which evaluates job-related factors in three distinct groups, is more suitable than the original model. The 14 questions of the MSQ scale were analyzed and categorized into Autonomy, Obligation, and Specificity based on the participants' responses. The confirmatory factor analysis (CFA) conducted on the new model demonstrated favorable fit indices: CFI = 0.934, TLI = 0.917, GFI = 0.919, and RMSEA = 0.093 (90% CI: 0.085-0.102). Conclusions: The Vietnamese version of the MSQ short form demonstrates reliability and validity. It also provides additional data on the effectiveness of the MSQ short form in measuring job satisfaction.

Enhanced Recovery after Surgery (ERAS) Implementation and Barriers among Healthcare Providers in France: A Cross-Sectional Study.

The implementation of Enhanced Recovery After Surgery (ERAS) is a challenge for healthcare systems, especially in case of patients undergoing major surgery. Despite a proven significant reduction in postoperative complications and hospital lengths of stay, ERAS protocols are inconsistently used in real-world practice, and barriers have been poorly described in a cohort comprising medical and paramedical professionals. This study aims to assess the proportion of French healthcare providers who practiced ERAS and to identify barriers to its implementation amongst those surveyed. We conducted a prospective cross-sectional study to survey healthcare providers about their practice of ERAS using an online questionnaire. Healthcare providers were contacted through hospital requests, private hospital group requests, professional corporation requests, social networks, and personal contacts. The questionnaire was also designed to explore barriers to ERAS implementation. Identified barriers were allocated by two independent assessors to one of the fourteen domains of the Theoretical Domains Framework (TDF), which is an integrative framework based on behavior change theories that can be used to identify issues relating to evidence on the implementation of best practice in healthcare settings. One hundred and fifty-three French healthcare providers answered the online questionnaire (76% female, median age 35 years (IQR: 29 to 48)). Physiotherapists, nurses, and dieticians were the most represented professions (31.4%, 24.2%, and, 14.4%, respectively). Amongst those surveyed, thirty-one practiced ERAS (20.3%, 95%CI: 13.9 to 26.63). Major barriers to ERAS practice were related to the "Environmental context and resources" domain (57.6%, 95%CI: 49.5-65.4), e.g., lack of professionals, funding, and coordination, and the "Knowledge" domain (52.8%, 95%CI: 44.7-60.8), e.g., ERAS unawareness. ERAS in major surgery is seldom practiced in France due to the unfavorable environment (i.e., logistics issues, and lack of professionals and funding) and a low rate of procedure awareness. Future studies should focus on devising and assessing strategies (e.g., education and training, collaboration, institutional support, the development of healthcare networks, and leveraging telehealth and technology) to overcome these barriers, thereby promoting the wider implementation of ERAS.

Swedish musculoskeletal researchers view on a collaborative network and future research priorities in Swedish healthcare

AbstractBackgroundMusculoskeletal disorders (MSK) are a global burden causing significant suffering and economic impact. Systematic identification and targeting of research questions of highest interest for stakeholders can aid in improving MSK disorder knowledge and management.ObjectiveTo obtain Swedish MSK researchers' opinions and views on a collaborative Swedish MSK network (SweMSK) and identify future research areas of importance for Swedish MSK research.MethodsA web‐based survey was conducted July to September 2021 to collect data from 354 Swedish MSK researchers. The survey focused on the need, objectives, and structure of a SweMSK network and identified prioritised areas for future MSK research.ResultsThe study included 141 respondents, of which 82 were associate professors or professors. The majority (68%) supported the creation of a new musculoskeletal network. The most supported element was increased collaboration regarding nationwide and multicenter studies. Respondents recommended the creation of a homepage and the establishment of national work groups with different specific interests as the primary elements of a new network.ConclusionThe results demonstrated a need and desire for increased national research collaboration and the creation of a new musculoskeletal network. The high academic experience and active research participation of the respondents suggest the need for MSK disorder knowledge and management improvement in Sweden. Therefore, the SweMSK network may help facilitate effective collaboration and research efforts that can contribute to the advancement of MSK disorder management and care. This study may provide valuable insights for policymakers, clinicians, and researchers to improve MSK disorder care and management in Sweden.

One down, fifty to go: managers' perceptions of their workload and how they cope with it to maintain their psychological health.

Like many other countries, healthcare services in Canada face numerous organizational changes with the main objective of doing more with less. The approach taken within different healthcare networks has brought about a reform in healthcare facilities in Quebec, leading to several mergers and eliminating over 1,000 managerial positions. As a result, this has placed a progressively heavier workload on the shoulders of the remaining managers. Research on mental health in the workplace has mainly focused with the workforce and generally neglects managers. However, studies have shown that workload is a risk factor for managers. Therefore, the objectives of our study are to (1) better understand the elements that make up a manager's workload and the factors that influence it and (2) identify the coping strategies used by managers to deal with their workloads. Employing a qualitative approach, we analyzed 61 semistructured interviews through an abductive method, utilizing diverse frameworks for data analysis. The participants came from the same Quebec healthcare establishment. Our findings align with the notion that workload is a multifaceted phenomenon that warrants a holistic analysis. The workload mapping framework we propose for healthcare network managers enables pinpointing those factors that contribute to the burden of their workload. Ultimately, this workload can detrimentally impact the psychological wellbeing of employees. In conclusion, this study takes a comprehensive look at workload by using a holistic approach, enabling a more comprehensive understanding of this phenomenon. It also allows for the identification of coping strategies used by managers to deal with their workloads. Finally, our results can provide valuable guidance for the interventions aimed at addressing workload issues among healthcare network managers in Quebec by utilizing the specific elements we have identified.

CYBERSECURITY CHALLENGES IN SMART CITIES: A CASE REVIEW OF AFRICAN METROPOLISES

The rapid urbanization and digital transformation of cities across Africa have given rise to the concept of Smart Cities, where advanced technologies are integrated to enhance efficiency, sustainability, and the overall quality of urban life. However, this paradigm shift towards interconnected and technology-driven urban environments brings forth a host of cybersecurity challenges that demand careful consideration. This paper explores the cybersecurity challenges in Smart Cities, focusing on a case review of African metropolises. African cities, emblematic of the global urbanization trend, are embracing Smart City initiatives to address urban challenges and foster economic development. While these initiatives promise improved services and enhanced connectivity, they concurrently expose cities to a myriad of cybersecurity threats. The interconnectedness of devices and systems in Smart Cities creates a vast attack surface, making them susceptible to cyber-attacks ranging from data breaches to infrastructure disruptions. This case review delves into specific instances of cybersecurity challenges faced by African metropolises in their quest for technological advancement. It analyzes the vulnerabilities in critical infrastructure, such as energy grids, transportation systems, and healthcare networks, highlighting the potential risks associated with inadequate cybersecurity measures. Moreover, the paper sheds light on the socio-economic implications of cyber threats in Smart Cities, emphasizing the importance of resilient cybersecurity frameworks in safeguarding citizen data and urban functionality. In conclusion, the paper underscores the urgent need for comprehensive cybersecurity strategies tailored to the unique challenges faced by Smart Cities in Africa. The findings aim to contribute to a better understanding of the intricate relationship between urbanization, technology, and cybersecurity, offering insights that can inform policy decisions, technological implementations, and collaborative efforts to build secure and resilient Smart Cities in the African context.&#x0D; Keywords: Cybersecurity, Smart Cities, Africa, Metropolis, Review.

Comparison of Lung-RADS Version 1.1 and Lung-RADS Version 2022 in Classifying Airway Nodules Detected at Lung Cancer Screening CT.

Purpose To compare the Lung Imaging Reporting and Data System (Lung-RADS) version 1.1 with version 2022 classification of airway nodules detected at lung cancer screening CT examinations. Materials and Methods This retrospective study included all patients who underwent a lung cancer screening CT examination in the authors' health care network between 2015 and 2021 with a reported airway or endobronchial nodule. A fellowship-trained cardiothoracic radiologist reviewed these CT images and characterized the airway nodules by size, location, multiplicity, morphology, dependent portions of airway, internal air, fluid attenuation, distal changes, outcome at follow-up, and final pathologic diagnosis, if malignant. Sensitivity and specificity of Lung-RADS version 1.1 in detecting malignant nodules were compared with those of Lung-RADS version 2022 using the McNemar test. Results A total of 174 patients were included. Of these, 163 (94%) had airway nodules that were deemed benign, while 11 (6%) had malignant nodules. Airway nodules in the trachea and mainstem bronchi were all benign, while lobar and segmental airway nodules had the highest risk for lung cancer (17.2% and 11.1%, respectively). Of the 12 subsegmental airway nodules that were obstructive, three (25%) were malignant and nine (75%) were benign. Nodules with nonobstructive morphologies, dependent portions of airway, internal air, or fluid attenuation were all benign. Only 10 of the 92 (10.9%) patients with positive Lung-RADS by clinical report had cancer. Lung-RADS version 2022 resulted in higher specificity than version 1.1 (82% vs 50%, P < .001), without sacrificing sensitivity (91% for both). Conclusion Compared with the previous version, Lung-RADS version 2022 reduced the number of false-positive screening CT examinations while still identifying malignant airway nodules. Keywords: CT, Lung, Primary Neoplasms, Pulmonary, Lung Cancer Screening, Lung-RADS, Nodule Risk, Airway Nodule, Endobronchial Nodule © RSNA, 2024.

Abstract B078: Artificial-intelligence-driven breast density assessment in the transition from full-field digital mammograms to digital breast tomosynthesis

Abstract Introduction: To enhance reproducibility and robustness in mammographic density assessment, various artificial intelligence (AI) models have been proposed to automatically classify mammographic images into BI-RADS density categories. Despite their promising performances, so far density AI models have been assessed primarily in traditional full-field digital mammography (FFDM) images. Our study aims to assess the potential of AI in breast density assessment in FFDM versus the newer synthetic mammography (SM) images acquired with digital breast tomosynthesis. Methods: We retrospectively analyzed negative (BI-RADS 1 or 2) routine mammographic screening exams (Selenia or Selenia Dimensions; Hologic) acquired at sites within the Barnes-Jewish/Christian (BJC) Healthcare network in St. Louis, MO from 2015 to 2018. BI-RADS breast density assessments of radiologists were obtained from BJC’s mammography reporting software (Magview 7.1). For each mammographic imaging modality, a balanced dataset of 4,000 women was selected so there were equal numbers of women in each of the four BI-RADS density categories, and each woman had at least one mediolateral oblique (MLO) and one craniocaudal (CC) view per breast in that mammographic imaging modality. Previously validated pre-processing steps were applied to all FFDM and SM images to standardize image orientation and intensity. Images were then split into training, validation, and test sets at ratios of 80%, 10%, and 10%, respectively, while maintaining the distribution of breast density categories and ensuring that all images of the same woman appear only in one set. Our AI model was based on the widely used ResNet50 architecture and was designed to accept as an input a mammographic image and predict the BI-RADS breast density category that the image belongs to. Our AI model was optimized, trained, and evaluated separately for each mammographic imaging modality. We report on the AI model’s predictive accuracy on the test set for each mammographic imaging modality, for both views as well as separately for CC and MLO; accuracy differences in FFDM versus SM were assessed via bootstrapping. Results: A batch size of 32, learning rate of e-6, and Adam optimizer were chosen as the optimal hyperparameters for our AI model. Using the same hyperparameters, the AI model demonstrated substantially higher accuracy on the test set for FFDM than for SM (FFDM: accuracy = 71% ± 4.5% versus SM: accuracy = 66% ± 4.2%; p-value&amp;lt;0.001 for comparison). Similar conclusion held when CC and MLO views were evaluated separately (accuracy = 72% ± 4.6% versus 66% ± 4.3% for CC; accuracy = 69% ± 4.5% versus 62% ± 4.3% for MLO; p-value&amp;lt;0.001 for both comparisons). Conclusions: AI performance in BI-RADS breast density assessment was significantly higher on FFDM versus SM, even under the same AI model design, dataset size and training process. Our preliminary findings suggest that further AI optimizations and adaptations may be needed as we translate AI models from FFDM to the newer SM format acquired with digital breast tomosynthesis. Citation Format: Krisha Anant, Juanita Hernandez Lopez, Debbie Bennett, Aimilia Gastounioti. Artificial-intelligence-driven breast density assessment in the transition from full-field digital mammograms to digital breast tomosynthesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B078.

Abstract TP76: Step by Step: Implementing Post-Acute Stroke Standards

Introduction: Certification programs and quality improvement initiatives improve outcomes among patient populations. Stroke rehab expertise, care processes, and educational resources vary among sites where post-acute care (PAC) is delivered. The Misson: Lifeline Stroke PAC Initiatives are recruiting sites in four states to implement the AHA PAC Stroke Program Standards. Purpose Developed from evidence-based rehab and proven secondary prevention interventions, the American Heart Association (AHA) created quality standards based on the AHA 2016 Guidelines for Adult Stroke Rehabilitation and Recovery to address gaps in PAC care. Methods: Nursing facilities (SNFs), critical access hospitals (CAHs) with swing beds, and inpatient rehab facilities (IRFs) in Iowa, Montana, Nebraska, and North Dakota were recruited to participate in the Initiative. Sites received education on the PAC Stroke Program standards and individualized quality improvement support. Sites were enrolled in the Healthcare Network, a collaborative tool to start discussions, share resources, and discuss patient care processes, and the Quality and Certification Tool, used to track clinical measures and outcome metrics. Monthly virtual learning collaboratives were held to share educational materials, community resources, and processes for patient care.Statewide AHA conferences and workshops facilitated in-person collaboration across settings. A pre-implementation survey was utilized to identify gaps in existing practices. A post-implementation survey will be used to assess the effectiveness of these initiatives. Results: Forty-seven facilities were recruited including 10 IRFs, 17 CAHs and 20 SNFs. Additional sites will be added throughout the project. 100% of facilities completed orientation. Nine learning collaboratives have been hosted and 26 facilities completed the pre-implementation survey. Data submissions and review are ongoing through 2025. Conclusions: Future evaluation of implementation of standards is needed to assess improvements in patient outcomes. The participation of 47 facilities shows a need for more organized initiatives that help health care professionals incorporate research proven standards for stroke rehab.