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  1. Article ; Online: Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services

    Ari Ercole

    BMJ Open, Vol 10, Iss

    2020  Volume 2

    Abstract: IntroductionHospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Metrics of performance have to date focused on the performance of individual elements rather than the whole ... ...

    Abstract IntroductionHospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Metrics of performance have to date focused on the performance of individual elements rather than the whole system. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counterintuitive outcomes and a holistic metric of hospital function might help design more efficient services.ObjectivesWe aimed to use network analysis to characterise the structure of the system of perioperative care for emergency surgical admissions in our tertiary care hospital.DesignWe constructed a weighted directional network representation of the emergency surgical services using patient location data from electronic health records.SettingA single-centre tertiary care hospital in the UK.ParticipantsWe selected data from the retrospective electronic health record data of all unplanned admissions with a surgical intervention during their stay during a 3.5-year period, which resulted in a set of 16 500 individual admissions.MethodsWe then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics.ResultsThe analysis showed the service to be a complex system with scale-free, small-world network properties. We also identified such potential hubs and bottlenecks in the system.ConclusionsOur holistic, system-wide description of a hospital service may provide tools to inform service improvement initiatives and gives us insights into the architecture of a complex system of care. The implications for the structure and resilience of the service is that while being robust in general, the system may be vulnerable to outages at specific key nodes.
    Keywords Medicine ; R
    Subject code 360
    Language English
    Publishing date 2020-02-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Bayesian model selection for multilevel models using integrated likelihoods.

    Tom Edinburgh / Ari Ercole / Stephen Eglen

    PLoS ONE, Vol 18, Iss 2, p e

    2023  Volume 0280046

    Abstract: Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the ...

    Abstract Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expressions for these quantities are available for the simplest linear models with unrealistic priors, but in most cases, direct computation is impossible. In practice, Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform. We present a method for estimation of the log model evidence, by an intermediate marginalisation over non-variance parameters. This reduces the dimensionality of any Monte Carlo sampling algorithm, which in turn yields more consistent estimates. The aim of this paper is to show how this framework fits together and works in practice, particularly on data with hierarchical structure. We illustrate this method on simulated multilevel data and on a popular dataset containing levels of radon in homes in the US state of Minnesota.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Bayesian model selection for multilevel models using integrated likelihoods

    Tom Edinburgh / Ari Ercole / Stephen Eglen

    PLoS ONE, Vol 18, Iss

    2023  Volume 2

    Abstract: Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the ...

    Abstract Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expressions for these quantities are available for the simplest linear models with unrealistic priors, but in most cases, direct computation is impossible. In practice, Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform. We present a method for estimation of the log model evidence, by an intermediate marginalisation over non-variance parameters. This reduces the dimensionality of any Monte Carlo sampling algorithm, which in turn yields more consistent estimates. The aim of this paper is to show how this framework fits together and works in practice, particularly on data with hierarchical structure. We illustrate this method on simulated multilevel data and on a popular dataset containing levels of radon in homes in the US state of Minnesota.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: ariercole/Cambridge_COVID-19_ICU

    Emma Rocheteau / Ari Ercole

    v1.0.0

    2020  

    Abstract: ... First ... ...

    Abstract First release
    Keywords covid19
    Publishing date 2020-03-19
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: ariercole/Cambridge_COVID-19_ICU

    Emma Rocheteau / Ari Ercole

    Cleaned release

    2020  

    Abstract: We have cleaned the code a little bit and got it working smoothly between ... ...

    Abstract We have cleaned the code a little bit and got it working smoothly between files
    Keywords covid19
    Publishing date 2020-03-24
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

    Jacob Deasy / Pietro Liò / Ari Ercole

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health ... ...

    Abstract Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2020-12-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  7. Article ; Online: Performance of cardiopulmonary exercise testing for the prediction of post-operative complications in non cardiopulmonary surgery

    Daniel J Stubbs / Lisa A Grimes / Ari Ercole

    PLoS ONE, Vol 15, Iss 2, p e

    A systematic review.

    2020  Volume 0226480

    Abstract: Introduction Cardiopulmonary exercise testing (CPET) is widely used within the United Kingdom for preoperative risk stratification. Despite this, CPET's performance in predicting adverse events has not been systematically evaluated within the framework ... ...

    Abstract Introduction Cardiopulmonary exercise testing (CPET) is widely used within the United Kingdom for preoperative risk stratification. Despite this, CPET's performance in predicting adverse events has not been systematically evaluated within the framework of classifier performance. Methods After prospective registration on PROSPERO (CRD42018095508) we systematically identified studies where CPET was used to aid in the prognostication of mortality, cardiorespiratory complications, and unplanned intensive care unit (ICU) admission in individuals undergoing non-cardiopulmonary surgery. For all included studies we extracted or calculated measures of predictive performance whilst identifying and critiquing predictive models encompassing CPET derived variables. Results We identified 36 studies for qualitative review, from 27 of which measures of classifier performance could be calculated. We found studies to be highly heterogeneous in methodology and quality with high potential for bias and confounding. We found seven studies that presented risk prediction models for outcomes of interest. Of these, only four studies outlined a clear process of model development; assessment of discrimination and calibration were performed in only two and only one study undertook internal validation. No scores were externally validated. Systematically identified and calculated measures of test performance for CPET demonstrated mixed performance. Data was most complete for anaerobic threshold (AT) based predictions: calculated sensitivities ranged from 20-100% when used for predicting risk of mortality with high negative predictive values (96-100%). In contrast, positive predictive value (PPV) was poor (2.9-42.1%). PPV appeared to be generally higher for cardiorespiratory complications, with similar sensitivities. Similar patterns were seen for the association of Peak VO2 (sensitivity 85.7-100%, PPV 2.7-5.9%) and VE/VCO2 (Sensitivity 27.8%-100%, PPV 3.4-7.1%) with mortality. Conclusions In general CPET's 'rule-out' capability appears better ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Sepsis-3 criteria in AmsterdamUMCdb

    Tom Edinburgh / Stephen J. Eglen / Patrick Thoral / Paul Elbers / Ari Ercole

    GigaByte (2022)

    open-source code implementation

    2022  

    Abstract: Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ...

    Abstract Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers.
    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher GigaScience Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Can process mapping and a multisite Delphi of perioperative professionals inform our understanding of system-wide factors that may impact operative risk?

    Ari Ercole / Nicholas Levy / Tom Bashford / John Clarkson / Daniel Stubbs / Fay Gilder / Basil Nourallah

    BMJ Open, Vol 12, Iss

    2022  Volume 11

    Abstract: Objectives To examine whether the use of process mapping and a multidisciplinary Delphi can identify potential contributors to perioperative risk. We hypothesised that this approach may identify factors not represented in common perioperative risk tools ... ...

    Abstract Objectives To examine whether the use of process mapping and a multidisciplinary Delphi can identify potential contributors to perioperative risk. We hypothesised that this approach may identify factors not represented in common perioperative risk tools and give insights of use to future research in this area.Design Multidisciplinary, modified Delphi study.Setting Two centres (one tertiary, one secondary) in the UK during 2020 amidst coronavirus pressures.Participants 91 stakeholders from 23 professional groups involved in the perioperative care of older patients. Key stakeholder groups were identified via process mapping of local perioperative care pathways.Results Response rate ranged from 51% in round 1 to 19% in round 3. After round 1, free text suggestions from the panel were combined with variables identified from perioperative risk scores. This yielded a total of 410 variables that were voted on in subsequent rounds. Including new suggestions from round two, 468/519 (90%) of the statements presented to the panel reached a consensus decision by the end of round 3. Identified risk factors included patient-level factors (such as ethnicity and socioeconomic status), and organisational or process factors related to the individual hospital (such as policies, staffing and organisational culture). 66/160 (41%) of the new suggestions did not feature in systematic reviews of perioperative risk scores or key process indicators. No factor categorised as ‘organisational’ is currently present in any perioperative risk score.Conclusions Through process mapping and a modified Delphi we gained insights into additional factors that may contribute to perioperative risk. Many were absent from currently used risk stratification scores. These results enable an appreciation of the contextual limitations of currently used risk tools and could support future research into the generation of more holistic data sets for the development of perioperative risk assessment tools.
    Keywords Medicine ; R
    Subject code 910
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Moving from non-emergency bleeps and long-range pagers to a hospital-wide, EHR-integrated secure messaging system

    Ewen Cameron / Justin Davies / James H F Rudd / Ari Ercole / Afzal Chaudhry / William Gelson / Claire Tolliday / Fiona Hamer

    BMJ Health & Care Informatics, Vol 30, Iss

    an implementer report

    2023  Volume 1

    Abstract: Introduction Obsolete bleep/long-range pager equipment remains firmly embedded in the National Health Service (NHS).Objective To introduce a secure, chart-integrated messaging system (Epic Secure Chat) in a large NHS tertiary referral centre to replace ... ...

    Abstract Introduction Obsolete bleep/long-range pager equipment remains firmly embedded in the National Health Service (NHS).Objective To introduce a secure, chart-integrated messaging system (Epic Secure Chat) in a large NHS tertiary referral centre to replace non-emergency bleeps/long-range pagers.Methods The system was socialised in the months before go-live. Operational readiness was overseen by an implementation group with stakeholder engagement. Cutover was accompanied by a week of Secure Chat and bleeps running in parallel.Results Engagement due to socialisation was high with usage stabilising approximately 3 months after go-live. Contact centre internal call activity fell significantly after go-live. No significant patient safety concerns were reported.Discussion Uptake was excellent with substantial utilisation well before cutover indirectly supporting high levels of engagement. The majority of those who previously carried bleeps were content to use personal devices for messaging because of user convenience after reassurance about privacy.Conclusion An integrated secure messaging system can replace non-emergency bleeps with beneficial impact on service.
    Keywords Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 360
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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