Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454

Reducing Sepsis Mortality: A Cloud-Based Alert Approach

The aim of this study is to examine the impact of a cloud-based CDS alerting system for SIRS, a precursor to sepsis, and sepsis itself, on adult patient and process outcomes at VCU Health System. The two main hypotheses are: 1) the implementation of cloud-based SIRS and sepsis alerts will lead to lower sepsis-related mortality and lower average length of stay, and 2) the implementation of cloud-based SIRS and sepsis alerts will lead to more frequent ordering of the Sepsis PowerPlan and more recording of sepsis diagnoses. To measure these outcomes, a pre-post study was conducted. A pre-implementation group diagnosed with sepsis within the year leading up to the alert intervention consisted of 1,551 unique inpatient visits, and the three-year post-implementation sample size was 9,711 visits, for a total cohort of 11,262 visits. Logistic regression and multiple linear regression were used to test the hypotheses. Study results showed that sepsis-related mortality was slightly higher after the implementation of SIRS alerts, but the presence of sepsis alerts did not have a significant relationship to mortality. The average length of stay and the total number of recorded sepsis diagnoses were higher after the implementation of both SIRS and sepsis alerts, while ordering of the Sepsis Initial Resuscitation PowerPlan was lower. There is preliminary evidence from this study that more sepsis diagnoses are made as a result of alert adoption, suggesting that clinicians can consider the implementation of these alerts in order to capture a higher number of sepsis diagnoses

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

Clinical decision support systems in the care of hospitalised patients with diabetes

This thesis explored the role of health informatics (decision support systems) in caring for hospitalised patients with diabetes through a systematic review and by analysing data from University Hospital Birmingham, UK. Findings from the thesis: 1) highlight the potential role of computerised physician order entry system in improving guideline based anti-diabetic medication prescription in particular insulin prescription, and their effectiveness in contributing to better glycaemic control; 2) quantify the occurrence of missed discharge diagnostic codes for diabetes using electronic prescription data and suggests 60% of this could be potentially reduced using an algorithm that could be introduced as part of the information system; 3) found that hypoglycaemia and foot disease in hospitalised diabetes patients were independently associated with higher in-hospital mortality rates and longer length of stay; 4) quantify the hypoglycaemia rates in non-diabetic patients and proposes one method of establishing a surveillance system to identify non diabetic hypoglycaemic patients; and 5) introduces a prediction model that may be useful to identify patients with diabetes at risk of poor clinical outcomes during their hospital stay

An interdisciplinary code sepsis team to improve sepsis bundle compliance in the emergency department

Purpose: Sepsis is one of the leading causes of mortality with over 700,000 hospitalizations and 200,000 deaths annually. Various tools exist to aid in the early identification and treatment of sepsis including electronic alert systems, standardized order sets, nurse-initiated protocols and specialty trained teams. Despite available guidelines, mortality rates for severe sepsis and septic shock are near 50%. Methods: The aims of this rapid cycle quality improvement project were 1) to develop and implement an interdisciplinary team to address early implementation of evidence-based sepsis bundles in the emergency department and 2) to compare sepsis bundle compliance three months pre-and three months’ post-intervention implementation. The population included all patients’ over 18 years of age presenting to the emergency department with clinical indications of sepsis, severe sepsis, or septic shock. Results: The pre-post intervention analysis shows an improvement in time to each bundle element except antibiotics. There was statistical significance in time to second lactate. Statistical significance was noted in the fluid resuscitation volume met (p=.000), initial lactate collected within 180 minutes (p=.001), and second lactate within 360 minutes (.000). Mortality rates in patients with sepsis on presentation showed a steady decline from 12.45% in the first month pre-intervention to 4.55% in the last month post intervention. Conclusion: Interdisciplinary teams can utilize existing knowledge, skills and tools to improve sepsis bundle compliance and mortality outcomes in sepsis patients presenting to the emergency department

Hospital Sepsis Program : Core Elements: 2023

Who is the Hospital Sepsis Program Core Elements guidance for?Clinicians, hospitals, and health systems leading efforts to improve the hospital management and outcomes of sepsis.Effective leadership is required to engage the multidisciplinary expertise required to support the care of patients with sepsis, as detailed later in this document.The Core Elements are intended to build upon the work of a number of initiatives related to sepsis that have been developed over the years. To find the most updated links to some practical resources that can help hospitals improve specific aspects of their sepsis programs, please visit https://www.cdc.gov/sepsis/core-elements/resources.html.Suggested citation: CDC. Hospital Sepsis Program Core Elements. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. Available at https://www.cdc.gov/sepsis/core-elements.htmlCS341364-Asepsis-core-elements-H.pd

Personalised antimicrobial management in secondary care

Background: The growing threat of Antimicrobial Resistance (AMR) requires innovative methods to promote the sustainable effectiveness of antimicrobial agents. Hypothesis: This thesis aimed to explore the hypothesis that personalised decision support interventions have the utility to enhance antimicrobial management across secondary care. Methods: Different research methods were used to investigate this hypothesis. Individual physician decision making was mapped and patient experiences of engagement with decision making explored using semi-structured interviews. Cross-specialty engagement with antimicrobial management was investigated through cross-sectional analysis of conference abstracts and educational training curricula. Artificial intelligence tools were developed to explore their ability to predict the likelihood of infection and provide individualised prescribing recommendations using routine patient data. Dynamic, individualised dose optimisation was explored through: (i) development of a microneedle based, electrochemical biosensor for minimally invasive monitoring of beta-lactams; and (ii) pharmacokinetic (PK)-pharmacodynamic (PD) modelling of a new PK-PD index using C-Reactive protein (CRP) to predict the pharmacodynamics of vancomycin. Ethics approval was granted for all aspects of work explored within this thesis. Results: Mapping of individual physician decision making during infection management demonstrated several areas where personalised, technological interventions could enhance antimicrobial management. At specialty level, non-infection specialties have little engagement with antimicrobial management. The importance of engaging surgical specialties, who have relatively high rates of antimicrobial usage and healthcare associated infections, was observed. An individualised information leaflet, co-designed with patients, to provide personalised infection information to in-patients receiving antibiotics significantly improved knowledge and reported engagement with decision making. Artificial intelligence was able to enhance the prediction of infection and the prescribing of antimicrobials using routinely available clinical data. Real-time, continuous penicillin monitoring was demonstrated using a microneedle based electrochemical sensor in-vivo. A new PK-PD index, using C-Reactive Protein, was able to predict individual patient response to vancomycin therapy at 96-120 hours of therapy. Conclusion: Through co-design and the application of specific technologies it is possible to provide personalised antimicrobial management within secondary care.Open Acces

Patient Monitoring Systems

book chapterBiomedical Informatic