Prediction of successful labor induction in persons with a low Bishop score using machine learning: Secondary analysis of two randomized controlled trials.

BACKGROUND: The objective of this paper was to identify predictors of a vaginal birth in individuals with singleton pregnancies and a Bishop Score <4, following Induction of Labor (IoL) using dinoprostone vaginal insert (DVI). Secondarily, we sought to understand the association between oxytocin use for labor augmentation and IoL outcomes. METHODS: We developed and internally validated a multivariate prediction model using machine learning (ML) applied to data from two Phase-III randomized controlled double-blind trials (NCT01127581, NCT00308711). The model was internally validated using 10-fold cross-validation. RESULTS: This study included 1107 participants. Despite unfavorable cervical status and inclusion of high-risk pregnancies, 72% of participants had vaginal births. The model's area under receiver operating characteristic curve was 0.73. The following factors increased the chance of vaginal birth: being parous; being between 37 and 41 weeks of gestation; having a lower Body Mass Index; having a lower maternal age; having fewer maternal comorbidities; and having a higher Bishop score. Parity alone correctly predicted the outcome in ~50% of cases, at a ~10% false-negative rate. Participants whose labors progressed without requiring oxytocin had a higher probability of vaginal birth than those requiring oxytocin for either induction or augmentation (81% vs 70% vs 77%, respectively). DISCUSSION: Even in high-risk pregnancies and with low Bishop scores, the use of DVI results in a high chance of vaginal birth. Parity is a critical predictor of success. The judicious use of oxytocin for labor induction or augmentation can increase the chance of vaginal birth. Our study validates the use of ML and predictive modeling for treatment response prediction when considering IoL.

ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients.

BACKGROUND: Improved multiple sclerosis (MS) diagnosis and increased availability of intravenous disease-modifying treatments can lead to overburdening of infusion centres. This study was focused on developing a decision-support tool to help infusion centres plan their operations. METHODS: A discrete event simulation model ('ENTIMOS') was developed using Simul8 software in collaboration with clinical experts. Model inputs included treatment-specific clinical parameters, resources such as infusion chairs and nursing staff, and costs, while model outputs included patient throughput, waiting time, queue size, resource utilisation, and costs. The model was parameterised using characteristics of the Charing Cross Hospital Infusion Centre in London, UK, where 12 infusion chairs were deployed for 170 non-MS and 860 MS patients as of March 2021. The number of MS patients was projected to increase by seven new patients per week. RESULTS: The model-estimated waiting time for an infusion is, on average, 8 days beyond clinical recommendation in the first year of simulation. Without corrective action, the delay in receiving due treatment is anticipated to reach 30 days on average at 30 months from the start of simulation. Such system compromise can be prevented either by adding one infusion chair annually or switching 7% of existing patients or 24% of new patients to alternative MS treatments not requiring infusion. CONCLUSION: ENTIMOS is a flexible model of patient flow and care delivery in infusion centres serving MS patients. It allows users to simulate specific local settings and therefore identify measures that are necessary to avoid clinically significant treatment delay resulting in suboptimal care.

Secukinumab's effect on structural damage progression in psoriatic arthritis: longitudinal mixture modelling of FUTURE-1 and FUTURE-5.

OBJECTIVES: Peripheral and axial manifestations of psoriatic arthritis (PsA) can lead to irreversible structural damage and chronic disability. Our objective was to explore predictors of radiographic progression and to increase our understanding of treatment effects in subgroups of patients with different rates of structural damage progression. METHODS: We analysed data from two large Phase-3 trials of secukinumab in PsA patients, FUTURE-1 (NCT01392326, n=606) and FUTURE-5 (NCT02404350, n=996), where different posologies ranging from 75 mg to 300 mg were used. We applied a longitudinal Bayesian mixture model with random effects to account for the variability in the repeated radiographic assessments. "Fast progressors" were defined post hoc as patients with a 50% model-estimated probability to progress at least 0.5 mTSS/year faster than an average patient. RESULTS: Higher baseline inflammation and higher body weight were identified as significant predictors of radiographic progression (multivariate model). Model-estimated structural damage progression in an average patient treated with secukinumab 150 mg subcutaneous (s.c.) was slower (0.04 mTSS/year; 95% CI -0.28, 0.34) compared to a patient treated with placebo (0.94 mTSS/year; 95% CI 0.45, 1.45). According to the model, the subgroup of "fast progressors" (hsCRP ≥26 mg/L, body weigth ≥94 kg, inadequate response to prior anti-TNF-alpha, structural damage ≥42 mTSS) treated with secukinumab 150 mg s.c. progressed at 0.56 mTSS/year (95% CI 0.02, 1.09) and 1.46 mTSS/year (95% CI 0.81, 2.11) when treated with placebo. CONCLUSIONS: Greater systemic inflammation and higher body weight at baseline were identified as significant predictors of progression. Even patients with fast radiographic progression could experience a beneficial effect with secukinumab that holds promise to prevent further mobility loss.

Artificial Intelligence and Machine Learning Applied at the Point of Care.

INTRODUCTION: The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. OBJECTIVE: Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. METHODS: A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. RESULTS: From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. CONCLUSIONS: The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.

Health Outcome Prioritization in Alzheimer's Disease: Understanding the Ethical Landscape.

BACKGROUND: Dementia has been described as the greatest global health challenge in the 21st Century on account of longevity gains increasing its incidence, escalating health and social care pressures. These pressures highlight ethical, social, and political challenges about healthcare resource allocation, what health improvements matter to patients, and how they are measured. This study highlights the complexity of the ethical landscape, relating particularly to the balances that need to be struck when allocating resources; when measuring and prioritizing outcomes; and when individual preferences are sought. OBJECTIVE: Health outcome prioritization is the ranking in order of desirability or importance of a set of disease-related objectives and their associated cost or risk. We analyze the complex ethical landscape in which this takes place in the most common dementia, Alzheimer's disease. METHODS: Narrative review of literature published since 2007, incorporating snowball sampling where necessary. We identified, thematized, and discussed key issues of ethical salience. RESULTS: Eight areas of ethical salience for outcome prioritization emerged: 1) Public health and distributive justice, 2) Scarcity of resources, 3) Heterogeneity and changing circumstances, 4) Knowledge of treatment, 5) Values and circumstances, 6) Conflicting priorities, 7) Communication, autonomy and caregiver issues, and 8) Disclosure of risk. CONCLUSION: These areas highlight the difficult balance to be struck when allocating resources, when measuring and prioritizing outcomes, and when individual preferences are sought. We conclude by reflecting on how tools in social sciences and ethics can help address challenges posed by resource allocation, measuring and prioritizing outcomes, and eliciting stakeholder preferences.

Ethical and Social Implications of Using Predictive Modeling for Alzheimer's Disease Prevention: A Systematic Literature Review.

BACKGROUND: The therapeutic paradigm in Alzheimer's disease (AD) is shifting from symptoms management toward prevention goals. Secondary prevention requires the identification of individuals without clinical symptoms, yet "at-risk" of developing AD dementia in the future, and thus, the use of predictive modeling. OBJECTIVE: The objective of this study was to review the ethical concerns and social implications generated by this new approach. METHODS: We conducted a systematic literature review in Medline, Embase, PsycInfo, and Scopus, and complemented it with a gray literature search between March and July 2018. Then we analyzed data qualitatively using a thematic analysis technique. RESULTS: We identified thirty-one ethical issues and social concerns corresponding to eight ethical principles: (i) respect for autonomy, (ii) beneficence, (iii) non-maleficence, (iv) equality, justice, and diversity, (v) identity and stigma, (vi) privacy, (vii) accountability, transparency, and professionalism, and (viii) uncertainty avoidance. Much of the literature sees the discovery of disease-modifying treatment as a necessary and sufficient condition to justify AD risk assessment, overlooking future challenges in providing equitable access to it, establishing long-term treatment outcomes and social consequences of this approach, e.g., medicalization. The ethical/social issues associated specifically with predictive models, such as the adequate predictive power and reliability, infrastructural requirements, data privacy, potential for personalized medicine in AD, and limiting access to future AD treatment based on risk stratification, were covered scarcely. CONCLUSION: The ethical discussion needs to advance to reflect recent scientific developments and guide clinical practice now and in the future, so that necessary safeguards are implemented for large-scale AD secondary prevention.

Ethical and social implications of using predictive modeling for Alzheimer's disease prevention: a systematic literature review protocol.

INTRODUCTION: The therapeutic paradigm in Alzheimer's disease (AD) has shifted towards secondary prevention, defined as an intervention aiming to prevent or delay disease onset in pre-symptomatic individuals at risk of developing dementia due to AD. The key feature of AD prevention is the need to treat years or even decades before the onset of cognitive, behavioural or functional decline. Prediction of AD risk and evaluation of long-term treatment outcomes in this setting requires predictive modelling and is associated with ethical concerns and social implications. The objective of this review is to identify and elucidate them, as presented in the literature. METHODS AND ANALYSIS: A systematic literature review was conducted in Medline, Embase, PsycInfo and Scopus, and was complemented with a grey literature search. All searches were conducted between March and July 2018. Two reviewers independently assessed each study for inclusion and disagreements were adjudicated by a third reviewer. Data are now being extracted using an extraction sheet developed within the group of reviewers, based on an initial sample of three manuscripts, but allowing for inclusion of newly identified data items (ethical arguments). Data will be analysed qualitatively using a thematic analysis technique. Potential biases in selection and interpretation of extracted data are mitigated by the fact that reviewers come from a range of different scientific backgrounds and represent different types of stakeholders in this ethical discussion (academia, industry, patient advocacy groups). ETHICS AND DISSEMINATION: The study does not require ethical approval. The findings of the review will be disseminated in a peer-reviewed journal and presented at conferences. They will also be reported through the Innovative Medicine Initiative project: Real World Outcomes Across the AD Spectrum for Better Care: Multi-modal Data Access Platform (IMI: ROADMAP). TRIAL REGISTRATION NUMBER: CRD42018092205.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project.

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.