betAS: intuitive analysis and visualization of differential alternative splicing using beta distributions.

Next-generation RNA sequencing allows alternative splicing (AS) quantification with unprecedented resolution, with the relative inclusion of an alternative sequence in transcripts being commonly quantified by the proportion of reads supporting it as percent spliced-in (PSI). However, PSI values do not incorporate information about precision, proportional to the respective AS events' read coverage. Beta distributions are suitable to quantify inclusion levels of alternative sequences, using reads supporting their inclusion and exclusion as surrogates for the two distribution shape parameters. Each such beta distribution has the PSI as its mean value and is narrower when the read coverage is higher, facilitating the interpretability of its precision when plotted. We herein introduce a computational pipeline, based on beta distributions accurately modeling PSI values and their precision, to quantitatively and visually compare AS between groups of samples. Our methodology includes a differential splicing significance metric that compromises the magnitude of intergroup differences, the estimation uncertainty in individual samples, and the intragroup variability, being therefore suitable for multiple-group comparisons. To make our approach accessible and clear to both noncomputational and computational biologists, we developed betAS, an interactive web app and user-friendly R package for visual and intuitive differential splicing analysis from read count data.

Advancing Pharmacogenomics from Single-Gene to Preemptive Testing.

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

A genome sequencing system for universal newborn screening, diagnosis, and precision medicine for severe genetic diseases.

Newborn screening (NBS) dramatically improves outcomes in severe childhood disorders by treatment before symptom onset. In many genetic diseases, however, outcomes remain poor because NBS has lagged behind drug development. Rapid whole-genome sequencing (rWGS) is attractive for comprehensive NBS because it concomitantly examines almost all genetic diseases and is gaining acceptance for genetic disease diagnosis in ill newborns. We describe prototypic methods for scalable, parentally consented, feedback-informed NBS and diagnosis of genetic diseases by rWGS and virtual, acute management guidance (NBS-rWGS). Using established criteria and the Delphi method, we reviewed 457 genetic diseases for NBS-rWGS, retaining 388 (85%) with effective treatments. Simulated NBS-rWGS in 454,707 UK Biobank subjects with 29,865 pathogenic or likely pathogenic variants associated with 388 disorders had a true negative rate (specificity) of 99.7% following root cause analysis. In 2,208 critically ill children with suspected genetic disorders and 2,168 of their parents, simulated NBS-rWGS for 388 disorders identified 104 (87%) of 119 diagnoses previously made by rWGS and 15 findings not previously reported (NBS-rWGS negative predictive value 99.6%, true positive rate [sensitivity] 88.8%). Retrospective NBS-rWGS diagnosed 15 children with disorders that had been undetected by conventional NBS. In 43 of the 104 children, had NBS-rWGS-based interventions been started on day of life 5, the Delphi consensus was that symptoms could have been avoided completely in seven critically ill children, mostly in 21, and partially in 13. We invite groups worldwide to refine these NBS-rWGS conditions and join us to prospectively examine clinical utility and cost effectiveness.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Technology in the management of diabetes in hospitalised adults.

Suboptimal glycaemic management in hospitals has been associated with adverse clinical outcomes and increased financial costs to healthcare systems. Despite the availability of guidelines for inpatient glycaemic management, implementation remains challenging because of the increasing workload of clinical staff and rising prevalence of diabetes. The development of novel and innovative technologies that support the clinical workflow and address the unmet need for effective and safe inpatient diabetes care delivery is still needed. There is robust evidence that the use of diabetes technology such as continuous glucose monitoring and closed-loop insulin delivery can improve glycaemic management in outpatient settings; however, relatively little is known of its potential benefits and application in inpatient diabetes management. Emerging data from clinical studies show that diabetes technologies such as integrated clinical decision support systems can potentially mediate safer and more efficient inpatient diabetes care, while continuous glucose sensors and closed-loop systems show early promise in improving inpatient glycaemic management. This review aims to provide an overview of current evidence related to diabetes technology use in non-critical care adult inpatient settings. We highlight existing barriers that may hinder or delay implementation, as well as strategies and opportunities to facilitate the clinical readiness of inpatient diabetes technology in the future.

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Shared Decision-Making and Cardiovascular Health: A Scientific Statement From the American Heart Association.

Shared decision-making is increasingly embraced in health care and recommended in cardiovascular guidelines. Patient involvement in health care decisions, patient-clinician communication, and models of patient-centered care are critical to improve health outcomes and to promote equity, but formal models and evaluation in cardiovascular care are nascent. Shared decision-making promotes equity by involving clinicians and patients, sharing the best available evidence, and recognizing the needs, values, and experiences of individuals and their families when faced with the task of making decisions. Broad endorsement of shared decision-making as a critical component of high-quality, value-based care has raised our awareness, although uptake in clinical practice remains suboptimal for a range of patient, clinician, and system issues. Strategies effective in promoting shared decision-making include educating clinicians on communication techniques, engaging multidisciplinary medical teams, incorporating trained decision coaches, and using tools (ie, patient decision aids) at appropriate literacy and numeracy levels to support patients in their cardiovascular decisions. This scientific statement shines a light on the limited but growing body of evidence of the impact of shared decision-making on cardiovascular outcomes and the potential of shared decision-making as a driver of health equity so that everyone has just opportunities. Multilevel solutions must align to address challenges in policies and reimbursement, system-level leadership and infrastructure, clinician training, access to decision aids, and patient engagement to fully support patients and clinicians to engage in the shared decision-making process and to drive equity and improvement in cardiovascular outcomes.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. METHODS: This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. RESULTS: We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. CONCLUSION: Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Canadians' use of cannabis for therapeutic purposes since legalization of recreational cannabis: a cross-sectional analysis by medical authorization status.

BACKGROUND: There has been a precipitous decline in authorizations for medical cannabis since non-medical cannabis was legalized in Canada in 2018. This study examines the demographic and health- and medical cannabis-related factors associated with authorization as well as the differences in medical cannabis use, side effects, and sources of medical cannabis and information by authorization status. METHODS: Individuals who were taking cannabis for therapeutic purposes completed an online survey in early 2022. Multivariable logistic regression was used to determine odds ratios (OR) and 95% confidence intervals (CI) of demographic and health- and medical cannabis-related variables associated with holding medical cannabis authorization. The differences in medical cannabis use, side effects, and sources of information by authorization status were determined via t-tests and chi-squared analysis. RESULTS: A total of 5433 individuals who were currently taking cannabis for therapeutic purposes completed the study, of which 2941 (54.1%) currently held medical authorization. Individuals with authorization were more likely to be older (OR ≥ 70 years vs. < 30 years, 4.85 (95% CI, 3.49-6.76)), identify as a man (OR man vs. woman, 1.53 (1.34-1.74)), have a higher income (OR > $100,000/year vs. < $50,000 year, 1.55 (1.30-1.84)), and less likely to live in a small town (OR small town/rural vs. large city, 0.69 (0.59-0.81)). They were significantly more likely to report not experiencing any side effects (29.9% vs. 23.4%; p < 0.001), knowing the amount of cannabis they were taking (32.1% vs. 17.7%; p < 0.001), obtaining cannabis from regulated sources (74.1% vs. 47.5%; p < 0.001), and seeking information about medical cannabis from healthcare professionals (67.8% vs. 48.2%; p < 0.01) than individuals without authorization. CONCLUSIONS: These findings offer insight into the possible issues regarding equitable access to medical cannabis and how authorization may support and influence individuals in a jurisdiction where recreational cannabis is legalized, highlighting the value of a formal medical cannabis authorization process.

Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow.

The unprecedented demand for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) testing led to challenges in prioritizing and processing specimens efficiently. We describe and evaluate a novel workflow using provider- and patient-facing ask at order entry (AOE) questions to generate distinctive icons on specimen labels for within-laboratory clinical decision support (CDS) for specimen triaging. A multidisciplinary committee established target turnaround times (TATs) for SARS-CoV-2 nucleic acid amplification test (NAAT) based on common clinical scenarios. A set of AOE questions was used to collect relevant clinical information that prompted icon generation for triaging SARS-CoV-2 NAAT specimens. We assessed the collect-to-verify TATs among relevant clinical scenarios. Our study included a total of 1,385,813 SARS-CoV-2 NAAT conducted from March 2020 to June 2022. Most testing met the TAT targets established by institutional committees, but deviations from target TATs occurred during periods of high demand and supply shortages. Median TATs for emergency department (ED) and inpatient specimens and ambulatory pre-procedure populations were stable over the pandemic. However, healthcare worker and other ambulatory test TATs varied substantially, depending on testing volume and community transmission rates. Median TAT significantly differed throughout the pandemic for ED and inpatient clinical scenarios, and there were significant differences in TAT among label icon-signified ambulatory clinical scenarios. We describe a novel approach to CDS for triaging specimens within the laboratory. The use of CDS tools could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes. IMPORTANCE We describe a novel approach to clinical decision support (CDS) for triaging specimens within the clinical laboratory for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) nucleic acid amplification tests (NAAT). The use of our CDS tool could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. There were significant differences in the turnaround time for specimens differentiated by icons on specimen labels. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Documentation of results and medication prescribing after combinatorial psychiatric pharmacogenetic testing: A case for discrete results.

PURPOSE: Combinatorial pharmacogenetic (PGx) panels intended to aid psychiatric prescribing are available to clinicians. Here, we evaluated the documentation of PGx panel results and subsequent prescribing patterns within a tertiary health care system. METHODS: We performed a query of psychiatry service note text in our electronic health record using 71 predefined PGx terms. Patients who underwent combinatorial PGx testing were identified, and documentation of test results was analyzed. Prescription data following testing were examined for the frequency of prescriptions influenced by genes on the panel along with the medical specialties involved. RESULTS: A total of 341 patients received combinatorial PGx testing, and documentation of results was found to be absent or incomplete for 198 patients (58%). The predominant method of documentation was through portable document formats uploaded to the electronic health record's "Media" section. Among patients with at least 1 year of follow-up, a large majority (194/228, 85%) received orders for medications affected by the tested genes, including 132 of 228 (58%) patients receiving at least 1 non-psychiatric medication influenced by the test results. CONCLUSION: Results from combinatorial PGx testing were poorly documented. Medications affected by these results were often prescribed after testing, highlighting the need for discrete results and clinical decision support.

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance.

There has been a steady rise in the use of clinical decision support (CDS) tools to guide Nephrology, as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down three sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution's CDS governance group, we show how health system-based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity.

Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.

OBJECTIVES: A rapidly expanding number of prediction models is being developed aiming to improve rheumatoid arthritis (RA) diagnosis and treatment. However, few are actually implemented in clinical practice. This study explores factors influencing the acceptance of prediction models in clinical decision-making by RA patients. METHODS: A qualitative study design was used with thematic analysis of semi-structured interviews. Purposive sampling was applied to capture a complete overview of influencing factors. The interview topic list was based on pilot data. RESULTS: Data saturation was reached after 12 interviews. Patients were generally positive about the use of prediction models in clinical decision-making. Six key themes were identified from the interviews. First, patients have the need for information on prediction models. Second, factors influencing trust in model-supported treatment are described. Third, patients envision the model to have a supportive role in clinical decision-making. Fourth, patients hope to personally benefit from model-supported treatment in various ways. Fifth, patients are willing to contribute time and effort to contribute to model input. And lastly, we discuss the theme on effects of the relationship with the caregiver in model-supported treatment. CONCLUSION: Within this study RA patients were generally positive about the use of prediction models in their treatment given some conditions were met and concerns addressed. The results of this study can be used during the development and implementation in RA care of prediction models in order to enhance patient acceptability.

ChatGPT: a reliable fertility decision-making tool?

The internet is the primary source of infertility-related information for most people who are experiencing fertility issues. Although no longer shrouded in stigma, the privacy of interacting only with a computer provides a sense of safety when engaging with sensitive content and allows for diverse and geographically dispersed communities to connect and share their experiences. It also provides businesses with a virtual marketplace for their products. The introduction of ChatGPT, a conversational language model developed by OpenAI to understand and generate human-like text in response to user input, in November 2022, and other emerging generative artificial intelligence (AI) language models, has changed and will continue to change the way we interact with large volumes of digital information. When it comes to its application in health information seeking, specifically in relation to fertility in this case, is ChatGPT a friend or foe in helping people make well-informed decisions? Furthermore, if deemed useful, how can we ensure this technology supports fertility-related decision-making? After conducting a study into the quality of the information provided by ChatGPT to people seeking information on fertility, we explore the potential benefits and pitfalls of using generative AI as a tool to support decision-making.

Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?

Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.

Eggsurance? A randomized controlled trial of a decision aid for elective egg freezing.

STUDY QUESTION: Does a purpose-designed Decision Aid for women considering elective egg freezing (EEF) impact decisional conflict and other decision-related outcomes? SUMMARY ANSWER: The Decision Aid reduces decisional conflict, prepares women for decision-making, and does not cause distress. WHAT IS ALREADY KNOWN: Elective egg-freezing decisions are complex, with 78% of women reporting high decisional conflict. Decision Aids are used to support complex health decisions. We developed an online Decision Aid for women considering EEF and demonstrated that it was acceptable and useful in Phase 1 testing. STUDY DESIGN, SIZE, DURATION: A single-blind, two-arm parallel group randomized controlled trial was carried out. Target sample size was 286 participants. Randomization was 1:1 to the control (existing website information) or intervention (Decision Aid plus existing website information) group and stratified by Australian state/territory and prior IVF specialist consultation. Participants were recruited between September 2020 and March 2021 with outcomes recorded over 12 months. Data were collected using online surveys and data collection was completed in March 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: Females aged ≥18 years, living in Australia, considering EEF, proficient in English, and with internet access were recruited using multiple methods including social media posts, Google advertising, newsletter/noticeboard posts, and fertility clinic promotion. After completing the baseline survey, participants were emailed their allocated website link(s). Follow-up surveys were sent at 6 and 12 months. Primary outcome was decisional conflict (Decisional Conflict Scale). Other outcomes included distress (Depression Anxiety and Stress Scale), knowledge about egg freezing and female age-related infertility (study-specific measure), whether a decision was made, preparedness to decide about egg freezing (Preparation for Decision-Making Scale), informed choice (Multi-Dimensional Measure of Informed Choice), and decision regret (Decision Regret Scale). MAIN RESULTS AND THE ROLE OF CHANCE: Overall, 306 participants (mean age 30 years; SD: 5.2) were randomized (intervention n = 150, control n = 156). Decisional Conflict Scale scores were significantly lower at 12 months (mean score difference: -6.99 [95% CI: -12.96, -1.02], P = 0.022) for the intervention versus control group after adjusting for baseline decisional conflict. At 6 months, the intervention group felt significantly more prepared to decide about EEF than the control (mean score difference: 9.22 [95% CI: 2.35, 16.08], P = 0.009). At 12 months, no group differences were observed in distress (mean score difference: 0.61 [95% CI: -3.72, 4.93], P = 0.783), knowledge (mean score difference: 0.23 [95% CI: -0.21, 0.66], P = 0.309), or whether a decision was made (relative risk: 1.21 [95% CI: 0.90, 1.64], P = 0.212). No group differences were found in informed choice (relative risk: 1.00 [95% CI: 0.81, 1.25], P = 0.983) or decision regret (median score difference: -5.00 [95% CI: -15.30, 5.30], P = 0.337) amongst participants who had decided about EEF by 12 months (intervention n = 48, control n = 45). LIMITATIONS, REASONS FOR CAUTION: Unknown participant uptake and potential sampling bias due to the recruitment methods used and restrictions caused by the coronavirus disease 2019 pandemic. Some outcomes had small sample sizes limiting the inferences made. The use of study-specific or adapted validated measures may impact the reliability of some results. WIDER IMPLICATIONS OF THE FINDINGS: This is the first randomized controlled trial to evaluate a Decision Aid for EEF. The Decision Aid reduced decisional conflict and improved women's preparation for decision making. The tool will be made publicly available and can be tailored for international use. STUDY FUNDING/COMPETING INTEREST(S): The Decision Aid was developed with funding from the Royal Women's Hospital Foundation and McBain Family Trust. The study was funded by a National Health and Medical Research Council (NHMRC) Project Grant APP1163202, awarded to M. Hickey, M. Peate, R.J. Norman, and R. Hart (2019-2021). S.S., M.P., D.K., and S.B. were supported by the NHMRC Project Grant APP1163202 to perform this work. R.H. is Medical Director of Fertility Specialists of Western Australia and National Medical Director of City Fertility. He has received grants from MSD, Merck-Serono, and Ferring Pharmaceuticals unrelated to this study and is a shareholder of CHA-SMG. R.L. is Director of Women's Health Melbourne (Medical Practice), ANZSREI Executive Secretary (Honorary), RANZCOG CREI Subspecialty Committee Member (Honorary), and a Fertility Specialist at Life Fertility Clinic Melbourne and Royal Women's Hospital Public Fertility Service. R.A.A. has received grants from Ferring Pharmaceuticals unrelated to this study. M.H., K.H., and R.J.N. have no conflicts to declare. TRIAL REGISTRATION NUMBER: ACTRN12620001032943. TRIAL REGISTRATION DATE: 11 August 2020. DATE OF FIRST PATIENT'S ENROLMENT: 29 September 2020.

Tackling Guideline Non-concordance: Primary Care Barriers to Incorporating Life Expectancy into Lung Cancer Screening Decision-Making-A Qualitative Study.

BACKGROUND: Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS. OBJECTIVE: We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection. DESIGN: Qualitative study based on semi-structured telephone interviews. PARTICIPANTS: Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities. APPROACH: We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations. KEY RESULTS: PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows. CONCLUSIONS: Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS.

Clinical data-based modeling of IVF live birth outcome and its application.

BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods. METHODS: The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications. RESULTS: Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO. CONCLUSIONS: The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.