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

Quality metrics for the evaluation of Rapid Response Systems: Proceedings from the third international consensus conference on Rapid Response Systems.

BACKGROUND: Clinically significant deterioration of patients admitted to general wards is a recognized complication of hospital care. Rapid Response Systems (RRS) aim to reduce the number of avoidable adverse events. The authors aimed to develop a core quality metric for the evaluation of RRS. METHODS: We conducted an international consensus process. Participants included patients, carers, clinicians, research scientists, and members of the International Society for Rapid Response Systems with representatives from Europe, Australia, Africa, Asia and the US. Scoping reviews of the literature identified potential metrics. We used a modified Delphi methodology to arrive at a list of candidate indicators that were reviewed for feasibility and applicability across a broad range of healthcare systems including low and middle-income countries. The writing group refined recommendations and further characterized measurement tools. RESULTS: Consensus emerged that core outcomes for reporting for quality improvement should include ten metrics related to structure, process and outcome for RRS with outcomes following the domains of the quadruple aim. The conference recommended that hospitals should collect data on cardiac arrests and their potential predictability, timeliness of escalation, critical care interventions and presence of written treatment goals for patients remaining on general wards. Unit level reporting should include the presence of patient activated rapid response and metrics of organizational culture. We suggest two exploratory cost metrics to underpin urgently needed research in this area. CONCLUSION: A consensus process was used to develop ten metrics for better understanding the course and care of deteriorating ward patients. Others are proposed for further development

Artificial intelligence and clinical deterioration

Purpose of review: To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI). Recent findings: There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms. Summary: Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach

Background We have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients’ vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm. Objectives The aim of this study was to identify variables or concepts that could improve HAVEN predictive performance. Methods This was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., ‘HAVEN Top 5′) had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data. Results The 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47). Conclusions Digital-only validation methods code the cohort not admitted to ICU as ‘falsely positive’ in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation

The Effect of Fractional Inspired Oxygen Concentration on Early Warning Score Performance: a database analysis

Objectives To calculate fractional inspired oxygen concentration (FiO2) thresholds in ward patients and add these to the National Early Warning Score (NEWS). To evaluate the performance of NEWS-FiO2 against NEWS when predicting in-hospital death and unplanned intensive care unit (ICU) admission. Methods A multi-centre, retrospective, observational cohort study was carried out in five hospitals from two UK NHS Trusts. Adult admissions with at least one complete set of vital sign observations recorded electronically were eligible. The primary outcome measure was an ‘adverse event’ which comprised either in-hospital death or unplanned ICU admission. Discrimination was assessed using the Area Under the Receiver Operating Characteristic curve (AUROC). Results A cohort of 83,304 patients from a total of 271,363 adult admissions were prescribed oxygen. In this cohort, NEWS-FiO2 (AUROC 0.823, 95% CI 0.819–0.824) outperformed NEWS (AUORC 0.811, 95% CI 0.809–0.814) when predicting in-hospital death or unplanned ICU admission within 24 h of a complete set of vital sign observations. Conclusions NEWS-FiO2 generates a performance gain over NEWS when studied in ward patients requiring oxygen. This warrants further study, particularly in patients with respiratory disorders

sj-docx-1-inc-10.1177_17511437241246901 – Supplemental material for Intensive care unit admission criteria: a scoping review

Supplemental material, sj-docx-1-inc-10.1177_17511437241246901 for Intensive care unit admission criteria: a scoping review by James Soares, Catherine Leung, Victoria Campbell, Anton Van Der Vegt, James Malycha and Christopher Andersen in Journal of the Intensive Care Society</p

Systematic review of applied usability metrics within usability evaluation methods for hospital electronic healthcare record systems: Metrics and Evaluation Methods for eHealth Systems

Background and objectives: Electronic healthcare records have become central to patient care. Evaluation of new systems include a variety of usability evaluation methods or usability metrics (often referred to interchangeably as usability components or usability attributes). This study reviews the breadth of usability evaluation methods, metrics, and associated measurement techniques that have been reported to assess systems designed for hospital staff to assess inpatient clinical condition. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we searched Medline, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, and Open Grey from 1986 to 2019. For included studies, we recorded usability evaluation methods or usability metrics as appropriate, and any measurement techniques applied to illustrate these. We classified and described all usability evaluation methods, usability metrics, and measurement techniques. Study quality was evaluated using a modified Downs and Black checklist. Results: The search identified 1336 studies. After abstract screening, 130 full texts were reviewed. In the 51 included studies 11 distinct usability evaluation methods were identified. Within these usability evaluation methods, seven usability metrics were reported. The most common metrics were ISO9241-11 and Nielsen's components. An additional “usefulness” metric was reported in almost 40% of included studies. We identified 70 measurement techniques used to evaluate systems. Overall study quality was reflected in a mean modified Downs and Black checklist score of 6.8/10 (range 1–9) 33% studies classified as “high-quality” (scoring eight or higher), 51% studies “moderate-quality” (scoring 6–7), and the remaining 16% (scoring below five) were “low-quality.” Conclusion: There is little consistency within the field of electronic health record systems evaluation. This review highlights the variability within usability methods, metrics, and reporting. Standardized processes may improve evaluation and comparison electronic health record systems and improve their development and implementation

Additional file 1: of Variables associated with unplanned general adult ICU admission in hospitalised patients: protocol for a systematic review

PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol. (DOC 97 kb

Defining the correlates of lymphopenia and independent predictors of poor clinical outcome in adults hospitalized with COVID-19 in Australia

Abstract Lymphopenia is a common feature of acute COVID-19 and is associated with increased disease severity and 30-day mortality. Here we aim to define the demographic and clinical characteristics that correlate with lymphopenia in COVID-19 and determine if lymphopenia is an independent predictor of poor clinical outcome. We analysed the ENTER-COVID (Epidemiology of hospitalized in-patient admissions following planned introduction of Epidemic SARS-CoV-2 to highly vaccinated COVID-19 naïve population) dataset of adults (N = 811) admitted for COVID-19 treatment in South Australia in a retrospective registry study, categorizing them as (a) lymphopenic (lymphocyte count < 1 × 109/L) or (b) non-lymphopenic at hospital admission. Comorbidities and laboratory parameters were compared between groups. Multiple regression analysis was performed using a linear or logistic model. Intensive care unit (ICU) patients and non-survivors exhibited lower median lymphocyte counts than non-ICU patients and survivors respectively. Univariate analysis revealed that low lymphocyte counts associated with hypertension and correlated with haemoglobin, platelet count and negatively correlated with urea, creatinine, bilirubin, and aspartate aminotransferase (AST). Multivariate analysis identified age, male, haemoglobin, platelet count, diabetes, creatinine, bilirubin, alanine transaminase, c-reactive protein (CRP) and lactate dehydrogenase (LDH) as independent predictors of poor clinical outcome in COVID-19, while lymphopenia did not emerge as a significant predictor