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  1. Article ; Online: A systematic review of cardiac surgery clinical prediction models that include intra-operative variables.

    Jones, Ceri / Taylor, Marcus / Sperrin, Matthew / Grant, Stuart W

    Perfusion

    2024  , Page(s) 2676591241237758

    Abstract: Background: Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this ... ...

    Abstract Background: Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery.
    Methods: Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures.
    Results: A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two.
    Conclusion: Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.
    Language English
    Publishing date 2024-04-22
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 645038-6
    ISSN 1477-111X ; 0267-6591
    ISSN (online) 1477-111X
    ISSN 0267-6591
    DOI 10.1177/02676591241237758
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Association between nebuliser therapies adherence and visit-to-visit variability of FEV1 in patients with cystic fibrosis.

    Drummond, David / Whelan, Pauline / Sperrin, Matthew

    Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society

    2023  Volume 22, Issue 4, Page(s) 702–705

    Abstract: At the same level of lung function, some patients with cystic fibrosis have large variations in their FEV1 percent predicted (FEV1pp) values while others have stable values. We hypothesised that lower adherence to nebuliser therapies was associated with ... ...

    Abstract At the same level of lung function, some patients with cystic fibrosis have large variations in their FEV1 percent predicted (FEV1pp) values while others have stable values. We hypothesised that lower adherence to nebuliser therapies was associated with higher FEV1pp variability. We conducted a post hoc analysis of the ACtiF trial data. Adherence was calculated using data from data-logging nebulisers, and FEV1pp variability using the coefficient of variation equation. Amongst the 543 patients included in the analysis, those poorly adherent (adherence < 50%) had a higher FEV1pp variability than patients moderately (50 to < 80%) and highly adherent (≥ 80%), with median values (IQR1-3) of 8.1% (4.9-13.7), 6.3% (3.9-9.8), and 6.3% (3.9-9.3) respectively (p < 0.01). This result was confirmed by a multiple linear regression including adherence as a continuous variable (p < 0.01). Further studies are needed to determine the implications of these differences in FEV1pp variability on the prognosis of patients.
    MeSH term(s) Humans ; Cystic Fibrosis/diagnosis ; Cystic Fibrosis/drug therapy ; Cystic Fibrosis/complications ; Forced Expiratory Volume ; Nebulizers and Vaporizers ; Respiratory Function Tests ; Respiratory Therapy
    Language English
    Publishing date 2023-03-14
    Publishing country Netherlands
    Document type Clinical Trial ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2084724-5
    ISSN 1873-5010 ; 1569-1993
    ISSN (online) 1873-5010
    ISSN 1569-1993
    DOI 10.1016/j.jcf.2023.03.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Cancer is becoming the leading cause of death in diabetes.

    Wang, Mengying / Sperrin, Matthew / Rutter, Martin K / Renehan, Andrew G

    Lancet (London, England)

    2023  Volume 401, Issue 10391, Page(s) 1849

    MeSH term(s) Humans ; Cause of Death ; Diabetes Mellitus ; Heart Diseases ; Neoplasms
    Language English
    Publishing date 2023-06-02
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 3306-6
    ISSN 1474-547X ; 0023-7507 ; 0140-6736
    ISSN (online) 1474-547X
    ISSN 0023-7507 ; 0140-6736
    DOI 10.1016/S0140-6736(23)00445-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

    Tsvetanova, Antonia / Sperrin, Matthew / Jenkins, David / Peek, Niels / Buchan, Iain / Hyland, Stephanie / Martin, Glen

    Studies in health technology and informatics

    2024  Volume 310, Page(s) 1476–1477

    Abstract: Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for ... ...

    Abstract Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.
    MeSH term(s) Computer Simulation ; Data Analysis
    Language English
    Publishing date 2024-01-10
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1879-8365
    ISSN (online) 1879-8365
    DOI 10.3233/SHTI231252
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care.

    Lin, Lijing / Poppe, Katrina / Wood, Angela / Martin, Glen P / Peek, Niels / Sperrin, Matthew

    Frontiers in epidemiology

    2024  Volume 4, Page(s) 1326306

    Abstract: Background: Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We ... ...

    Abstract Background: Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol.
    Methods: We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining existing prediction models with causal effects reported in published literature.
    Results: The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between a non-causal estimate of 2.5% and a causal estimate of 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate).
    Conclusions: Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.
    Language English
    Publishing date 2024-04-03
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2674-1199
    ISSN (online) 2674-1199
    DOI 10.3389/fepid.2024.1326306
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Prediction models for covid-19 outcomes.

    Sperrin, Matthew / McMillan, Brian

    BMJ (Clinical research ed.)

    2020  Volume 371, Page(s) m3777

    MeSH term(s) Betacoronavirus/isolation & purification ; COVID-19 ; Clinical Decision Rules ; Communicable Disease Control/statistics & numerical data ; Coronavirus Infections/diagnosis ; Coronavirus Infections/mortality ; Coronavirus Infections/psychology ; Health Behavior ; Humans ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/mortality ; Pneumonia, Viral/psychology ; Predictive Value of Tests ; Prevalence ; Prognosis ; Public Health/methods ; Reproducibility of Results ; Risk Assessment/methods ; Risk Assessment/standards ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-10-20
    Publishing country England
    Document type Editorial
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.m3777
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study.

    Sperrin, Matthew / Martin, Glen P

    BMC medical research methodology

    2020  Volume 20, Issue 1, Page(s) 185

    Abstract: Background: Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. ... ...

    Abstract Background: Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such information can introduce bias, its use in conjunction with multiple imputation may unlock the potential value of missingness to reduce bias in causal effect estimation, particularly in missing not at random scenarios and where missingness might be associated with unmeasured confounders.
    Methods: We conducted a simulation study to determine when the use of a missing indicator, combined with multiple imputation, would reduce bias for causal effect estimation, under a range of scenarios including unmeasured variables, missing not at random, and missing at random mechanisms. We use directed acyclic graphs and structural models to elucidate a variety of causal structures of interest. We handled missing data using complete case analysis, and multiple imputation with and without missing indicator terms.
    Results: We find that multiple imputation combined with a missing indicator gives minimal bias for causal effect estimation in most scenarios. In particular the approach: 1) does not introduce bias in missing (completely) at random scenarios; 2) reduces bias in missing not at random scenarios where the missing mechanism depends on the missing variable itself; and 3) may reduce or increase bias when unmeasured confounding is present.
    Conclusion: In the presence of missing data, careful use of missing indicators, combined with multiple imputation, can improve causal effect estimation when missingness is informative, and is not detrimental when missingness is at random.
    MeSH term(s) Bias ; Computer Simulation ; Data Interpretation, Statistical ; Humans
    Language English
    Publishing date 2020-07-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1471-2288
    ISSN (online) 1471-2288
    DOI 10.1186/s12874-020-01068-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study.

    Sisk, Rose / Sperrin, Matthew / Peek, Niels / van Smeden, Maarten / Martin, Glen Philip

    Statistical methods in medical research

    2023  Volume 32, Issue 8, Page(s) 1461–1477

    Abstract: Background: ...

    Abstract Background:
    MeSH term(s) Humans ; Data Interpretation, Statistical ; Computer Simulation ; Research Design ; Critical Care
    Language English
    Publishing date 2023-04-27
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/09622802231165001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Link Between Obesity and Early-Onset Colorectal Cancers (EOCRC): Importance of Accounting for BMI Trajectories in Early Life.

    Hawwash, Nadin / Martin, Glen P / Sperrin, Matthew / Renehan, Andrew G

    The American journal of gastroenterology

    2022  Volume 117, Issue 5, Page(s) 812

    MeSH term(s) Body Mass Index ; Colorectal Neoplasms/epidemiology ; Colorectal Neoplasms/etiology ; Humans ; Incidence ; Obesity/complications ; Obesity/epidemiology
    Language English
    Publishing date 2022-02-18
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 390122-1
    ISSN 1572-0241 ; 0002-9270
    ISSN (online) 1572-0241
    ISSN 0002-9270
    DOI 10.14309/ajg.0000000000001661
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Targeted validation: validating clinical prediction models in their intended population and setting.

    Sperrin, Matthew / Riley, Richard D / Collins, Gary S / Martin, Glen P

    Diagnostic and prognostic research

    2022  Volume 6, Issue 1, Page(s) 24

    Abstract: Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with ... ...

    Abstract Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We call estimating how well a model performs within the intended population/setting "targeted validation". Use of this term sharpens the focus on the intended use of a model, which may increase the applicability of developed models, avoid misleading conclusions, and reduce research waste. It also exposes that external validation may not be required when the intended population for the model matches the population used to develop the model; here, a robust internal validation may be sufficient, especially if the development dataset was large.
    Language English
    Publishing date 2022-12-22
    Publishing country England
    Document type Letter
    ISSN 2397-7523
    ISSN (online) 2397-7523
    DOI 10.1186/s41512-022-00136-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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