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Objective 17-α-hydroxyprogesterone caproate (17-OHP) has been recommended by professional societies for the prevention of recurrent preterm birth, but subsequent clinical studies have reported conflicting efficacy results. This study aimed to contribute to the evidence base regarding the effectiveness of 17-OHP in clinical practice using real-world data. Study Design A total of 4,422 individuals meeting inclusion criteria representing recurrent spontaneous preterm birth (sPTB) were identified in a database of insurance claims, and 568 (12.8%) received 17-OHP. Crude and propensity score-matched recurrence rates and risk ratios (RRs) for the association of receiving 17-OHP on recurrent sPTB were calculated. Results Raw sPTB recurrence rates were higher among those treated versus not treated; after propensity score matching, no association was detected (26.3 vs. 23.8%, RR = 1.1, 95% CI: 0.9–1.4). Conclusion We failed to identify a beneficial effect of 17-OHP for the prevention of spontaneous recurrent preterm birth in our observational, U.S. based cohort. Key Points We observed higher risk for sPTB in the group receiving 17-OHP in the unmatched analysis After propensity-score matching, we still failed to identify a beneficial effect of 17-OHP on sPTB Sensitivity analyses demonstrated robustness to the inclusion criteria and modeling assumptions. [ABSTRACT FROM AUTHOR]

To describe regional differences in utilization of 17α-hydroxyprogesterone caproate (17-OHP). Retrospective cohort study of a large, US commercial managed care plan claims database with pharmacy coverage from 2008 to 2018. Singleton pregnancies with at least one prior spontaneous preterm birth (sPTB) were included. Regional and state-based differences in 17-OHP use were compared. Data were analyzed using t-tests and Fisher's exact tests. Of the 4,514 individuals with an indication for 17-OHP, 580 (12.8%) were prescribed 17-OHP. Regional and state-based differences in 17-OHP utilization were identified; Northeast 15.7%, Midwest 13.7%, South 12.0%, and West 10.4% (p=0.003). While significant regional differences in 17-OHP utilization were demonstrated, 17-OHP utilization remained low despite this cohort having insurance through a US commercial managed care plan. Suboptimal utilization demonstrates a disconnect between research and uptake in clinical practice. This underscores a need for implementation science in obstetrics to translate updated recommendations more effectively and efficiently into clinical practice. [ABSTRACT FROM AUTHOR]

Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take. We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC). Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician's shoulders—using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary. [ABSTRACT FROM AUTHOR]

The article focuses on the potential and challenges of using artificial intelligence (AI) in medicine. Topics include the wide range of AI applications in healthcare, the need for diversity in authorship and editorial control to address potential biases; and the importance of ethical, governance, and regulatory considerations in the design and implementation of AI systems in medicine to provide a forum for high-quality evidence and resource sharing for medical AI.

Presenting features of inflammatory bowel disease (IBD) are non-specific. We hypothesized that mRNA profiles could (1) identify genes and pathways involved in disease pathogenesis; (2) identify a molecular signature that differentiates IBD from other conditions; (3) provide insight into systemic and colon-specific dysregulation through study of the concordance of the gene expression. Children (8–18 years) were prospectively recruited at the time of diagnostic colonoscopy for possible IBD. We used transcriptome-wide mRNA profiling to study gene expression in colon biopsies and paired whole blood samples. Using blood mRNA measurements, we fit a regression model for disease state prediction that was validated in an independent test set of adult subjects (GSE3365). Ninety-eight children were recruited [39 Crohn's disease, 18 ulcerative colitis, 2 IBDU, 39 non-IBD]. There were 1,118 significantly differentially (IBD vs non-IBD) expressed genes in colon tissue, and 880 in blood. The direction of relative change in expression was concordant for 106/112 genes differentially expressed in both tissue types. The regression model from the blood mRNA measurements distinguished IBD vs non-IBD disease status in the independent test set with 80% accuracy using only 6 genes. The overlap of 5 immune and metabolic pathways in the two tissue types was significant (p<0.001). Blood and colon tissue from patients with IBD share a common transcriptional profile dominated by immune and metabolic pathways. Our results suggest that peripheral blood expression levels of as few as 6 genes (IL7R, UBB, TXNIP, S100A8, ALAS2, and SLC2A3) may distinguish patients with IBD from non-IBD. [ABSTRACT FROM AUTHOR]

Infants with autism spectrum disorder (ASD) were recently found to have prolonged auditory brainstem response (ABR); however, at older ages, findings are contradictory. We compared ABR differences between participants with ASD and controls with respect to age using a meta‐analysis. Data sources included MEDLINE, EMBASE, Web of Science, Google Scholar, HOLLIS, and ScienceDirect from their inception to June 2016. The 25 studies that were included had a total of 1349 participants (727 participants with ASD and 622 controls) and an age range of 0–40 years. Prolongation of the absolute latency of wave V in ASD had a significant negative correlation with age (R2 = 0.23; P = 0.01). The 22 studies below age 18 years showed a significantly prolonged wave V in ASD (Standard Mean Difference = 0.6 [95% CI, 0.5–0.8]; P < 0.001). The 3 studies above 18 years of age showed a significantly shorter wave V in ASD (SMD = −0.6 [95% CI, −1.0 to −0.2]; P = 0.004). Prolonged ABR was consistent in infants and children with ASD, suggesting it can serve as an ASD biomarker at infancy. As the ABR is routinely used to screen infants for hearing impairment, the opportunity for replication studies is extensive. Autism Res2018, 11: 355–363. © 2017 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. Lay Summary: Our analysis of previous studies showed that infants and children with autism spectrum disorder (ASD) have a slower brain response to sound, while adults have a faster brain response to sound. This suggests that slower brain response in infants may predict ASD risk. Brain response to sound is routinely tested on newborns to screen hearing impairment, which has created large data sets to afford replication of these results. [ABSTRACT FROM AUTHOR]

Background: Best practice for statistical methodology in cell-based dose-response studies has yet to be established. We examine the ability of MANOVA to detect trait-associated genetic loci in the presence of gene-gene interactions. We present a novel Bayesian nonparametric method designed to detect such interactions. Results: MANOVA and the Bayesian nonparametric approach show good ability to detect trait-associated genetic variants under various possible genetic models. It is shown through several sets of analyses that this may be due to marginal effects being present, even if the underlying genetic model does not explicitly contain them. Conclusions: Understanding how genetic interactions affect drug response continues to be a critical goal. MANOVA and the novel Bayesian framework present a trade-off between computational complexity and model flexibility. [ABSTRACT FROM AUTHOR]

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10−37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

Background: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. Results: A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions: The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets. [ABSTRACT FROM AUTHOR]

An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC50) and efficacy (Emax) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data. [ABSTRACT FROM AUTHOR]

Primary human hepatocyte cultures are useful in vitro model systems of human liver because when cultured under appropriate conditions the hepatocytes retain liver-like functionality such as metabolism, transport, and cell signaling. This model system was used to characterize the concentration- and time-response of the 320 ToxCast chemicals for changes in expression of genes regulated by nuclear receptors. Fourteen gene targets were monitored in quantitative nuclease protection assays: six representative cytochromes P-450, four hepatic transporters, three Phase II conjugating enzymes, and one endogenous metabolism gene involved in cholesterol synthesis. These gene targets are sentinels of five major signaling pathways: AhR, CAR, PXR, FXR, and PPARα. Besides gene expression, the relative potency and efficacy for these chemicals to modulate cellular health and enzymatic activity were assessed. Results demonstrated that the culture system was an effective model of chemical-induced responses by prototypical inducers such as phenobarbital and rifampicin. Gene expression results identified various ToxCast chemicals that were potent or efficacious inducers of one or more of the 14 genes, and by inference the 5 nuclear receptor signaling pathways. Significant relative risk associations with rodent in vivo chronic toxicity effects are reported for the five major receptor pathways. These gene expression data are being incorporated into the larger ToxCast predictive modeling effort. [ABSTRACT FROM AUTHOR]

Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the quality of a model's uncertainty is evaluated using point-prediction metrics, such as the negative log-likelihood (NLL), expected calibration error (ECE) or the Brier score on held-out data. Marginal coverage of prediction intervals or sets, a well-known concept in the statistical literature, is an intuitive alternative to these metrics but has yet to be systematically studied for many popular uncertainty quantification techniques for deep learning models. With marginal coverage and the complementary notion of the width of a prediction interval, downstream users of deployed machine learning models can better understand uncertainty quantification both on a global dataset level and on a per-sample basis. In this study, we provide the first large-scale evaluation of the empirical frequentist coverage properties of well-known uncertainty quantification techniques on a suite of regression and classification tasks. We find that, in general, some methods do achieve desirable coverage properties on in distribution samples, but that coverage is not maintained on out-of-distribution data. Our results demonstrate the failings of current uncertainty quantification techniques as dataset shift increases and reinforce coverage as an important metric in developing models for real-world applications. [ABSTRACT FROM AUTHOR]

This Viewpoint reviews how machine learning models are developed and trained, discusses the challenges of reproducing medical machine learning model accuracy, and proposes practices and standards to improve the models' reproducibility and replicability. [ABSTRACT FROM AUTHOR]

In recent years, the volume and complexity of electronic patient-generated health data has increased exponentially leading to unprecedented opportunities for automated extraction of clinical features from free-text medical note and speech. Natural language processing (NLP), a branch of computer science that focuses on enabling computers to process human language, can process this rich resource of data for clinical and research purposes. In this narrative review, we provide the fundamentals of NLP and discuss the potentials and challenges in the spinal community. This lays a foundation for the reader to better understand and appreciate the role that NLP will play in the evolving field of artificial intelligence. [ABSTRACT FROM AUTHOR]

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we discuss how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches. [ABSTRACT FROM AUTHOR]

This Viewpoint discusses how newer technologies such as machine learning and the compilation of “big data” can be used for research and clinical applications. [ABSTRACT FROM AUTHOR]

The author reflects on the use of artificial intelligence in the medical sector as of December 2016. Also cited are the history of artificial intelligence, including the development of artificial neural network algorithms in the 1940s and 1950s, a study on the use of deep learning for the detection of diabetic retinopathy and macular edema, and the workhorse model in deep learning called the convolutional neural network.

Objectives: Our objective was to compare rates of hospitalizations for respiratory illnesses in preterm and full-term (FT) children for 4 years before and after the 2014 update to the American Academy of Pediatrics (AAP) respiratory syncytial virus (RSV) immunoprophylaxis guidance, which restricted eligibility among infants born at 29 to 34 weeks in the first winter and all preterm infants in the second winter after neonatal discharge., Study Design: We conducted pre-post and interrupted time series analyses on claims data from a commercial national managed care plan. We compared the number of RSV and all respiratory hospital admissions in the first and second RSV seasons after neonatal discharge among a cohort of preterm children, regardless of palivizumab status, in the 4 years before and after the implementation of the 2014 palivizumab eligibility change. A FT group was included for reference., Results: The cohort included 821 early preterm (EP, <29 weeks), 4,790 moderate preterm (MP, 29-34 weeks), and 130,782 FT children. Palivizumab use after the policy update decreased among MP children in the first and second RSV seasons after neonatal discharge, without any change in the odds of hospitalization with RSV or respiratory illness. For the EP group, there was no change in the rate of palivizumab or the odds of hospitalization with RSV or respiratory illness after the policy update. For the FT group, there was a slight decrease in odds of hospitalization post-2014 after the policy update. The interrupted time series did not reveal any secular trends over time in hospitalization rates among preterm children. Following the policy change, there were cost savings for MP children in the first and second RSV seasons, when accounting for the cost of hospitalizations and the cost of palivizumab., Conclusion: Hospitalizations for RSV or respiratory illness did not increase, and cost savings were obtained after the implementation of the 2014 AAP palivizumab prophylaxis policy., Key Points: · Palivizumab use decreased among children born moderate preterm (29 to34 weeks) after the 2014 palivizuamb policy update.. · There was no change in odds of hospitalization with respiratory syncitial virus or respiratory illness among preterm infants after the policy update when compared to before.. · There were cost savings, when accounting for the cost of hospitalizations and the cost of palivizumab, after the policy update among children born moderate preterm.., Competing Interests: None declared., (Thieme. All rights reserved.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the funding bodies listed above for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. GSC is a National Institute for Health and Care Research (NIHR) senior investigator, the director of the UK EQUATOR Centre, editor-in-chief of BMC Diagnostic and Prognostic Research, and a statistics editor for The BMJ. KGMM is director of Health Innovation Netherlands and editor-in-chief of BMC Diagnostic and Prognostic Research. RDR is an NIHR senior investigator, a statistics editor for The BMJ, and receives royalties from textbooks Prognosis Research in Healthcare and Individual Participant Data Meta-Analysis. AKD is an NIHR senior investigator. EWL is the head of research at The BMJ. BG is a part time employee of HeartFlow and Kheiron Medical Technologies and holds stock options with both as part of the standard compensation package. SR receives royalties from Springer for the textbooks Targeted Learning: Causal Inference for Observational and Experimental Data and Targeted Learning: Causal Inference for Complex Longitudinal Studies. JCC receives honorariums as a current lay member on the UK NICE covid-19 expert panel and a citizen partner on the COVID-END Covid-19 Evidence Network to support decision making; was a lay member on the UK NIHR AI AWARD panel in 2020-22 and is a current lay member on the UK NHS England AAC Accelerated Access Collaborative NHS AI Laboratory Evaluation Advisory Group; is a patient fellow of the European Patients’ Academy on Therapeutic Innovation and a EURORDIS rare disease alumni; reports grants from the UK National Institute for Health and Care Research, European Commission, UK Cell Gene Catapult, University College London, and University of East Anglia; reports patient speaker fees from MEDABLE, Reuters Pharma events, Patients as Partners Europe, and EIT Health Scandinavia; reports consultancy fees from Roche Global, GlaxoSmithKline, the FutureScience Group and Springer Healthcare (scientific publishing), outside of the scope of the present work; and is a strategic board member of the UK Medical Research Council IASB Advanced Pain Discovery Platform initiative, Plymouth Institute of Health, and EU project Digipredict Edge AI-deployed Digital Twins for covid-19 Cardiovascular Disease. ALB is a paid consultant for Generate Biomedicines, Flagship Pioneering, Porter Health, FL97, Tessera, FL85; has an equity stake in Generate Biomedicines; and receives research funding support from GlaxoSmithKline, National Heart, Lung, and Blood Institute, and National Institute of Diabetes and Digestive and Kidney Diseases. No other conflicts of interests with this specific work are declared.

Introduction: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques., Methods and Analysis: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation., Ethics and Dissemination: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications., Prospero Registration Number: CRD42019140361 and CRD42019161764., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare., (©2020 AMIA - All rights reserved.)

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings.

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding models. These embeddings allow for comparison between clinical concepts or for straightforward input to machine learning models. Using traditional approaches, good representations require high dimensionality, making downstream tasks such as visualization more difficult. We applied Poincaré embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space. We then examine disease relationships under different disease contexts to better understand potential phenotypes.