ORCID Profile
0000-0001-8407-7176
Current Organisations
ARC Centre of Excellence for Engineered Quantum Systems
,
University of Queensland
,
Imperial College London
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Publisher: University of Chicago Press
Date: 03-2017
DOI: 10.1093/BJPS/AXX044
Publisher: Elsevier BV
Date: 12-2021
Publisher: Georg Thieme Verlag KG
Date: 03-2022
Abstract: Objective A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. Methods Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. Results A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. Conclusion Health care organizations face erse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting erse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.
Publisher: Cold Spring Harbor Laboratory
Date: 21-03-2022
DOI: 10.1101/2022.03.18.22272601
Abstract: Acute kidney injury (AKI) is one of the most common and significant problems in patients with COVID-19. However, little is known about the incidence and impact of AKI occurring in the community or early in the hospital admission. The traditional KDIGO definition can fail to identify patients for whom hospitalization coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesized that an extended KDIGO definition, adapted from the International Society of Nephrology 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI with similarly poor outcomes as previously reported in this population. All in iduals in the ISARIC cohort admitted to hospital with SARS-CoV-2 infection from February 15 th , 2020, to February 1 st , 2021, were included in the study. Data was collected and analysed for the duration of a patient’s admission. Incidence, staging and timing of AKI were evaluated using a traditional and extended KDIGO (eKDIGO) definition which incorporated a commensurate decrease in serum creatinine. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristic and outcomes – intensive care unit (ICU) admission, invasive mechanical ventilation and in-hospital death - were compared for all three groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. 75,670 patients from 54 countries were included in the final analysis cohort. Median length of admission was 12 days (IQR 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7 vs 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% vs 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% vs 23%) invasive ventilation (45% vs 15%) and increased mortality (38% vs 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted OR: 1.78, 95% confidence interval: 1.71-1.8, p-value 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% vs 50% death and 35% vs 70% ICU admission) but significantly higher when compared to patients with no AKI (25% vs 19% death and 35% vs 23% ICU admission) (all p values 5×10 −5 ). Limitations include ad hoc sCr s ling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis. The use of an extended KDIGO definition to diagnose AKI in this population resulted in a significantly higher incidence rate compared to traditional KDIGO criteria. These additional cases of AKI appear to be occurring in the community or early in the hospital admission and are associated with worse outcomes than those without AKI. Previous studies have shown that acute kidney injury (AKI) is a common problem among hospitalized patients with COVID-19. The current biochemical criteria used to diagnose AKI may be insufficient to capture AKI that develops in the community and is recovering by the time a patient presents to hospital. The use of an extended definition, that can identify AKI both during its development and recovery phase, may allow us to identify more patients with AKI. These patients may benefit from early management strategies to improve long term outcomes. In this study, we examined AKI incidence, severity and outcomes among a large international cohort of patients with COVID-19 using both a traditional and extended definition of AKI. We found that using the extended definition identified almost twice as many cases of AKI than the traditional definition (31.7 vs 16.8%). These additional cases of AKI were generally less severe and occurred earlier in the hospital admission. Nevertheless, they were associated with worse outcomes, including ICU admission and in-hospital death (adjusted odds ratio: 1.78, 95% confidence interval: 1.71-1.8, p-value 0.001) than those with no AKI. The current definition of AKI fails to identify a large group of patients with AKI that appears to develop in the community or early in the hospital admission. Given the finding that these cases of AKI are associated with worse admission outcomes than those without AKI, identifying and managing them in a timely manner is enormously important.
Publisher: Cold Spring Harbor Laboratory
Date: 25-07-2020
DOI: 10.1101/2020.07.17.20155218
Abstract: ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 in iduals from 1701 partner institutions and networks across 60 countries. The comprehensive analyses detailed in this report includes hospitalised in iduals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 699,014 cases who meet eligibility criteria for this report, selected findings include: median age of 58 years, with an approximately equal (50/50) male:female sex distribution 29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age the most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time the five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports age-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above 16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%) antibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics use of corticosteroids was reported in 24% of all patients for whom data were available (677,012) in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids outcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: esearch/covid-19-clinical-research-resources/evidence-reports/
Publisher: MDPI AG
Date: 08-09-2018
DOI: 10.3390/E20090687
Abstract: Characterising causal structure is an activity that is ubiquitous across the sciences. Causal models are representational devices that can be used as oracles for future interventions, to predict how values of some variables will change in response to interventions on others. Recent work has generalised concepts from this field to situations involving quantum systems, resulting in a new notion of quantum causal structure. A key concept in both the classical and quantum context is that of an intervention. Interventions are the controlled operations required to identify causal structure and ultimately the feature that endows causal models with empirical meaning. Although interventions are a crucial feature of both the classical and quantum causal modelling frameworks, to date there has been no discussion of their physical basis. In this paper, we consider interventions from a physical perspective and show that, in both the classical and quantum case, they are constrained by the thermodynamics of measurement and feedback in open systems. We demonstrate that the perfect “atomic” or “surgical” interventions characterised by Pearl’s famous do-calculus are physically impossible, and this is the case for both classical and quantum systems.
Publisher: University of Queensland Library
Publisher: European Respiratory Society (ERS)
Date: 10-12-2021
DOI: 10.1183/23120541.00552-2021
Abstract: Due to the large number of patients with severe coronavirus disease 2019 (COVID-19), many were treated outside the traditional walls of the intensive care unit (ICU), and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or noninvasive mechanical ventilation, high-flow nasal cannula, inotropes or vasopressors. A logistic generalised additive model was used to compare clinical outcomes among patients admitted or not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median (interquartile range (IQR), 67 (55–78) years), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 (5–19) days and was longer in patients admitted to an ICU than in those who were cared for outside the ICU (12 (6–23) days versus 8 (4–15) days, p .0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% (5797 out of 18 831) versus 39.0% (7532 out of 19 295), p .0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR 0.70, 95% CI 0.65–0.75 p .0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside an ICU.
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer Science and Business Media LLC
Date: 22-06-2014
Publisher: American Physical Society (APS)
Date: 26-08-2021
Publisher: IOP Publishing
Date: 05-2020
Abstract: In this article we present a full description of the quantum Kerr Ising model—a linear optical network of parametrically pumped Kerr nonlinearities. We consider the non-dissipative Kerr Ising model and, using variational techniques, show that the energy spectrum is primarily determined by the adjacency matrix in the Ising model and exhibits highly non-classical cat like eigenstates. We then introduce dissipation to give a quantum mechanical treatment of the measurement process based on homodyne detection via the conditional stochastic Schrodinger equation. Finally, we identify a quantum advantage in comparison to the classical analogue for the ex le of two anti-ferromagnetic cavities.
Publisher: Elsevier BV
Date: 06-2022
DOI: 10.1016/J.IJMEDINF.2022.104758
Abstract: Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for In idual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, s le size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.
Publisher: BMJ
Date: 12-2020
DOI: 10.1136/BMJOPEN-2020-041417
Abstract: There is a paucity of data that can be used to guide the management of critically ill patients with COVID-19. In response, a research and data-sharing collaborative—The COVID-19 Critical Care Consortium—has been assembled to harness the cumulative experience of intensive care units (ICUs) worldwide. The resulting observational study provides a platform to rapidly disseminate detailed data and insights crucial to improving outcomes. This is an international, multicentre, observational study of patients with confirmed or suspected SARS-CoV-2 infection admitted to ICUs. This is an evolving, open-ended study that commenced on 1 January 2020 and currently includes sites in over 48 countries. The study enrols patients at the time of ICU admission and follows them to the time of death, hospital discharge or 28 days post-ICU admission, whichever occurs last. Key data, collected via an electronic case report form devised in collaboration with the International Severe Acute Respiratory and Emerging Infection Consortium/Short Period Incidence Study of Severe Acute Respiratory Illness networks, include: patient demographic data and risk factors, clinical features, severity of illness and respiratory failure, need for non-invasive and/or mechanical ventilation and/or extracorporeal membrane oxygenation and associated complications, as well as data on adjunctive therapies. Local principal investigators will ensure that the study adheres to all relevant national regulations, and that the necessary approvals are in place before a site may contribute data. In jurisdictions where a waiver of consent is deemed insufficient, prospective, representative or retrospective consent will be obtained, as appropriate. A web-based dashboard has been developed to provide relevant data and descriptive statistics to international collaborators in real-time. It is anticipated that, following study completion, all de-identified data will be made open access. ACTRN12620000421932 ( anzctr.org.au/ACTRN12620000421932.aspx ).
Publisher: IOP Publishing
Date: 09-10-2024
Publisher: IOP Publishing
Date: 04-05-2018
Publisher: Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Date: 18-05-2018
Abstract: Realist interpretations of quantum mechanics presuppose the existence of elements of reality that are independent of the actions used to reveal them. Such a view is challenged by several no-go theorems that show quantum correlations cannot be explained by non-contextual ontological models, where physical properties are assumed to exist prior to and independently of the act of measurement. However, all such contextuality proofs assume a traditional notion of causal structure, where causal influence flows from past to future according to ordinary dynamical laws. This leaves open the question of whether the apparent contextuality of quantum mechanics is simply the signature of some exotic causal structure, where the future might affect the past or distant systems might get correlated due to non-local constraints. Here we show that quantum predictions require a deeper form of contextuality: even allowing for arbitrary causal structure, no model can explain quantum correlations from non-contextual ontological properties of the world, be they initial states, dynamical laws, or global constraints.
Publisher: American Physical Society (APS)
Date: 22-06-2020
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2018
DOI: 10.1142/S0219749918400105
Abstract: Supervised learning algorithms take as input a set of labeled ex les and return as output a predictive model. Such models are used to estimate labels for future, previously unseen ex les, drawn from the same generating distribution. In this paper, we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated datasets. Our results indicate this is a promising line of research.
Publisher: American Physical Society (APS)
Date: 12-03-2021
Publisher: MDPI AG
Date: 10-08-2023
DOI: 10.3390/E25081190
Abstract: We ground the asymmetry of causal relations in the internal physical states of a special kind of open and irreversible physical system, a causal agent. A causal agent is an autonomous physical system, maintained in a steady state, far from thermal equilibrium, with special subsystems: sensors, actuators, and learning machines. Using feedback, the learning machine, driven purely by thermodynamic constraints, changes its internal states to learn probabilistic functional relations inherent in correlations between sensor and actuator records. We argue that these functional relations just are causal relations learned by the agent, and so such causal relations are simply relations between the internal physical states of a causal agent. We show that learning is driven by a thermodynamic principle: the error rate is minimised when the dissipated power is minimised. While the internal states of a causal agent are necessarily stochastic, the learned causal relations are shared by all machines with the same hardware embedded in the same environment. We argue that this dependence of causal relations on such ‘hardware’ is a novel demonstration of causal perspectivalism.
Publisher: Springer Science and Business Media LLC
Date: 25-06-2021
DOI: 10.1007/S15010-021-01599-5
Abstract: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69% at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23 85%), older adults (≥ 70 years: 61, 62, 65 90%), and women (66, 66, 64 90% vs. men 71, 70, 67 93%, each P 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 09-06-2021
DOI: 10.1186/S13054-021-03518-4
Abstract: Heterogeneous respiratory system static compliance ( C RS ) values and levels of hypoxemia in patients with novel coronavirus disease (COVID-19) requiring mechanical ventilation have been reported in previous small-case series or studies conducted at a national level. We designed a retrospective observational cohort study with rapid data gathering from the international COVID-19 Critical Care Consortium study to comprehensively describe C RS —calculated as: tidal volume/[airway plateau pressure-positive end-expiratory pressure (PEEP)]—and its association with ventilatory management and outcomes of COVID-19 patients on mechanical ventilation (MV), admitted to intensive care units (ICU) worldwide. We studied 745 patients from 22 countries, who required admission to the ICU and MV from January 14 to December 31, 2020, and presented at least one value of C RS within the first seven days of MV. Median (IQR) age was 62 (52–71), patients were predominantly males (68%) and from Europe/North and South America (88%). C RS , within 48 h from endotracheal intubation, was available in 649 patients and was neither associated with the duration from onset of symptoms to commencement of MV ( p = 0.417) nor with PaO 2 /FiO 2 ( p = 0.100). Females presented lower C RS than males (95% CI of C RS difference between females-males: − 11.8 to − 7.4 mL/cmH 2 O p 0.001), and although females presented higher body mass index (BMI), association of BMI with C RS was marginal ( p = 0.139). Ventilatory management varied across C RS range, resulting in a significant association between C RS and driving pressure (estimated decrease − 0.31 cmH 2 O/L per mL/cmH 2 0 of C RS , 95% CI − 0.48 to − 0.14, p 0.001). Overall, 28-day ICU mortality, accounting for the competing risk of being discharged within the period, was 35.6% (SE 1.7). Cox proportional hazard analysis demonstrated that C RS (+ 10 mL/cm H 2 O) was only associated with being discharge from the ICU within 28 days (HR 1.14, 95% CI 1.02–1.28, p = 0.018). This multicentre report provides a comprehensive account of C RS in COVID-19 patients on MV. C RS measured within 48 h from commencement of MV has marginal predictive value for 28-day mortality, but was associated with being discharged from ICU within the same period. Trial documentation: Available at tudy . Trial registration : ACTRN12620000421932.
Publisher: IOP Publishing
Date: 24-06-2016
Publisher: Public Library of Science (PLoS)
Date: 20-04-2022
DOI: 10.1371/JOURNAL.PMED.1003969
Abstract: Acute kidney injury (AKI) is one of the most common and significant problems in patients with Coronavirus Disease 2019 (COVID-19). However, little is known about the incidence and impact of AKI occurring in the community or early in the hospital admission. The traditional Kidney Disease Improving Global Outcomes (KDIGO) definition can fail to identify patients for whom hospitalisation coincides with recovery of AKI as manifested by a decrease in serum creatinine (sCr). We hypothesised that an extended KDIGO (eKDIGO) definition, adapted from the International Society of Nephrology (ISN) 0by25 studies, would identify more cases of AKI in patients with COVID-19 and that these may correspond to community-acquired AKI (CA-AKI) with similarly poor outcomes as previously reported in this population. All in iduals recruited using the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)–World Health Organization (WHO) Clinical Characterisation Protocol (CCP) and admitted to 1,609 hospitals in 54 countries with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection from February 15, 2020 to February 1, 2021 were included in the study. Data were collected and analysed for the duration of a patient’s admission. Incidence, staging, and timing of AKI were evaluated using a traditional and eKDIGO definition, which incorporated a commensurate decrease in sCr. Patients within eKDIGO diagnosed with AKI by a decrease in sCr were labelled as deKDIGO. Clinical characteristics and outcomes—intensive care unit (ICU) admission, invasive mechanical ventilation, and in-hospital death—were compared for all 3 groups of patients. The relationship between eKDIGO AKI and in-hospital death was assessed using survival curves and logistic regression, adjusting for disease severity and AKI susceptibility. A total of 75,670 patients were included in the final analysis cohort. Median length of admission was 12 days (interquartile range [IQR] 7, 20). There were twice as many patients with AKI identified by eKDIGO than KDIGO (31.7% versus 16.8%). Those in the eKDIGO group had a greater proportion of stage 1 AKI (58% versus 36% in KDIGO patients). Peak AKI occurred early in the admission more frequently among eKDIGO than KDIGO patients. Compared to those without AKI, patients in the eKDIGO group had worse renal function on admission, more in-hospital complications, higher rates of ICU admission (54% versus 23%) invasive ventilation (45% versus 15%), and increased mortality (38% versus 19%). Patients in the eKDIGO group had a higher risk of in-hospital death than those without AKI (adjusted odds ratio: 1.78, 95% confidence interval: 1.71 to 1.80, p -value 0.001). Mortality and rate of ICU admission were lower among deKDIGO than KDIGO patients (25% versus 50% death and 35% versus 70% ICU admission) but significantly higher when compared to patients with no AKI (25% versus 19% death and 35% versus 23% ICU admission) (all p -values × 10 −5 ). Limitations include ad hoc sCr s ling, exclusion of patients with less than two sCr measurements, and limited availability of sCr measurements prior to initiation of acute dialysis. An extended KDIGO definition of AKI resulted in a significantly higher detection rate in this population. These additional cases of AKI occurred early in the hospital admission and were associated with worse outcomes compared to patients without AKI.
Publisher: SAGE Publications
Date: 27-01-2021
Abstract: The COVID-19 pandemic has required intensive care units to rapidly adjust and adapt their existing practices. Although there has a focus on expanding critical care infrastructure, equipment and workforce, plans have not emphasised the need to increase digital capabilities. The objective of this report was to recognise key areas of digital health related to the COVID-19 response. We identified and explored six focus areas relevant to intensive care, including using digital solutions to increase critical care capacity, developing surge capacity within an electronic health record, maintenance and downtime planning, training considerations and the role of data analytics. This article forms the basis of a framework for the intensive care digital health response to COVID-19 and other emerging infectious disease outbreaks.
Location: Australia
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sally Shrapnel.