A shared decision-making intervention between health care professionals and individuals undergoing Pulmonary Rehabilitation: An iterative development process with qualitative methods.

Background: Pulmonary Rehabilitation (PR) services typically offer programmes to support individuals living with COPD make rehabilitation choices that best meet their needs, however, uptake remains low. Shared Decision-Making (SDM; e.g.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare.

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System.

Development and Validation of a Natural Language Processing Model to Identify Low-Risk Pulmonary Embolism in Real Time to Facilitate Safe Outpatient Management.

Study Objective: This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management.

Methods: Data were curated from all patients who received a PE-protocol computed tomography pulmonary angiogram (PE-CTPA) imaging study in the ED of a 3-hospital academic health system between June 1, 2018 and December 31, 2020 (n=12,183).

Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.

Objectives: Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.

Methods: Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

Study Objective: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models.

Methods: Data were curated from 468,167 adult patient encounters in 3 EDs (1 academic and 2 community-based EDs) of a large academic health system from August 1, 2015, to October 31, 2018.

Preliminary results of a clinical research and innovation scholarship to prepare medical students to lead innovations in health care.

There is consensus amongst national organizations to integrate health innovation and augmented intelligence (AI) into medical education. However, there is scant evidence to guide policymakers and medical educators working to revise curricula. This study presents academic, operational, and domain understanding outcomes for the first three cohorts of participants in a clinical research and innovation scholarship program.

Impact of diagnosis code grouping method on clinical prediction model performance: A multi-site retrospective observational study.

Objective: The primary purpose of this work is to systematically assess the performance trade-offs on clinical prediction tasks of four diagnosis code groupings: AHRQ-Elixhauser, Single-level CCS, truncated ICD-9-CM codes, and raw ICD-9-CM codes.

Materials And Methods: We used two distinct datasets from different geographic regions and patient populations and train models for three prediction tasks: 1-year mortality following an ICU stay, 30-day mortality following surgery, and 30-day complication following surgery. We run multiple commonly-used binary classification models including penalized logistic regression, random forest, and gradient boosted trees.

Predicting Hospital Utilization and Inpatient Mortality of Patients Tested for COVID-19.

Using structured elements from Electronic Health Records (EHR), we seek to: ) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, and ) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission.

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.

Background: Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.

Methods: In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years.

Machine learning for early detection of sepsis: an internal and temporal validation study.

Objective: Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

Materials And Methods: We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center.

Correction: Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.

Background: Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature.

Objective: This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care.

Methods: In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis.

Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.

Importance: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable. Similarly, few have been implemented, and none have been evaluated prospectively and externally validated.

Assessing Quality of Surgical Real-World Data from an Automated Electronic Health Record Pipeline.

Background: Significant analysis errors can be caused by nonvalidated data quality of electronic health records data. To determine surgical data fitness, a framework of foundational and study-specific data analyses was adapted and assessed using conformance, completeness, and plausibility analyses.

Study Design: Electronic health records-derived data from a cohort of 241,695 patients undergoing 412,182 procedures from October 1, 2014 to August 31, 2018 at 3 hospital sites was evaluated.

Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities.

Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care.

A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies.

Acute respiratory infections (ARIs) are the leading indication for antibacterial prescriptions despite a viral etiology in the majority of cases. The lack of available diagnostics to discriminate viral and bacterial etiologies contributes to this discordance. Recent efforts have focused on the host response as a source for novel diagnostic targets although none have explored the ability of host-derived microRNAs (miRNA) to discriminate between these etiologies.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

Objectives: To find and validate generalizable sepsis subtypes using data-driven clustering.

Design: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).

Setting: Retrospective analysis.

A community approach to mortality prediction in sepsis via gene expression analysis.

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients.

Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network's efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.

Development of an objective gene expression panel as an alternative to self-reported symptom scores in human influenza challenge trials.

Background: Influenza challenge trials are important for vaccine efficacy testing. Currently, disease severity is determined by self-reported scores to a list of symptoms which can be highly subjective. A more objective measure would allow for improved data analysis.

Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection.

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression.

A Genomic Signature of Influenza Infection Shows Potential for Presymptomatic Detection, Guiding Early Therapy, and Monitoring Clinical Responses.

Early, presymptomatic intervention with oseltamivir (corresponding to the onset of a published host-based genomic signature of influenza infection) resulted in decreased overall influenza symptoms (aggregate symptom scores of 23.5 vs 46.3), more rapid resolution of clinical disease (20 hours earlier), reduced viral shedding (total median tissue culture infectious dose [TCID50] 7.

Host gene expression classifiers diagnose acute respiratory illness etiology.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use.