Mental health risks in children with food allergies: a population-based comparison of food allergies with other chronic conditions.

Unlabelled: Children with chronic diseases face a higher risk of mental health disorders. However, the cognitive consequences of food allergies (FA), which are not classified as a chronic disease, remain insufficiently researched. The objective of this study is to examine the association between FA and mental health in children and adolescents, compared to children and adolescents without FA (controls) and those with other chronic conditions but no history of FA.

SNOMED CT entity linking challenge.

Objective: This paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology.

Materials And Methods: As a basis for this work, a large set of 74 808 annotations was curated across 272 discharge notes spanning 6624 unique clinical concepts. Submissions were evaluated using the mean Intersection-over-Union metric, evaluated at the character level with the 3 best performing solutions awarded a cash prize.

Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics.

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining-external.

Characteristics and Outcomes of Over a Million Patients with Inflammatory Bowel Disease in Seven Countries: Multinational Cohort Study and Open Data Resource.

Background And Aims: Observational healthcare data are an important tool for delineating patients' inflammatory bowel disease (IBD) journey in real-world settings. However, studies that characterize IBD cohorts typically rely on a single resource, apply diverse eligibility criteria, and extract variable sets of attributes, making comparison between cohorts challenging. We aim to longitudinally describe and compare IBD patient cohorts across multiple geographic regions, employing unified data and analysis framework.

Non-osteoporotic fractures are associated with increased risk of subsequent major osteoporotic fractures.

Unlabelled: We studied the association between non-osteoporotic fractures and future major osteoporotic fractures, using UK health records. Non-osteoporotic fractures were found to increase the risk of major osteoporotic fractures, although to a lesser extent than osteoporotic fractures. This highlights the importance of considering all previous fractures in assessing future fracture risk.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

Background: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

Estimating the effect of cesarean delivery on long-term childhood health across two countries.

Assessing the impact of cesarean delivery (CD) on long-term childhood outcomes is challenging as conducting a randomized controlled trial is rarely feasible and inferring it from observational data may be confounded. Utilizing data from electronic health records of 737,904 births, we defined and emulated a target trial to estimate the effect of CD on predefined long-term pediatric outcomes. Causal effects were estimated using pooled logistic regression and standardized survival curves, leveraging data breadth to account for potential confounders.

Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.

Purpose: Supplementing investigator-specified variables with large numbers of empirically identified features that collectively serve as 'proxies' for unspecified or unmeasured factors can often improve confounding control in studies utilizing administrative healthcare databases. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment in pharmacoepidemiologic research. In this paper, we survey current approaches and recent advancements for high-dimensional proxy confounder adjustment in healthcare database studies.

ISPE-Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID-19: Opportunities and Trade-Offs.

As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19.

Results of an early second PCR test performed on SARS-CoV-2 positive patients may support risk assessment for severe COVID-19.

Identifying patients at increased risk for severe COVID-19 is of high priority during the pandemic as it could affect clinical management and shape public health guidelines. In this study we assessed whether a second PCR test conducted 2-7 days after a SARS-CoV-2 positive test could identify patients at risk for severe illness. Analysis of a nationwide electronic health records data of 1683 SARS-CoV-2 positive individuals indicated that a second negative PCR test result was associated with lower risk for severe illness compared to a positive result.

Personalized approach to childhood obesity: Lessons from gut microbiota and omics studies. Narrative review and insights from the 29th European childhood obesity congress.

The traditional approach to childhood obesity prevention and treatment should fit most patients, but misdiagnosis and treatment failure could be observed in some cases that lie away from average as part of individual variation or misclassification. Here, we reflect on the contributions that high-throughput technologies such as next-generation sequencing, mass spectrometry-based metabolomics and microbiome analysis make towards a personalized medicine approach to childhood obesity. We hypothesize that diagnosing a child as someone with obesity captures only part of the phenotype; and that metabolomics, genomics, transcriptomics and analyses of the gut microbiome, could add precision to the term "obese," providing novel corresponding biomarkers.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines.

Framework for identifying drug repurposing candidates from observational healthcare data.

Objective: Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs.

What Factors Increase the Risk of Complications in SARS-CoV-2-Infected Patients? A Cohort Study in a Nationwide Israeli Health Organization.

Background: Reliably identifying patients at increased risk for coronavirus disease (COVID-19) complications could guide clinical decisions, public health policies, and preparedness efforts. Multiple studies have attempted to characterize at-risk patients, using various data sources and methodologies. Most of these studies, however, explored condition-specific patient cohorts (eg, hospitalized patients) or had limited access to patients' medical history, thus, investigating related questions and, potentially, obtaining biased results.

Inferring new relations between medical entities using literature curated term co-occurrences.

Objectives: Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied.

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study.

Objective: Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records.

Study Design And Setting: Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders.

Changes in vaginal community state types reflect major shifts in the microbiome.

: Recent studies of various human microbiome habitats have revealed thousands of bacterial species and the existence of large variation in communities of microorganisms in the same habitats across individual human subjects. Previous efforts to summarize this diversity, notably in the human gut and vagina, have categorized microbiome profiles by clustering them into community state types (CSTs). The functional relevance of specific CSTs has not been established.

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Background: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system.

Methods: Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system.

Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to β-lactam Antibiotics.

Mycobacterium tuberculosis (M. tuberculosis) is considered innately resistant to β-lactam antibiotics. However, there is evidence that susceptibility to β-lactam antibiotics in combination with β-lactamase inhibitors is variable among clinical isolates, and these may present therapeutic options for drug-resistant cases.

The intron-22-inverted F8 locus permits factor VIII synthesis: explanation for low inhibitor risk and a role for pharmacogenomics.

Intron-22-inversion patients express the entire Factor VIII (FVIII)-amino-acid sequence intracellularly as 2 non-secreted polypeptides and have a positive "intracellular (I)-FVIII-CRM" status. Mutations conferring a positive I-FVIII-CRM status are associated with low inhibitor risk and are pharmacogenetically relevant because inhibitor risk may be affected by the nature of the therapeutic FVIII-protein (tFVIII), the affinity of any tFVIII-derived foreign peptide (tFVIII-fp) for any HLA class-II isomer (HLA-II) comprising individual major histocompatibility complex (MHC) repertoires, and the stability of any tFVIII-fp/HLA-II complex. We hypothesize that mutations conferring a completely or substantially negative I-FVIII-CRM status are pharmacogenetically irrelevant because inhibitor risk is high with any tFVIII and individual MHC repertoire.

Redundancy-weighting for better inference of protein structural features.

Motivation: Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. These gaps in care may lead to increased mental health disease burden and relapse, as well as repeated incarcerations. Further, the complex health, social service, and criminal justice ecosystem within which the patient may be embedded makes it difficult to examine the role of modifiable risk factors and delivered services on patient outcomes, particularly given that agencies often maintain isolated sets of relevant data.

Building better drugs: developing and regulating engineered therapeutic proteins.

Most native proteins do not make optimal drugs and thus a second- and third-generation of therapeutic proteins, which have been engineered to improve product attributes or to enhance process characteristics, are rapidly becoming the norm. There has been unprecedented progress, during the past decade, in the development of platform technologies that further these ends. Although the advantages of engineered therapeutic proteins are considerable, the alterations can affect the safety and efficacy of the drugs.