Publications by authors named "Glen P Martin"

49 Publications

A scoping review of causal methods enabling predictions under hypothetical interventions.

Diagn Progn Res 2021 Feb 4;5(1). Epub 2021 Feb 4.

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Background: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions.

Aims: We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges.

Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies.

Results: We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.

Conclusions: There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.
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http://dx.doi.org/10.1186/s41512-021-00092-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860039PMC
February 2021

Descriptive analysis of the context of child sexual abuse reportedly perpetrated by female suspects: Insights from Saint Mary's Sexual Assault Referral Centre.

J Forensic Leg Med 2021 Feb 7;78:102112. Epub 2021 Jan 7.

Manchester University Hospitals NHS Foundation Trust, UK. Electronic address:

Background: Determining the prevalence and characteristics of female-perpetrated child sexual abuse (CSA) is fraught with difficultly. There is a historical lack of empirical research and a discrepancy between the number of cases that reach the attention of the authorities and its suspected prevalence in society. It is also noted that for a myriad of reasons many CSA reports do not progress through the criminal justice process so many remain as allegations rather than proven or disproven crimes.

Objectives: The study set out to answer the research questions: 'What are the characteristics and context of CSA reportedly perpetrated by females, and what are the similarities and differences in the context of alleged CSA committed by male and female suspects?'

Participants And Setting: This study presents data from all service users aged 0-17 years (n = 986) that attended Saint Mary's Sexual Assault Referral Centre (SARC) for a forensic medical examination over a three-year period.

Methods: Data collection was performed retrospectively from the paper case files recorded at the time of attendance. Due to the small number of female suspects, analysis was restricted to frequency calculations.

Results: Results show females were reportedly involved in the alleged abuse of less than 4% of the children attending SARC. Females appeared more likely to be associated with the alleged abuse of younger children and abuse occurring within the child's home.

Conclusions: This study's most arresting feature is that despite the large number of CSA cases examined, it was rare to have a female suspect. This study demonstrates how much is still unknown about female-perpetrated CSA.
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http://dx.doi.org/10.1016/j.jflm.2020.102112DOI Listing
February 2021

Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?

Diagn Progn Res 2021 Jan 11;5(1). Epub 2021 Jan 11.

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment, and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad hoc manner. This fails to address the fact that the performance of a CPM worsens over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, "living" (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.
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http://dx.doi.org/10.1186/s41512-020-00090-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797885PMC
January 2021

Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient.

J Clin Epidemiol 2020 Dec 28;133:53-60. Epub 2020 Dec 28.

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. Electronic address:

Objective: Developing clinical prediction models (CPMs) on data of sufficient sample size is critical to help minimize overfitting. Using prostate cancer as a clinical exemplar, we aimed to investigate to what extent existing CPMs adhere to recent formal sample size criteria, or historic rules of thumb of events per predictor parameter (EPP)≥10.

Study Design And Setting: A systematic review to identify CPMs related to prostate cancer, which provided enough information to calculate minimum sample size. We compared the reported sample size of each CPM against the traditional 10 EPP rule of thumb and formal sample size criteria.

Results: About 211 CPMs were included. Three of the studies justified the sample size used, mostly using EPP rules of thumb. Overall, 69% of the CPMs were derived on sample sizes that surpassed the traditional EPP≥10 rule of thumb, but only 48% surpassed recent formal sample size criteria. For most CPMs, the required sample size based on formal criteria was higher than the sample sizes to surpass 10 EPP.

Conclusion: Few of the CPMs included in this study justified their sample size, with most justifications being based on EPP. This study shows that, in real-world data sets, adhering to the classic EPP rules of thumb is insufficient to adhere to recent formal sample size criteria.
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http://dx.doi.org/10.1016/j.jclinepi.2020.12.011DOI Listing
December 2020

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small.

J Clin Epidemiol 2020 Dec 8;132:88-96. Epub 2020 Dec 8.

Nuffield Department of Orthopaedics, Centre for Statistics in Medicine, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, OX3 7LD; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK.

Objectives: When developing a clinical prediction model, penalization techniques are recommended to address overfitting, as they shrink predictor effect estimates toward the null and reduce mean-square prediction error in new individuals. However, shrinkage and penalty terms ('tuning parameters') are estimated with uncertainty from the development data set. We examined the magnitude of this uncertainty and the subsequent impact on prediction model performance.

Study Design And Setting: This study comprises applied examples and a simulation study of the following methods: uniform shrinkage (estimated via a closed-form solution or bootstrapping), ridge regression, the lasso, and elastic net.

Results: In a particular model development data set, penalization methods can be unreliable because tuning parameters are estimated with large uncertainty. This is of most concern when development data sets have a small effective sample size and the model's Cox-Snell R is low. The problem can lead to considerable miscalibration of model predictions in new individuals.

Conclusion: Penalization methods are not a 'carte blanche'; they do not guarantee a reliable prediction model is developed. They are more unreliable when needed most (i.e., when overfitting may be large). We recommend they are best applied with large effective sample sizes, as identified from recent sample size calculations that aim to minimize the potential for model overfitting and precisely estimate key parameters.
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http://dx.doi.org/10.1016/j.jclinepi.2020.12.005DOI Listing
December 2020

A systematic review of risk prediction models for perioperative mortality after thoracic surgery.

Interact Cardiovasc Thorac Surg 2020 Nov 30. Epub 2020 Nov 30.

Division of Cardiovascular Sciences, University of Manchester, ERC, Manchester University Hospitals Foundation Trust, Manchester, UK.

Objectives: Guidelines advocate that patients being considered for thoracic surgery should undergo a comprehensive preoperative risk assessment. Multiple risk prediction models to estimate the risk of mortality after thoracic surgery have been developed, but their quality and performance has not been reviewed in a systematic way. The objective was to systematically review these models and critically appraise their performance.

Methods: The Cochrane Library and the MEDLINE database were searched for articles published between 1990 and 2019. Studies that developed or validated a model predicting perioperative mortality after thoracic surgery were included. Data were extracted based on the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies.

Results: A total of 31 studies describing 22 different risk prediction models were identified. There were 20 models developed specifically for thoracic surgery with two developed in other surgical specialties. A total of 57 different predictors were included across the identified models. Age, sex and pneumonectomy were the most frequently included predictors in 19, 13 and 11 models, respectively. Model performance based on either discrimination or calibration was inadequate for all externally validated models. The most recent data included in validation studies were from 2018. Risk of bias (assessed using Prediction model Risk Of Bias ASsessment Tool) was high for all except two models.

Conclusions: Despite multiple risk prediction models being developed to predict perioperative mortality after thoracic surgery, none could be described as appropriate for contemporary thoracic surgery. Contemporary validation of available models or new model development is required to ensure that appropriate estimates of operative risk are available for contemporary thoracic surgical practice.
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http://dx.doi.org/10.1093/icvts/ivaa273DOI Listing
November 2020

External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection.

Eur J Cardiothorac Surg 2020 Nov 24. Epub 2020 Nov 24.

Division of Cardiovascular Sciences, University of Manchester, ERC, Manchester University Hospitals Foundation Trust, Manchester, UK.

Objectives: National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort.

Methods: The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance.

Results: Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality.

Conclusions: Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.
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http://dx.doi.org/10.1093/ejcts/ezaa422DOI Listing
November 2020

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

J Am Med Inform Assoc 2021 Jan;28(1):155-166

Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom.

Objective: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.

Materials And Methods: A systematic literature search was conducted by 2 independent reviewers using prespecified keywords.

Results: Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).

Discussion: This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.

Conclusions: A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.
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http://dx.doi.org/10.1093/jamia/ocaa242DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810439PMC
January 2021

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches.

Stat Med 2021 Jan 26;40(2):498-517. Epub 2020 Oct 26.

Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi-outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real-world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.
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http://dx.doi.org/10.1002/sim.8787DOI Listing
January 2021

Performance of prediction models for COVID-19: the Caudine Forks of the external validation.

Eur Respir J 2020 12 24;56(6). Epub 2020 Dec 24.

Clinical Epidemiology and Medical Statistics Unit, Dept of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy.

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http://dx.doi.org/10.1183/13993003.03728-2020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562696PMC
December 2020

Adaptive Symptom Monitoring using Hidden Markov - an Application in Ecological Momentary Assessment.

IEEE J Biomed Health Inform 2020 Oct 15;PP. Epub 2020 Oct 15.

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability trade-off between collecting information at sufficient frequencies and quantities to be useful, and over-burdening the user or the underlying technology, particularly when active input is required from the user. Selecting the next sampling time adaptively using previous responses, so that people are only sampled at high frequency when necessary, can help to manage this trade-off. We present a novel approach to adaptive sampling using clustered continuous-time hidden Markov models. The model predicts, at any given sampling time, the probability of moving to an "alert" state, and the next sample time is scheduled when this probability has exceeded a given threshold. The clusters, each representing a distinct sub-model, allow heterogeneity in states and state transitions. The work is illustrated using longitudinal mentalhealth symptom data in 49 people collected using ClinTouch, a mobile app designed to monitor people with a diagnosis of schizophrenia. Using these data, we show how the adaptive sampling scheme behaves under different model parameters and risk thresholds, and how the average sampling can be substantially reduced whilst maintaining a high sampling frequency during high-risk periods.
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http://dx.doi.org/10.1109/JBHI.2020.3031263DOI Listing
October 2020

Impact of sample size on the stability of risk scores from clinical prediction models: a case study in cardiovascular disease.

Diagn Progn Res 2020 9;4:14. Epub 2020 Sep 9.

Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL UK.

Background: Stability of risk estimates from prediction models may be highly dependent on the sample size of the dataset available for model derivation. In this paper, we evaluate the stability of cardiovascular disease risk scores for individual patients when using different sample sizes for model derivation; such sample sizes include those similar to models recommended in the national guidelines, and those based on recently published sample size formula for prediction models.

Methods: We mimicked the process of sampling patients from a population to develop a risk prediction model by sampling patients from the Clinical Practice Research Datalink. A cardiovascular disease risk prediction model was developed on this sample and used to generate risk scores for an independent cohort of patients. This process was repeated 1000 times, giving a distribution of risks for each patient. = 100,000, 50,000, 10,000, (derived from sample size formula) and (meets 10 events per predictor rule) were considered. The 5-95th percentile range of risks across these models was used to evaluate instability. Patients were grouped by a risk derived from a model developed on the entire population (population-derived risk) to summarise results.

Results: For a sample size of 100,000, the median 5-95th percentile range of risks for patients across the 1000 models was 0.77%, 1.60%, 2.42% and 3.22% for patients with population-derived risks of 4-5%, 9-10%, 14-15% and 19-20% respectively; for = 10,000, it was 2.49%, 5.23%, 7.92% and 10.59%, and for using the formula-derived sample size, it was 6.79%, 14.41%, 21.89% and 29.21%. Restricting this analysis to models with high discrimination, good calibration or small mean absolute prediction error reduced the percentile range, but high levels of instability remained.

Conclusions: Widely used cardiovascular disease risk prediction models suffer from high levels of instability induced by sampling variation. Many models will also suffer from overfitting (a closely linked concept), but at acceptable levels of overfitting, there may still be high levels of instability in individual risk. Stability of risk estimates should be a criterion when determining the minimum sample size to develop models.
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http://dx.doi.org/10.1186/s41512-020-00082-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487849PMC
September 2020

Rotational atherectomy and same day discharge: Safety and growth from a national perspective.

Catheter Cardiovasc Interv 2020 Aug 26. Epub 2020 Aug 26.

Keele Cardiovascular Research Group, Keele University, Keele, UK.

Objectives: We explore whether same day discharge (SDD) is a feasible and safe practice following rotational atherectomy (ROTA) treatment during elective percutaneous coronary intervention (PCI), and examine which baseline characteristics are independently associated with SDD.

Background: SDD following elective ROTA PCI is not recommended as per the recent SCAI consensus. However, reports show it is practiced and no previous study has evaluated its safety and feasibility.

Methods: Our dataset included 4,591 patients undergoing elective ROTA PCI in England & Wales within an 8-years period. Independent associations with SDD were quantified via a multiple logistic regression model and the BCIS 30-day mortality risk model was used to evaluate the safety of SDD.

Results: The majority of elective ROTA PCI cases remain at the hospital for overnight (ON) observation, although SDD rates increased substantially from 6.7% in 2007 to 35.5% in 2014. The use of glycoprotein IIb/IIIa antagonists, Q wave AMI, left main PCI and valvular heart disease were independently associated with ON, while patients operated underwent transradial PCI were more likely to be SDD (OR = 1.77, 95% CI [1.45-2.15]). Over the study period, observed mortality rates were not significantly higher than those expected from the BCIS risk model.

Conclusions: Our findings did not show superiority of the ON strategy over SDD for higher risk cases undergoing elective ROTA PCI, in terms of 30-day mortality. This is the first study to examine the safety of SDD after elective ROTA PCI and more should follow.
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http://dx.doi.org/10.1002/ccd.29228DOI Listing
August 2020

Adoption of same day discharge following elective left main stem percutaneous coronary intervention.

Int J Cardiol 2020 Dec 30;321:38-47. Epub 2020 Jul 30.

Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK; Keele Cardiovascular Research Group, Institute of Primary Care and Health Sciences, University of Keele and Academic Department of Cardiology, Royal Stoke Hospital, Stoke-on-Trent, UK. Electronic address:

Background: This study sought to investigate the safety and feasibility of same day discharge (SDD) practice and compare clinical outcomes to patients admitted for overnight stay (ON) undergoing elective left main stem (LMS) percutaneous coronary intervention (PCI). ON observation is still widely practiced in highly complex PCI as the standard of care, with no previous data comparing clinical outcomes in patients undergoing LMS PCI.

Methods: We analysed 6452 patients undergoing elective LMS PCI between 2007 and 2014 in England and Wales. Multiple logistic regressions and the BCIS risk model were used to study association between SDD and 30 day mortality.

Results: SDD rates almost doubled from 19.9% in 2007 to 39.8% in 2014 for all LMS procedures and increased from 20.7% to 41.4% for unprotected LMS cases during the same study period. There was a significant increase in procedural complexity with higher use of rotational atherectomy, longer stents and multivessel PCI. SDD was not associated with increased 30 day mortality (OR 0.70 95%CI 0.30-1.65) in the overall LMS PCI cohort and the results were similar in unprotected LMS (OR 0.48 95%CI 0.17-1.41) and those requiring ON stay (OR 0.58 95%CI 0.25-1.34).

Conclusions: We did not find evidence that SDD is not safe or feasible in highly complex LMS PCI procedures despite increasing procedural complexity with no significant increase in 30 day mortality rates.
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http://dx.doi.org/10.1016/j.ijcard.2020.07.038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392050PMC
December 2020

Toward a framework for the design, implementation, and reporting of methodology scoping reviews.

J Clin Epidemiol 2020 11 26;127:191-197. Epub 2020 Jul 26.

Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester, UK.

Background And Objective: In view of the growth of published articles, there is an increasing need for studies that summarize scientific research. An increasingly common review is a "methodology scoping review," which provides a summary of existing analytical methods, techniques and software that have been proposed or applied in research articles to address an analytical problem or further an analytical approach. However, guidelines for their design, implementation, and reporting are limited.

Methods: Drawing on the experiences of the authors, which were consolidated through a series of face-to-face workshops, we summarize the challenges inherent in conducting a methodology scoping review and offer suggestions of best practice to promote future guideline development.

Results: We identified three challenges of conducting a methodology scoping review. First, identification of search terms; one cannot usually define the search terms a priori, and the language used for a particular method can vary across the literature. Second, the scope of the review requires careful consideration because new methodology is often not described (in full) within abstracts. Third, many new methods are motivated by a specific clinical question, where the methodology may only be documented in supplementary materials. We formulated several recommendations that build upon existing review guidelines. These recommendations ranged from an iterative approach to defining search terms through to screening and data extraction processes.

Conclusion: Although methodology scoping reviews are an important aspect of research, there is currently a lack of guidelines to standardize their design, implementation, and reporting. We recommend a wider discussion on this topic.
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http://dx.doi.org/10.1016/j.jclinepi.2020.07.014DOI Listing
November 2020

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.

Diagn Progn Res 2020 9;4. Epub 2020 Jul 9.

Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.

Background: Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers.

Methods: MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method.

Results: The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change.

Conclusions: The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.
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http://dx.doi.org/10.1186/s41512-020-00078-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346415PMC
July 2020

Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study.

BMC Med Res Methodol 2020 07 8;20(1):185. Epub 2020 Jul 8.

Faculty of Biology, Medicine and Health, Vaughan House, University of Manchester, Manchester, M13 9PL, UK.

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.
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http://dx.doi.org/10.1186/s12874-020-01068-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346454PMC
July 2020

Pride and prejudice - What can we learn from peer review?

Med Teach 2020 09 6;42(9):1012-1018. Epub 2020 Jul 6.

Centre for Health Informatics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Peer review is a powerful tool that steers the education and practice of medical researchers but may allow biased critique by anonymous reviewers. We explored factors unrelated to research quality that may influence peer review reports, and assessed the possibility that sub-types of reviewers exist. Our findings could potentially improve the peer review process. We evaluated the harshness, constructiveness and positiveness in 596 reviews from journals with open peer review, plus 46 reviews from colleagues' anonymously reviewed manuscripts. We considered possible influencing factors, such as number of authors and seasonal trends, on the content of the review. Finally, using machine-learning we identified latent types of reviewer with differing characteristics. Reviews provided during a northern-hemisphere winter were significantly harsher, suggesting a seasonal effect on language. Reviews for articles in journals with an open peer review policy were significantly less harsh than those with an anonymous review process. Further, we identified three types of reviewers: nurturing, begrudged, and blasé. Nurturing reviews were in a minority and our findings suggest that more widespread open peer reviewing could improve the educational value of peer review, increase the constructive criticism that encourages researchers, and reduce pride and prejudice in editorial processes.
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http://dx.doi.org/10.1080/0142159X.2020.1774527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497287PMC
September 2020

Missing data should be handled differently for prediction than for description or causal explanation.

J Clin Epidemiol 2020 09 12;125:183-187. Epub 2020 Jun 12.

Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Missing data are much studied in epidemiology and statistics. Theoretical development and application of methods for handling missing data have mostly been conducted in the context of prospective research data and with a goal of description or causal explanation. However, it is now common to build predictive models using routinely collected data, where missing patterns may convey important information, and one might take a pragmatic approach to optimizing prediction. Therefore, different methods to handle missing data may be preferred. Furthermore, an underappreciated issue in prediction modeling is that the missing data method used in model development may not match the method used when a model is deployed. This may lead to overoptimistic assessments of model performance. For prediction, particularly with routinely collected data, methods for handling missing data that incorporate information within the missingness pattern should be explored and further developed. Where missing data methods differ between model development and model deployment, the implications of this must be explicitly evaluated. The trade-off between building a prediction model that is causally principled, and building a prediction model that maximizes the use of all available information, should be carefully considered and will depend on the intended use of the model.
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http://dx.doi.org/10.1016/j.jclinepi.2020.03.028DOI Listing
September 2020

Development and validation of a multivariable prediction model for infection-related complications in patients with common infections in UK primary care and the extent of risk-based prescribing of antibiotics.

BMC Med 2020 05 21;18(1):118. Epub 2020 May 21.

Health e-Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.

Background: Antimicrobial resistance is driven by the overuse of antibiotics. This study aimed to develop and validate clinical prediction models for the risk of infection-related hospital admission with upper respiratory infection (URTI), lower respiratory infection (LRTI) and urinary tract infection (UTI). These models were used to investigate whether there is an association between the risk of an infection-related complication and the probability of receiving an antibiotic prescription.

Methods: The study used electronic health record data from general practices contributing to the Clinical Practice Research Datalink (CPRD GOLD) and Welsh Secure Anonymised Information Linkage (SAIL), both linked to hospital records. Patients who visited their general practitioner with an incidental URTI, LRTI or UTI were included and followed for 30 days for hospitalisation due to infection-related complications. Predictors included age, gender, clinical and medication risk factors, ethnicity and socioeconomic status. Cox proportional hazards regression models were used with predicted risks independently validated in SAIL.

Results: The derivation and validation cohorts included 8.1 and 2.7 million patients in CPRD and SAIL, respectively. A total of 7125 (0.09%) hospital admissions occurred in CPRD and 7685 (0.28%) in SAIL. Important predictors included age and measures of comorbidity. Initial attempts at validating in SAIL (i.e. transporting the models with no adjustment) indicated the need to recalibrate the models for age and underlying incidence of infections; internal bootstrap validation of these updated models yielded C-statistics of 0.63 (LRTI), 0.69 (URTI) and 0.73 (UTI) indicating good calibration. For all three infection types, the rate of antibiotic prescribing was not associated with patients' risk of infection-related hospital admissions.

Conclusion: The risk for infection-related hospital admissions varied substantially between patients, but prescribing of antibiotics in primary care was not associated with risk of hospitalisation due to infection-related complications. Our findings highlight the potential role of clinical prediction models to help inform decisions of prescribing of antibiotics in primary care.
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http://dx.doi.org/10.1186/s12916-020-01581-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240993PMC
May 2020

Sex Disparities in the Choice of Cardiac Resynchronization Therapy Device: An Analysis of Trends, Predictors, and Outcomes.

Can J Cardiol 2021 Jan 20;37(1):86-93. Epub 2020 Feb 20.

Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Keele, United Kingdom; Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom; Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom. Electronic address:

Background: There is limited evidence on the influence of sex on the decision to implant a cardiac resynchronization therapy device with pacemaker (CRT-P) or defibrillator (CRT-D) and the existence of sex-dependent differences in complications that may affect this decision.

Methods: All patients undergoing de novo CRT implantation (2004-2014) in the United States National Inpatient Sample were included and stratified by device type (CRT-P and CRT-D). Multivariable logistic regression models were conducted to assess the association of female sex with receipt of CRT-D and periprocedural complications.

Results: Out of 400,823 weighted CRT procedural records, the overall percentages of women undergoing CRT-P and CRT-D implantations were 41.5% and 27.8%, respectively, and these percentages increased compared with men over the study period. Women were less likely to receive CRT-D (odds ratio 0.66, 95% confidence interval 0.64-0.67), and this trend remained stable throughout the study period (P = 0.06). Furthermore, compared with men, women were associated with increased odds of procedure-related complications (bleeding, thoracic, and cardiac) in the CRT-D group but not in the CRT-P group. Factors such as atrial fibrillation, malignancies, renal failure, advanced age (> 60 years), and admission to nonurban/small hospitals favoured the receipt of CRT-P over CRT-D, whereas history of ischemic heart disease, cardiac arrest ,or ventricular arrhythmias favoured the receipt of CRT-D over CRT-P.

Conclusions: Women were associated with persistently reduced odds of receipt of CRT-D compared with men over an 11-year period. This study identifies important factors that predict the choice of CRT device offered to patients in the United States.
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http://dx.doi.org/10.1016/j.cjca.2020.02.073DOI Listing
January 2021

Prevalence and in-hospital outcomes of patients with malignancies undergoing de novo cardiac electronic device implantation in the USA.

Europace 2020 07;22(7):1083-1096

Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Keele, UK.

Aims: To study the outcomes of cancer patients undergoing cardiac implantable electronic device (CIED) implantation.

Methods And Results: De novo CIED implantations (2004-15; n = 2 670 590) from the National Inpatient Sample were analysed for characteristics and in-hospital outcomes, stratified by presence of cancer (no cancer, historical and current cancers) and further by current cancer type (haematological, lung, breast, colon, and prostate). Current and historical cancer prevalence has increased from 3.3% to 7.8%, and 5.8% to 7.8%, respectively, between 2004 and 2015. Current cancer was associated with increased adjusted odds ratio (OR) of major adverse cardiovascular events (MACE) [composite of all-cause mortality, thoracic and cardiac complications, and device-related infection; OR 1.26, 95% confidence interval (CI) 1.23-1.30], all-cause mortality (OR 1.43, 95% CI 1.35-1.50), major bleeding (OR 1.38, 95% CI 1.32-1.44), and thoracic complications (OR 1.39, 95% CI 1.35-1.43). Differences in outcomes were observed according to cancer type, with significantly worse MACE, mortality and thoracic complications with lung and haematological malignancies, and increased major bleeding in colon and prostate malignancies. The risk of complications was also different according to CIED subtype.

Conclusion: The prevalence of cancer patients amongst those undergoing CIED implantation has significantly increased over 12 years. Overall, current cancers are associated with increased mortality and worse outcomes, especially in patients with lung, haematological, and colon malignancies whereas there was no evidence that historical cancer had a negative impact on outcomes.
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http://dx.doi.org/10.1093/europace/euaa087DOI Listing
July 2020

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal.

BMJ 2020 04 7;369:m1328. Epub 2020 Apr 7.

Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands

Objective: To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease.

Design: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

Data Sources: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

Study Selection: Studies that developed or validated a multivariable covid-19 related prediction model.

Data Extraction: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

Results: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

Conclusion: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

Systematic Review Registration: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

Readers' Note: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity..
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http://dx.doi.org/10.1136/bmj.m1328DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222643PMC
April 2020

Calculating the sample size required for developing a clinical prediction model.

BMJ 2020 03 18;368:m441. Epub 2020 Mar 18.

Julius Center for Health Sciences, University Medical Center Utrecht, Utrecht, Netherlands.

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http://dx.doi.org/10.1136/bmj.m441DOI Listing
March 2020

Contributors to the Growth of Same Day Discharge After Elective Percutaneous Coronary Intervention.

Circ Cardiovasc Interv 2020 03 10;13(3):e008458. Epub 2020 Mar 10.

Keele Cardiovascular Research Group, Institute of Primary Care and Health Sciences, University of Keele (T.K., A.S., M.R., M.A.M.).

Background: Financial pressures for reducing hospitalization costs have driven to a move toward same day discharge (SDD) following uncomplicated percutaneous coronary intervention. The UK healthcare system has transitioned to predominantly SDD for elective percutaneous coronary intervention. This study aimed to examine patient's clinical, procedural, and institutional characteristics that are associated with the increased adoption of SDD adoption over time in the United Kingdom and determine whether these vary by region.

Methods: The data were derived from the British Cardiovascular Intervention Society including all the elective percutaneous coronary intervention from 2007 to 2014 in the United Kingdom. We structured 8 meaningful groups of variables, and their relative importance was obtained by decomposing the R in each study year.

Results: The relative importance of Strategic Health Authorities was substantially higher than all other factors every year, with some reduction over time, from 49.2% (95% CI, 45.4%-52.4%) in 2007 to 43.4% (95% CI, 39.9%-46.6%) in 2014. Center volume followed with 8.95% (95% CI, 7.0%-10.9%) to 19.8% (95% CI, 16.7%-22.4%). Between patients' clinical and procedural characteristics, pharmacology and access site had the highest relative importance values, from 14.3% (95% CI, 12.1%-16.4%) to 7.1% (95% CI, 5.5%-8.8%) and from 3.6% (95% CI, 2.3%-5.1%) to 11.8% (95% CI, 9.4%-14.3%), respectively. Relative importance of different groups varied differently across Strategic Health Authorities.

Conclusions: Growth of SDD was mainly associated with regional characteristics, while subcontributors varied substantially between different regions. Standardized guidelines would provide more homogenous adoption of SDD nationally. This analysis might be of wider interest in healthcare systems slower in SDD adoption.
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http://dx.doi.org/10.1161/CIRCINTERVENTIONS.119.008458DOI Listing
March 2020

Sex differences in rates and causes of 30-day readmissions after cardiac electronic device implantations: insights from the Nationwide Readmissions Database.

Int J Cardiol 2020 03 10;302:67-74. Epub 2019 Dec 10.

Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, UK; Department of Cardiology, Royal Stoke University Hospital, Stoke-on-Trent, UK; Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. Electronic address:

Background: Women undergoing cardiac implantable electronic device (CIED) implantation are at a higher risk of procedure-related complications. The present study examined sex differences in rates and causes of 30-day readmissions following CIED implantation.

Methods: Using the United States Nationwide Readmissions Database (NRD), all adults who had undergone CIED implantation (cardiac resynchronization therapy (CRT), permanent pacemakers (PPM) and implantable cardioverter defibrillators (ICD)) between January 2010 and September 2015 were included. We compared rates, trends and causes of 30-day readmissions between sexes, and examined associations between sex and outcomes (adjusted odds ratios (aOR) and 95% confidence intervals (CI)).

Results: Out of 1,155,992 index hospitalizations for CIED implantation, 43.1% of the patients were women. All-cause 30-day readmissions were persistently higher in women than men but declined in both sexes over the study period, more so in women (women vs. men; 2010: 15.0% vs. 14.1%; 2015: 13.7% vs.13.4%). Women were at higher odds of readmission due to cardiac (aOR 1.22, 95%CI 1.20-1.24) and device-related complications (aOR 1.18, 95%CI 1.15-1.20) compared to men, but no difference odds of all-cause readmission were found between sexes (women: aOR 0.998, 95%CI 0.997-1.008). The most common cardiac and non-cardiac causes of readmission were heart failure and infection, respectively, and these were similar in both sexes (men vs. women: 17.8% vs. 17.6% and 10.7% vs. 10.8%, respectively).

Conclusion: Women are persistently at higher risk of readmission due to cardiac causes and device-related complications compared to men over a six-year period, but no difference in all-cause readmissions was found between sexes.
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http://dx.doi.org/10.1016/j.ijcard.2019.12.012DOI Listing
March 2020

Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease.

Int J Med Inform 2020 01 11;133:104033. Epub 2019 Nov 11.

Health e-Research Centre, Farr Institute, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre (MAHSC), Oxford Road, Manchester, M13 9PL, UK; Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Alan Turing Institute, Headquartered at the British Library, London, UK. Electronic address:

Objective: To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3).

Design: Longitudinal cohort study.

Settings: 392 general practices (including 3.6 million patients) linked to hospital admission data.

Methods: Variation in data quality was assessed using Sáez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices.

Results: There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients.

Conclusions: The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.
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http://dx.doi.org/10.1016/j.ijmedinf.2019.104033DOI Listing
January 2020

Relation of Frailty to Outcomes in Percutaneous Coronary Intervention.

Cardiovasc Revasc Med 2020 07 15;21(7):811-818. Epub 2019 Nov 15.

Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, United Kingdom; Department of Cardiology, Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom; Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom. Electronic address:

There is growing awareness that frailty may be an important marker of adverse outcomes in PCI but there is no literature from national cohorts. This study examines a national cohort of patients who underwent percutaneous coronary intervention (PCI) regarding the prevalence of frailty, changes over time, and associated outcomes. The National Inpatients Sample was used to identify adults who underwent PCI procedures between 2004 and 2014. Frailty risk was measured using a validated Hospital Frailty Risk Score (HFRS) using the cutoffs <5, 5-15 and >15 for low, intermediate and high HFRS. From 7,306,007 admissions, a total of 94.58% of admissions were for patients who had a low HFRS(<5), 5.39% had an intermediate HFRS(5-15) and 0.03% had a high HFRS(>15). The prevalence of intermediate or high frailty risk patients has increased over time from 1.9% in 2004 to 11.7% in 2014. The incidence of in-hospital death increased from 1.0% with low HFRS to 13.9% with high HFRS. Mean length of stay also increased from 2.9 days to 17.1 days from low to high HFRS. High frailty risk was independently associated with an OR 9.91 95%CI 7.17-13.71 for in-hospital death, OR 4.99 95%CI 3.82-6.51 for bleeding and OR 3.96 95%CI 3.00-5.23 for vascular injury as compared to patients with low risk of HFRS. While rare in frequency overall, frailty is increasing in prevalence in recent years and intermediate and high HFRS associated with increased odds of mortality compared to low risk of frailty.
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http://dx.doi.org/10.1016/j.carrev.2019.11.009DOI Listing
July 2020

Trends of Sex Differences in Outcomes of Cardiac Electronic Device Implantations in the United States.

Can J Cardiol 2020 01 22;36(1):69-78. Epub 2019 Aug 22.

Keele Cardiovascular Research Group, Centre for Prognosis Research, Institutes of Applied Clinical Science and Primary Care and Health Sciences, Keele University, Stoke-on-Trent, Staffordshire, United Kingdom; Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom. Electronic address:

Background: The disparity in outcomes of cardiac electronic device implantations between sexes has been previously demonstrated in device-specific cohorts (eg, implantable cardioverter-defibrillators [ICDs]). However, it is unclear whether sex differences are present with all types of cardiac implantable electronic devices (CIEDs) and, if so, what the trends of such differences have been in recent years.

Methods: With the use of the National Inpatient Sample, all hospitalizations from 2004 to 2014 for de novo implantation of permanent pacemakers, cardiac resynchronization therapy with or without a defibrillator, and ICDs were analyzed to examine the association between sex and in-hospital acute complications of CIED implantation.

Results: Out of 2,815,613 hospitalizations for de novo CIED implantation, 41.9% were performed on women. Women were associated with increased adjusted odds (95% confidence interval) of adverse procedural complications (major adverse cardiovascular complications: 1.17 [1.16-1.19]; bleeding: 1.13 [1.12-1.15],-thoracic: 1.42 [1.40-1.44]; cardiac: 1.44 [1.38-1.50]), whereas the adjusted odds of in-hospital all-cause mortality compared with men was 0.96 (0.94-1.00). The odds of adverse complications in the overall CIED cohort were persistently raised in women throughout the study period, whereas similar odds of all-cause mortality across the sexes were observed throughout the study period.

Conclusion: In a national cohort of CIED implantations we demonstrate that women are at an overall higher risk of procedure-related adverse events compared with men, but not at increased risk of all-cause mortality. Further studies are required to identify procedural techniques that would improve outcomes among women undergoing such procedures.
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http://dx.doi.org/10.1016/j.cjca.2019.08.012DOI Listing
January 2020