Improved diagnosis and management of sepsis and bloodstream infection

Sepsis is a severe organ dysfunction triggered by infections, and a leading cause of hospitalization and death. Concurrent bloodstream infection (BSI) is common and around one third of sepsis patients have positive blood cultures. Prompt diagnosis and treatment is crucial, but there is a trade-off between the negative effects of over diagnosis and failure to recognize sepsis in time. The emerging crisis of antimicrobial resistance has made bacterial infections more difficult to treat, especially gram-negative pathogens such as Pseudomonas aeruginosa. The overall aim with this thesis was to improve diagnosis, assess the influence of time to antimicrobial treatment and explore prognostic bacterial virulence markers in sepsis and BSI. The papers are based on observational data from 7 cohorts of more than 100 000 hospital episodes. In addition, whole genome sequencing has been performed on approximately 800 invasive P. aeruginosa isolates collected from centers in Europe and Australia. Paper I showed that automated surveillance of sepsis incidence using the Sepsis-3 criteria is feasible in the non-ICU setting, with examples of how implementing this model generates continuous epidemiological data down to the ward level. This information can be used for directing resources and evaluating quality-of-care interventions. In Paper II, evidence is provided for using peripheral oxygen saturation (SpO2) to diagnose respiratory dysfunction in sepsis, proposing the novel thresholds 94% and 90% to get 1 and 2 SOFA points, respectively. This has important implications for improving sepsis diagnosis, especially when conventional arterial blood gas measurements are unavailable. Paper III verified that sepsis surveillance data can be utilized to develop machine learning screening tools to improve early identification of sepsis. A Bayesian network algorithm trained on routine electronic health record data predicted sepsis onset within 48 hours with better discrimination and earlier than conventional NEWS2 outside the ICU. The results suggested that screening may primarily be suited for the early admission period, which have broader implications also for other sepsis screening tools. Paper IV demonstrated that delays in antimicrobial treatment with in vitro pathogen coverage in BSI were associated with increased mortality after 12 hours from blood culture collection, but not at 1, 3, and 6 hours. This indicates a time window where clinicians should focus on the diagnostic workup, and proposes a target for rapid diagnostics of blood cultures. Finally, Paper V showed that the virulence genotype had some influence on mortality and septic shock in P. aeruginosa BSI, however, it was not a major prognostic determinant. Together these studies contribute to better understanding of the sepsis and BSI populations, and provide several suggestions to improve diagnosis and timing of treatment, with implications for clinical practice. Future works should focus on the implementation of sepsis surveillance, clinical trials of time to antimicrobial treatment and evaluating the prognostic importance of bacterial genotype data in larger populations from diverse infection sources and pathogens

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

[EN]Introduction: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. Methods and analysis: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. Ethics and dissemination: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease

Automated machine learning for healthcare and clinical notes analysis

Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes

Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation

A Systematic Approach to Manage Clinical Deterioration on Inpatient Units in the Health Care System

The transformation of health care delivery in the United States is accelerating at unbelievable speed. The acceleration is a result of many variables including health care reform as well as the covariation occurring with adjustments in regulations related to resident work hours. The evolving care delivery model has exposed a vulnerability of the health system, specifically in academic medical centers of the United States. Academic medical centers have established a care delivery model grounded and predicated in resident presence and performance. With changes in resident work expectations and reduced time spent in hospitals, an urgent need exists to evaluate and recreate a model of care that produces quality outcomes in an efficient, service driven organization. One potential care model that would stabilize organizations is infusion of APNs with the expanded skills and knowledge to instill practice continuity in the critical care environment. A Medicare demonstration project is proposed for funding an APN expanded role and alteration in the care delivery model. Formative and summative evaluation and impact of such an expanded practice role is included in the proposed project. An evolved partnership between the advanced practice nurse and physician will serve to fill some of the gap currently existing in the delivery system of today. As the complexity and acuity of the patients in the hospital escalates, innovation is demanded to ensure a care model that will foster achievement of the quality outcomes expected and deserved

Predicting clinical deterioration

This thesis describes the development of a prognostic algorithm that uses Electronic Patient Record (EPR) data to predict potentially avoidable adverse events (e.g., cardiac arrest/unanticipated Intensive Care Unit (ICU) admission) in sufficient time so that interventions can take place in patients admitted to the hospital ward. The system is called Hospital-wide Alerts Via Electronic Noticeboard (HAVEN). The thesis is composed of six chapters: evaluating variables for potential inclusion in HAVEN (chapter 1), evaluating the prognostic value of fractional inspired oxygen for potential inclusion in HAVEN (chapter 2), evaluating HAVEN in the ward environment (chapter 3), validating HAVEN (chapter 4), working towards improved outcome measures for HAVEN (chapter 5) and the automated quantification of the clinical workload associated with systems like HAVEN (chapter 6).Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Investigating genetic determinants of liver disease and its associations with cardiovascular diseases

Background Dramatic modifications in lifestyle have given rise to an epidemic in chronic liver diseases, predominantly driven by non-alcoholic fatty liver disease (NAFLD). The more severe NAFLD phenotypes are associated with elevated liver iron, inflammation (steatohepatitis), scarring and liver failure (fibrosis, cirrhosis), and possibly with cardiovascular diseases (CVDs); genetic and population studies of these phenotypes and their links to CVDs have been limited. Aims 1) Investigate the genetic susceptibility underlying liver MRI phenotypes (iron and corrected T1 (cT1), a steatohepatitis proxy) and explore associations with other cardiometabolic traits. 2) Investigate whether liver fibrosis is an independent risk factor for CVDs. Methods We carried out genome-wide association studies (GWASs) of liver MRI phenotypes (iron (N = 8,289), and corrected T1 (a steatohepatitis proxy, N = 14,440)) in UK Biobank. We used genetics to investigate causality with other traits. We calculated a FIB-4 score (a validated non-invasive scoring system that predicts liver fibrosis) in 44,956 individuals in the UK and investigated its association with the incidence of five CVDs (ischaemic stroke, myocardial infarction, heart failure, peripheral arterial disease, atrial fibrillation (AF)). Results Three genetic variants known to influence hepcidin regulation (rs1800562 (C282Y) and rs1799945 (H63D) in HFE, rs855791 (V736A) in TMPRSS6) were associated with liver iron (p < 5 x 10-8). Mendelian randomisation provided evidence that central obesity causes higher liver iron. Four variants (rs75935921 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were associated with elevated cT1 (p < 5 x 10-8). Insulin resistance, type 2 diabetes, fatty liver, and BMI were causally associated with elevated cT1 whilst favourable adiposity was protective. In 44,956 individuals over a median of 5.4 years, adjusted models demonstrated strong associations of “suspected liver fibrosis” (FIB-4 1.3) with cirrhosis (Hazard ratio (HR 13.64 [10.79 – 17.26], p < 2 x 10-16)) and hepatocellular carcinoma (HR 11.64 [5.15 – 26.31], p = 3.5 x 10-9), but no association with the incidence of most CVDs, albeit a modest increase in AF risk (HR 1.18 [1.01 – 1.37]), when compared to individuals with a FIB-4 < 1.3. Conclusions This thesis provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific. The association between two metal ion transporters and cT1 indicates a new mechanism in steatohepatitis. There is little evidence to suggest that liver fibrosis is an independent risk factor for most CVDs, except possibly a small increase risk in incident AF risk. This thesis’ findings can be used to investigate causality, generate hypotheses for drug development and inform health policies

Unlocking the Potential of Artificial Intelligence (AI) for Healthcare

This book chapter examines the potential of artificial intelligence (AI) to improve healthcare. AI has become increasingly prominent in healthcare, providing the capability to automate tasks, analyze large patient data sets, and deliver quicker and more cost-effective healthcare. We focus on its various applications in healthcare including vital sign monitoring, glycemic control, radiology, and emergency room triage with point of care ultrasound (POCUS). We also address Ai’s ethical, legal, and privacy implications in healthcare such as data protection and safeguarding patient privacy. Finally, we explore the potential of AI in healthcare improvement in the future and investigate the current trends, opportunities, and evolving threats posed by AI in healthcare, as well as its implications for human-AI interfacing and job security. This book chapter provides an essential and comprehensive overview of the potential of AI in healthcare, providing a valuable resource for healthcare professionals and researchers in the field