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

The EFFECT (End-oF-liFE-CommunicaTion) Study: The Acceptability, Feasibility, and Potential Impact of Using Mortality Prediction Scores for Initiating End-of-Life Goals of Care Communication in the Adult Intensive Care Unit

Purpose: The purpose of this dissertation was to determine the acceptability, feasibility, and potential impact of using Severity of Illness (SOI) mortality risk prediction scores for initiating end-of-life (EOL) goals-of-care communication in the adult Intensive Care Unit (ICU). First, an integrative review was conducted to evaluate the psychometric properties of existing SOI scoring systems and their ability to predict mortality in the adult ICU population as the basis for clinical care and provider-patient/family communication. Next, an integrative review of interventions that can guide researchers in reducing surrogate burden was conducted as the basis for conducting research that may impact surrogates of dying patients in the ICU. Finally, a mixed-methods study was conducted to determine the acceptability and feasibility of having providers use SOI mortality prediction scores for their patients as part of routine care and investigate providers’ intentions to change practice related to goals-of-care communication as a result of awareness of the scores. Problem: While healthcare teams recognize that profoundly ill patients in adult ICUs may die, many families are caught by surprise when their loved one dies in a setting with the most advanced technology and intense care available. ICU deaths account for about 20% of patient deaths in US hospitals and this rate is increasing due in part to deficiencies in EOL care communication that can compromise quality of EOL care and increase resource utilization. Previous studies suggest that communication about EOL goals-of-care is infrequent among healthcare providers, patients, and families; often occurs late in the course of illness; and relies on family members to act as patient surrogates in discussions. Furthermore, despite advances in healthcare quality, family members remain more dissatisfied with communication in the ICU than with other aspects of care. Mechanisms for increasing the timeliness and frequency of discussions about EOL goals-of-care are needed. Specific Aims: Aim 1. Evaluate four valid SOI instruments to determine which instrument, or combination of instruments, is the best fit for the study site, given providers’ perceived feasibility of use. Aim 2. Evaluate the acceptability and feasibility of having providers use SOI mortality prediction scores for their patients as part of routine workflow and practice. Aim 3. Evaluate providers’ intentions to change their practice related to goals-of-care communication with patients and/or their families as a result of awareness of SOI mortality prediction scores. Design: First, an integrative review was conducted to evaluate the psychometric properties of existing SOI scoring systems and their ability to predict mortality in the adult ICU. This review provided the foundational knowledge needed in the selection of SOI systems that were used in aim 1. Next, an integrative review of interventions that can guide researchers in reducing surrogate burden was conducted. This review provided foundational knowledge needed for designing a study that may impact surrogates of dying patients in the ICU. Lastly, an explanatory mixed-methods study was conducted to determine the acceptability and feasibility of having providers use SOI mortality prediction scores for their patients as part of routine care and investigate providers’ intentions to change practice related to goals-of-care communication as a result of awareness of the scores. Self-efficacy theory was used as the theoretical underpinning for the design of this study, specifically aim 3. Findings: Based on discrimination alone, the first integrative review found APACHE IV to be superior, but the VA ICU, SICULA, and SOFA Max were close with ‘very good’ discrimination. The second integrative review revealed six levels of intervention, from the personal ‘Direct Care of the Surrogate’ to the population-based ‘Legal/Regulatory’ and provided a framework to assist researchers when designing and conducting research that involves surrogates. The dissertation study found the use of mortality risk prediction scores as part of routine workflow and practice to be acceptable and feasible – providers agreed to participate, patient mortality risk were evaluated by the instrument chosen by the providers (i.e., the Sequential Organ Failure Assessment - SOFA), and overall, participants found use of daily mortality prediction scores possible in their setting. However, there was some disagreement related to the use of SOFA scores as an effective way for determining patient mortality risk. Based on themes that emerged from interviews, providers with limited ICU experience were eager and accepting of the mortality risk scores while those with vast experience found the scores to be an adjunct to their own intuition; though all acknowledged the benefit of looking at daily scores or ‘trends’. The most substantial of all themes identified was the need to consider SOFA scores in relation to patient context; a number alone should not determine mortality risk and whether a goals-of-care conversation needs to occur. Conclusion: This dissertation study found that overall, participants indicated that using mortality prediction scores as part of their daily workflow was acceptable and feasible. Use of SOFA scores for potentially increasing EOL goals-of-care conversations appears to be most beneficial for providers with limited ICU experience. Large-scale studies are needed to determine the effect of using mortality risk predictions on patient EOL outcomes

Development of the Assessment of Clinical Prediction Model Transportability (APT) Checklist

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and tools. However, naïve implementations of external CPMs are prone to failure due to the incompatibilities between the environments of the development and implementation sites. Although prior research has described methods for estimating the external validity of predictive models, quantifying dataset shift, updating models, as well as numerous CPM-specific frameworks for guiding the development, evaluation, reporting, and systematic reviews of CPMs, there are no frameworks for assessing the compatibility between a CPM and the target environment. This dissertation addresses this critical gap by proposing a novel CPM transportability checklist for guiding the adoption of externally developed CPMs.To guide the development of the checklist, four extant CPM-relevant frameworks (TRIPOD, CHARMS, PROBAST, and GRASP) were reviewed and synthesized, thereby identifying the key domains of CPMs. Then, four individual studies were conducted, each identifying, assessing the impact of, and/or proposing solutions for the disparity between CPM and environment in those domains. The first two studies target disparities in features, with the first characterizing the non-generalizability impact of a particular class of commonly used, EHR-idiosyncratic features. The second study was conducted to identify and propose a solution for the semantic discrepancy in features across sites caused by the insufficient coverage of EHR data by standards. The third study focused on the prediction target of CPMs, identifying significant heterogeneity in disease understanding, phenotyping algorithms, and cohort characteristics of the same clinical condition. In the fourth study investigating CPM evaluation, the gap between typical CPM evaluation design and expected implemented behavior was identified, and a novel evaluative framework was proposed to bridge that gap. Finally, the APT checklist was developed using the synthesis of the aforementioned CPM frameworks as the foundation, enriched through the incorporation of innovations and findings from these four conducted studies. While rigorous meta-evaluation remains, the APT checklist shows promise as a tool for assessing CPM transportability thereby reducing the risk of failure of externally implemented CPMs. The key contributions to informatics include: the discovery of healthcare process (HCP) variables as a driver of CPM non-transportability, the fragility of clinical phenotyping used to identify CPM targets, a novel classification system and meta-heuristics for an aspect of EHR data previously lacking in standards, a novel CPM evaluation design termed the pseudo-prospective trial, and the APT checklist. Overall, this work contributes to the body of biomedical informatics literature guiding the success of informatics interventions

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we present the COVID-19 Early Warning System (CovEWS), a clinical risk scoring system for assessing COVID-19 related mortality risk. CovEWS provides continuous real-time risk scores for individual patients with clinically meaningful predictive performance up to 192 hours (8 days) in advance, and is automatically derived from patients' electronic health records (EHRs) using machine learning. We trained and evaluated CovEWS using de-identified data from a cohort of 66430 COVID-19 positive patients seen at over 69 healthcare institutions in the United States (US), Australia, Malaysia and India amounting to an aggregated total of over 2863 years of patient observation time. On an external test cohort of 5005 patients, CovEWS predicts COVID-19 related mortality from $78.8\%$ ($95\%$ confidence interval [CI]: $76.0$, $84.7\%$) to $69.4\%$ ($95\%$ CI: $57.6, 75.2\%$) specificity at a sensitivity greater than $95\%$ between respectively 1 and 192 hours prior to observed mortality events - significantly outperforming existing generic and COVID-19 specific clinical risk scores. CovEWS could enable clinicians to intervene at an earlier stage, and may therefore help in preventing or mitigating COVID-19 related mortality

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings