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BACKGROUND: Patients who suffer from chronic conditions or diseases are susceptible to experiencing repeated events of the same type (e.g. seizures), termed 'recurrent events'. Prediction models can be used to predict the risk of recurrence so that intervention or management can be tailored accordingly, but statistical methodology can vary. The objective of this systematic review was to identify and describe statistical approaches that have been applied for the development and validation of multivariable prediction models with recurrent event data. A secondary objective was to informally assess the characteristics and quality of analysis approaches used in the development and validation of prediction models of recurrent event data. METHODS: Searches were run in MEDLINE using a search strategy in 2019 which included index terms and phrases related to recurrent events and prediction models. For studies to be included in the review they must have developed or validated a multivariable clinical prediction model for recurrent event outcome data, specifically modelling the recurrent events and the timing between them. The statistical analysis methods used to analyse the recurrent event data in the clinical prediction model were extracted to answer the primary aim of the systematic review. In addition, items such as the event rate as well as any discrimination and calibration statistics that were used to assess the model performance were extracted for the secondary aim of the review. RESULTS: A total of 855 publications were identified using the developed search strategy and 301 of these are included in our systematic review. The Andersen-Gill method was identified as the most commonly applied method in the analysis of recurrent events, which was used in 152 (50.5%) studies. This was closely followed by frailty models which were used in 116 (38.5%) included studies. Of the 301 included studies, only 75 (24.9%) internally validated their model(s) and three (1.0%) validated their model(s) in an external dataset. CONCLUSIONS: This review identified a variety of methods which are used in practice when developing or validating prediction models for recurrent events. The variability of the approaches identified is cause for concern as it indicates possible immaturity in the field and highlights the need for more methodological research to bring greater consistency in approach of recurrent event analysis. Further work is required to ensure publications report all required information and use robust statistical methods for model development and validation. PROSPERO REGISTRATION: CRD42019116031.
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BACKGROUND: Heart failure (HF) most commonly occurs in patients who have had a myocardial infarction (MI), but factors other than MI size may be deterministic. Fibrosis of myocardium remote from the MI is associated with adverse remodeling. We aimed to 1) investigate the association between remote myocardial fibrosis, measured using cardiovascular magnetic resonance (CMR) extracellular volume fraction (ECV), and HF and death following MI, 2) identify predictors of remote myocardial fibrosis in patients with evidence of MI and determine the relationship with infarct size. METHODS: Multicenter prospective cohort study of 1199 consecutive patients undergoing CMR with evidence of MI on late gadolinium enhancement. Median follow-up was 1133 (895-1442) days. Cox proportional hazards modeling was used to identify factors predictive of the primary outcome, a composite of first hospitalization for HF (HHF) or all-cause mortality, post-CMR. Linear regression modeling was used to identify determinants of remote ECV. RESULTS: Remote myocardial fibrosis was a strong predictor of primary outcome (χ2: 15.6, hazard ratio [HR]: 1.07 per 1% increase in ECV, 95% confidence interval [CI]: 1.04-1.11, p < 0.001) and was separately predictive of both HHF and death. The strongest predictors of remote ECV were diabetes, sex, natriuretic peptides, and body mass index, but, despite extensive phenotyping, the adjusted model R2 was only 0.283. The relationship between infarct size and remote fibrosis was very weak. CONCLUSION: Myocardial fibrosis, measured using CMR ECV, is a strong predictor of HHF and death in patients with evidence of MI. The mechanisms underlying remote myocardial fibrosis formation post-MI remain poorly understood, but factors other than infarct size appear to be important.
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Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug-resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow-up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.
Subject(s)
Epilepsy , Humans , Treatment Outcome , Epilepsy/diagnosis , Epilepsy/drug therapy , Drug Resistant Epilepsy/diagnosis , Drug Resistant Epilepsy/drug therapy , Anticonvulsants/therapeutic use , PrognosisABSTRACT
PURPOSE: Suspected seizures present challenges for ambulance services, with paramedics reporting uncertainty over whether or not to convey individuals to emergency departments. The Risk of ADverse Outcomes after a Suspected Seizure (RADOSS) project aims to address this by developing a risk assessment tool utilizing structured patient care record and dispatch data. It proposes a tool that would provide estimates of an individual's likelihood of death and/or recontact with emergency care within 3 days if conveyed compared to not conveyed, and the likelihood of an 'avoidable attendance' occurring if conveyed. Knowledge Exchange workshops engaged stakeholders to resolve key design uncertainties before model derivation. METHOD: Six workshops involved 26 service users and their significant others (epilepsy or nonepileptic attack disorder), and 25 urgent and emergency care clinicians from different English ambulance regions. Utilizing Nominal Group Techniques, participants shared views of the proposed tool, benefits and concerns, suggested predictors, critiqued outcome measures, and expressed functionality preferences. Data were analysed using Hamilton's Rapid Analysis. RESULTS: Stakeholders supported tool development, proposing 10 structured variables for predictive testing. Emphasis was placed on the tool supporting, not dictating, care decisions. Participants highlighted some reasons why RADOSS might struggle to derive a predictive model based on structured data alone and suggested some non-structured variables for future testing. Feedback on prediction timeframes for service recontact was received, along with advice on amending the 'avoidable attendance' definition to prevent the tool's predictions being undermined by potential overuse of certain investigations in hospital. CONCLUSION: Collaborative stakeholder engagement provided crucial insights that can guide RADOSS to develop a user-aligned, optimized tool.
Subject(s)
Emergency Medical Services , Humans , Emergency Medical Services/methods , Ambulances , Emergency Service, Hospital , Seizures/diagnosis , Seizures/therapy , Risk AssessmentABSTRACT
OBJECTIVE: Guidelines suggest considering antiseizure medication (ASM) discontinuation in seizure-free patients with epilepsy. Past work has poorly explored how discontinuation effects vary between patients. We evaluated (1) what factors modify the influence of discontinuation on seizure risk; and (2) the range of seizure risk increase due to discontinuation across low- versus high-risk patients. METHODS: We pooled three datasets including seizure-free patients who did and did not discontinue ASMs. We conducted time-to-first-seizure analyses. First, we evaluated what individual patient factors modified the relative effect of ASM discontinuation on seizure risk via interaction terms. Then, we assessed the distribution of 2-year risk increase as predicted by our adjusted logistic regressions. RESULTS: We included 1626 patients, of whom 678 (42%) planned to discontinue all ASMs. The mean predicted 2-year seizure risk was 43% [95% confidence interval (CI) 39%-46%] for discontinuation versus 21% (95% CI 19%-24%) for continuation. The mean 2-year absolute seizure risk increase was 21% (95% CI 18%-26%). No individual interaction term was significant after correcting for multiple comparisons. The median [interquartile range (IQR)] risk increase across patients was 19% (IQR 14%-24%; range 7%-37%). Results were unchanged when restricting analyses to only the two RCTs. SIGNIFICANCE: No single patient factor significantly modified the influence of discontinuation on seizure risk, although we captured how absolute risk increases change for patients that are at low versus high risk. Patients should likely continue ASMs if even a 7% 2-year increase in the chance of any more seizures would be too much and should likely discontinue ASMs if even a 37% risk increase would be too little. In between these extremes, individualized risk calculation and a careful understanding of patient preferences are critical. Future work will further develop a two-armed individualized seizure risk calculator and contextualize seizure risk thresholds below which to consider discontinuation. PLAIN LANGUAGE SUMMARY: Understanding how much antiseizure medications (ASMs) decrease seizure risk is an important part of determining which patients with epilepsy should be treated, especially for patients who have not had a seizure in a while. We found that there was a wide range in the amount that ASM discontinuation increases seizure risk-between 7% and 37%. We found that no single patient factor modified that amount. Understanding what a patient's seizure risk might be if they discontinued versus continued ASM treatment is critical to making informed decisions about whether the benefit of treatment outweighs the downsides.
Subject(s)
Epilepsy , Seizures , Humans , Seizures/drug therapy , Epilepsy/drug therapy , Decision Making , Patient Preference , PatientsABSTRACT
OBJECTIVE: Identification of patients at risk of adverse outcome from heart failure (HF) at an early stage is a priority. Growth differentiation factor (GDF)-15 has emerged as a potentially useful biomarker. This study sought to identify determinants of circulating GDF-15 and evaluate its prognostic value, in patients at risk of HF or with HF but before first hospitalisation. METHODS: Prospective, longitudinal cohort study of 2166 consecutive patients in stage A-C HF undergoing cardiovascular magnetic resonance and measurement of GDF-15. Multivariable linear regression investigated determinants of GDF-15. Cox proportional hazards modelling, Net Reclassification Improvement and decision curve analysis examined its incremental prognostic value. Primary outcome was a composite of first hospitalisation for HF or all-cause mortality. Median follow-up was 1093 (939-1231) days. RESULTS: Major determinants of GDF-15 were age, diabetes and N-terminal pro-B-type natriuretic peptide, although despite extensive phenotyping, only around half of the variability of GDF-15 could be explained (R2 0.51). Log-transformed GDF-15 was the strongest predictor of outcome (HR 2.12, 95% CI 1.71 to 2.63) and resulted in a risk prediction model with higher predictive accuracy (continuous Net Reclassification Improvement 0.26; 95% CI 0.13 to 0.39) and with greater clinical net benefit across the entire range of threshold probabilities. CONCLUSION: In patients at risk of HF, or with HF but before first hospitalisation, GDF-15 provides unique information and is highly predictive of hospitalisation for HF or all-cause mortality, leading to more accurate risk stratification that can improve clinical decision making. TRIAL REGISTRATION NUMBER: NCT02326324.
Subject(s)
Growth Differentiation Factor 15 , Heart Failure , Humans , Prospective Studies , Longitudinal Studies , Heart Failure/diagnosis , Heart Failure/therapy , Prognosis , BiomarkersABSTRACT
Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.
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INTRODUCTION: Ambulances services are asked to further reduce avoidable conveyances to emergency departments (EDs). Risk of Adverse Outcomes after a Suspected Seizure seeks to support this by: (1) clarifying the risks of conveyance and non-conveyance, and (2) developing a risk prediction tool for clinicians to use 'on scene' to estimate the benefits an individual would receive if conveyed to ED and risks if not. METHODS AND ANALYSIS: Mixed-methods, multi-work package (WP) project. For WP1 and WP2 we shall use an existing linked data set that tracks urgent and emergency care (UEC) use of persons served by one English regional ambulance service. Risk tools are specific to clinical scenarios. We shall use suspected seizures in adults as an exemplar.WP1: Form a cohort of patients cared for a seizure by the service during 2019/2020. It, and nested Knowledge Exchange workshops with clinicians and service users, will allow us to: determine the proportions following conveyance and non-conveyance that die and/or recontact UEC system within 3 (/30) days; quantify the proportion of conveyed incidents resulting in 'avoidable ED attendances' (AA); optimise risk tool development; and develop statistical models that, using information available 'on scene', predict the risk of death/recontact with the UEC system within 3 (/30) days and the likelihood of an attendance at ED resulting in an AA.WP2: Form a cohort of patients cared for a seizure during 2021/2022 to 'temporally' validate the WP1 predictive models.WP3: Complete the 'next steps' workshops with stakeholders. Using nominal group techniques, finalise plans to develop the risk tool for clinical use and its evaluation. ETHICS AND DISSEMINATION: WP1a and WP2 will be conducted under database ethical approval (IRAS 307353) and Confidentiality Advisory Group (22/CAG/0019) approval. WP1b and WP3 have approval from the University of Liverpool Central Research Ethics Committee (11450). We shall engage in proactive dissemination and knowledge mobilisation to share findings with stakeholders and maximise evidence usage.
Subject(s)
Ambulances , Emergency Medical Services , Humans , Adult , Emergency Medical Services/methods , Seizures/diagnosis , Emergency Treatment , Hospitals , Emergency Service, HospitalABSTRACT
BACKGROUND: Rectal cancer has a high prevalence. The standard of care for management of localised disease involves major surgery and/or chemoradiotherapy, but these modalities are sometimes associated with mortality and morbidity. The notion of 'watch and wait' has therefore emerged and offers an organ-sparing approach to patients after administering a less invasive initial treatment, such as X-ray brachytherapy (Papillon technique). It is thus important to evaluate how likely patients are to respond to such therapies, to develop patient-tailored treatment pathways. We propose a systematic review to identify published clinical prediction models of the response of rectal cancer to treatment that includes radiotherapy and here present our protocol. METHODS: Included studies will develop multivariable clinical prediction models which assess response to treatment and overall survival of adult patients who have been diagnosed with any stage of rectal cancer and have received radiotherapy treatment with curative intent. Cohort studies and randomised controlled trials will be included. The primary outcome will be the occurrence of salvage surgery at 1 year after treatment. Secondary outcomes include salavage surgery at at any reported time point, the predictive accuracy of models, the quality of the developed models and the feasibility of using the model in clinical practice. Ovid MEDLINE, PubMed, Cochrane Library, EMBASE and CINAHL will be searched from inception to 24 February 2022. Keywords and phrases related to rectal cancer, radiotherapy and prediction models will be used. Studies will be selected once the deduplication, title, abstract and full-text screening process have been completed by two independent reviewers. The PRISMA-P checklist will be followed. A third reviewer will resolve any disagreement. The data extraction form will be pilot-tested using a representative 5% sample of the studies reviewed. The CHARMS checklist will be implemented. Risk of bias in each study will be assessed using the PROBAST tool. A narrative synthesis will be performed and if sufficient data are identified, meta-analysis will be undertaken as described in Debray et al. DISCUSSION: This systematic review will identify factors that predict response to the treatment protocol. Any gaps for potential development of new clinical prediction models will be highlighted. TRIAL REGISTRATION: CRD42022277704.
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BACKGROUND: The cardiac manifestations of Fabry disease are the leading cause of death, but risk stratification remains inadequate. Identifying patients who are at risk of adverse cardiac outcome may facilitate more evidence-based treatment guidance. Contemporary cardiovascular cardiac magnetic resonance biomarkers have become widely adopted, but their prognostic value remains unclear. OBJECTIVES: The objective of this study was to develop, internally validate, and evaluate the performance of, a prognostic model, including contemporary deep phenotyping, which can be used to generate individual risk estimates for adverse cardiac outcome in patients with Fabry disease. METHODS: This longitudinal prospective cohort study consisted of 200 consecutive patients with Fabry disease undergoing clinical cardiac magnetic resonance. Median follow-up was 4.5 years (IQR: 2.7-6.3 years). Prognostic models were developed using Cox proportional hazards modeling. Outcome was a composite of adverse cardiac events. Model performance was evaluated. A risk calculator, which provides 5-year estimated risk of adverse cardiac outcome for individual patients, including men and women, was generated. RESULTS: The highest performing, internally validated, parsimonious multivariable model included age, native myocardial T1 dispersion (SD of per voxel myocardial T1 relaxation times), and indexed left ventricular mass. Median optimism-adjusted c-statistic across 5 imputed model development data sets was 0.77 (95% CI: 0.70-0.84). Model calibration was excellent across the full risk profile. CONCLUSIONS: This study developed and internally validated a risk prediction model that accurately predicts 5-year risk of adverse cardiac outcome for individual patients with Fabry disease, including men and women, which could easily be integrated into clinical care. External validation is warranted.
Subject(s)
Fabry Disease , Fabry Disease/complications , Fabry Disease/diagnosis , Female , Heart , Humans , Male , Myocardium/pathology , Predictive Value of Tests , Prognosis , Prospective Studies , Risk FactorsABSTRACT
BACKGROUND: Identifying people who are at risk of being admitted to hospital (hospitalised) for heart failure and death, and particularly those who have not previously been hospitalised for heart failure, is a priority. We aimed to develop and externally validate a prognostic model involving contemporary deep phenotyping that can be used to generate individual risk estimates of hospitalisation for heart failure or all-cause mortality in patients with, or at risk of, heart failure, but who have not previously been hospitalised for heart failure. METHODS: Between June 1, 2016, and May 31, 2018, 3019 consecutive adult patients (aged ≥16 years) undergoing cardiac magnetic resonance (CMR) at Manchester University National Health Service Foundation Trust, Manchester, UK, were prospectively recruited into a model development cohort. Candidate predictor variables were selected according to clinical practice and literature review. Cox proportional hazards modelling was used to develop a prognostic model. The final model was validated in an external cohort of 1242 consecutive adult patients undergoing CMR at the University of Pittsburgh Medical Center Cardiovascular Magnetic Resonance Center, Pittsburgh, PA, USA, between June 1, 2010, and March 25, 2016. Exclusion criteria for both cohorts included previous hospitalisation for heart failure. Our study outcome was a composite of first hospitalisation for heart failure or all-cause mortality after CMR. Model performance was evaluated in both cohorts by discrimination (Harrell's C-index) and calibration (assessed graphically). FINDINGS: Median follow-up durations were 1118 days (IQR 950-1324) for the development cohort and 2117 days (1685-2446) for the validation cohort. The composite outcome occurred in 225 (7·5%) of 3019 patients in the development cohort and in 219 (17·6%) of 1242 patients in the validation cohort. The final, externally validated, parsimonious, multivariable model comprised the predictors: age, diabetes, chronic obstructive pulmonary disease, N-terminal pro-B-type natriuretic peptide, and the CMR variables, global longitudinal strain, myocardial infarction, and myocardial extracellular volume. The median optimism-adjusted C-index for the externally validated model across 20 imputed model development datasets was 0·805 (95% CI 0·793-0·829) in the development cohort and 0·793 (0·766-0·820) in the external validation cohort. Model calibration was excellent across the full risk profile. A risk calculator that provides an estimated risk of hospitalisation for heart failure or all-cause mortality at 3 years after CMR for individual patients was generated. INTERPRETATION: We developed and externally validated a risk prediction model that provides accurate, individualised estimates of the risk of hospitalisation for heart failure and all-cause mortality in patients with, or at risk of, heart failure, before first hospitalisation. It could be used to direct intensified therapy and closer follow-up to those at increased risk. FUNDING: The UK National Institute for Health Research, Guerbet Laboratories, and Roche Diagnostics International.
Subject(s)
Heart Failure , State Medicine , Adult , Hospitalization , Humans , Prognosis , Retrospective StudiesABSTRACT
BACKGROUND: Bariatric surgery promotes weight loss and improves co-morbid conditions, with patients who are more physically active having better outcomes. However, levels of physical activity and sedentary behaviour often remain unchanged following surgery. OBJECTIVES: To identify interventions and components thereof that are able to facilitate changes in physical activity and sedentary behaviour. ELIGIBILITY: Physical activity and/or sedentary behaviour must have been measured, pre and post intervention, in patients who have undergone bariatric surgery. STUDY APPRAISAL AND SYNTHESIS METHODS: Four databases were searched with key-words. Two researchers conducted paper screening, data extraction and risk-of-bias assessment. RESULTS: Twelve studies were included; eleven were randomised. Two were delivered presurgery and ten postsurgery; five found positive effect. Moderate to vigorous physical activity increased in three studies, two of which also found a significant increase in step count. The fourth found a significant increase in strenuous activity and the fifth a significant increase in metabolic equivalent of task/day and reduced time spent watching television. LIMITATIONS: Meta-analysis could not be conducted due to heterogeneity of outcomes and the tools used. CONCLUSION AND IMPLICATIONS OF KEY FINDINGS: This review has identified interventions and components thereof that were able to provoke positive effect. However, intervention and control conditions were not always well described particularly in terms of behaviour change techniques and the rationale for their use. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO (CRD42019121372).
Subject(s)
Bariatric Surgery , Sedentary Behavior , Exercise , Health Promotion/methods , HumansABSTRACT
PURPOSE: Following a single seizure, or recent epilepsy diagnosis, it is difficult to balance risk of medication side effects with the potential to prevent seizure recurrence. A prediction model was developed and validated enabling risk stratification which in turn informs treatment decisions and individualises counselling. METHODS: Data from a randomised controlled trial was used to develop a prediction model for risk of seizure recurrence following a first seizure or diagnosis of epilepsy. Time-to-event data was modelled via Cox's proportional hazards regression. Model validity was assessed via discrimination and calibration using the original dataset and also using three external datasets - National General Practice Survey of Epilepsy (NGPSE), Western Australian first seizure database (WA) and FIRST (Italian dataset of people with first tonic-clonic seizures). RESULTS: People with neurological deficit, focal seizures, abnormal EEG, not indicated for CT/MRI scan, or not immediately treated have a significantly higher risk of seizure recurrence. Discrimination was fair and consistent across the datasets (c-statistics: 0.555 (NGPSE); 0.558 (WA); 0.597 (FIRST)). Calibration plots showed good agreement between observed and predicted probabilities in NGPSE at one and three years. Plots for WA and FIRST showed poorer agreement with the model underpredicting risk in WA, and over-predicting in FIRST. This was resolved following model recalibration. CONCLUSION: The model performs well in independent data especially when recalibrated. It should now be used in clinical practice as it can improve the lives of people with single seizures and early epilepsy by enabling targeted treatment choices and more informed patient counselling.
Subject(s)
Anticonvulsants , Epilepsy , Anticonvulsants/therapeutic use , Australia , Epilepsy/drug therapy , Epilepsy/epidemiology , Humans , Probability , Seizures/drug therapy , Seizures/epidemiologyABSTRACT
BACKGROUND: Non-traumatic coma is a common acute childhood presentation to healthcare facilities in Africa and is associated with high morbidity and mortality. Historically, the majority of cases were attributed to cerebral malaria (CM). With the recent drastic reduction in malaria incidence, non-malarial coma is becoming a larger proportion of cases and determining the aetiology is diagnostically challenging, particularly in resource-limited settings. The purpose of this study will be to evaluate the aetiology and prognosis of non-traumatic coma in African children. METHODS: With no date restrictions, systematic searches of MEDLINE, Embase, and Scopus will identify prospective and retrospective studies (including randomised controlled trials, cluster randomised trials, cohort studies, cross-sectional, and case-control studies) recruiting children (1 month-16 years) with non-traumatic coma (defined by Blantyre Coma Score ≤ 2 or comparable alternative) from any African country. Disease-specific studies will be included if coma is associated and reported. The primary outcome is to determine the aetiology (infectious and non-infectious) of non-traumatic coma in African children, with pooled prevalence estimates of causes (e.g., malaria). Secondary outcomes are to determine overall estimates of morbidity and mortality of all-cause non-traumatic coma and disease-specific states of non-traumatic coma, where available. Random effects meta-analysis will summarise aetiology data and in-hospital and post-discharge mortality. Heterogeneity will be quantified with τ2, I2, and Cochran's Q test. DISCUSSION: This systematic review will provide a summary of the best available evidence on the aetiology and outcome of non-traumatic coma in African children. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020141937.
Subject(s)
Aftercare , Patient Discharge , Child , Cross-Sectional Studies , Humans , Meta-Analysis as Topic , Prospective Studies , Retrospective Studies , Systematic Reviews as TopicABSTRACT
INTRODUCTION: Sample size "rules-of-thumb" for external validation of clinical prediction models suggest at least 100 events and 100 non-events. Such blanket guidance is imprecise, and not specific to the model or validation setting. We investigate factors affecting precision of model performance estimates upon external validation, and propose a more tailored sample size approach. METHODS: Simulation of logistic regression prediction models to investigate factors associated with precision of performance estimates. Then, explanation and illustration of a simulation-based approach to calculate the minimum sample size required to precisely estimate a model's calibration, discrimination and clinical utility. RESULTS: Precision is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration. The simulation-based calculation accounts for the LP distribution and (mis)calibration in the validation sample. Application identifies 2430 required participants (531 events) for external validation of a deep vein thrombosis diagnostic model. CONCLUSION: Where researchers can anticipate the distribution of the model's LP (eg, based on development sample, or a pilot study), a simulation-based approach for calculating sample size for external validation offers more flexibility and reliability than rules-of-thumb.
Subject(s)
Computer Simulation/statistics & numerical data , Patient Outcome Assessment , Research Design/statistics & numerical data , Humans , Reproducibility of Results , Sample SizeABSTRACT
Background: Accurately diagnosing asthma can be challenging. Uncertainty about the best combination of clinical features and investigations for asthma diagnosis is reflected in conflicting recommendations from international guidelines. One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis. However, systematic review evidence identifies that existing models for asthma diagnosis are at high risk of bias and unsuitable for clinical use. Being mindful of previous limitations, this protocol describes plans to derive and validate a prediction model for use by healthcare professionals to aid diagnostic decision making during assessment of a child or young person with symptoms suggestive of asthma in primary care. Methods: A prediction model will be derived using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and linked primary care electronic health records (EHR). Data will be included from study participants up to 25 years of age where permissions exist to use their linked EHR. Participants will be identified as having asthma if they received at least three prescriptions for an inhaled corticosteroid within a one-year period and have an asthma code in their EHR. To deal with missing data we will consider conducting a complete case analysis. However, if the exclusion of cases with missing data substantially reduces the total sample size, multiple imputation will be used. A multivariable logistic regression model will be fitted with backward stepwise selection of candidate predictors. Apparent model performance will be assessed before internal validation using bootstrapping techniques. The model will be adjusted for optimism before external validation in a dataset created from the Optimum Patient Care Research Database. Discussion: This protocol describes a robust strategy for the derivation and validation of a prediction model to support the diagnosis of asthma in children and young people in primary care.
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BACKGROUND: Predictive models within epilepsy are frequently developed via Cox's proportional hazards models. These models estimate risk of a specified event such as 12-month remission. They are relatively simple to produce, have familiar output, and are useful to answer questions about short-term prognosis. However, the Cox model only considers time to first event rather than all seizures after starting treatment for example. This makes assessing change in seizure rates over time difficult. Variants to the Cox model exist enabling recurrent events to be modelled. One such variant is the Prentice, Williams and Peterson - Total Time (PWP-TT) model. An alternative is the negative binomial model for event counts. This study aims to demonstrate the differences between the three approaches, and to consider the benefits of the PWP-TT approach for assessing change in seizure rates over time. METHODS: Time to 12-month remission and time to first seizure after randomisation were modelled using the Cox model. Risk of seizure recurrence was modelled using the PWP-TT model, including all seizures across the whole follow-up period. Seizure counts were modelled using negative binomial regression. Differences between the approaches were demonstrated using participants recruited to the UK-based multi-centre Standard versus New Antiepileptic Drug (SANAD) study. RESULTS: Results from the PWP-TT model were similar to those from the conventional Cox and negative binomial models. In general, the direction of effect was consistent although the variables included in the models and the significance of the predictors varied. The confidence intervals obtained via the PWP-TT model tended to be narrower due to the increase in statistical power of the model. CONCLUSIONS: The Cox model is useful for determining the initial response to treatment and potentially informing when the next intervention may be required. The negative binomial model is useful for modelling event counts. The PWP-TT model extends the Cox model to all included events. This is useful in determining the longer-term effects of treatment policy. Such a model should be considered when designing future clinical trials in medical conditions typified by recurrent events to improve efficiency and statistical power as well as providing evidence regarding changes in event rates over time.
Subject(s)
Anticonvulsants , Carbamazepine , Epilepsies, Partial , Epilepsy, Generalized , Seizures , Anticonvulsants/adverse effects , Carbamazepine/therapeutic use , Clinical Trials as Topic , Epilepsies, Partial/drug therapy , Epilepsy, Generalized/drug therapy , Humans , Seizures/chemically induced , Seizures/drug therapyABSTRACT
BACKGROUND: In sub-Saharan Africa, there is a dearth of epidemiologic data on the burden of cerebral atherosclerosis. This is explained by the limited availability and the high cost of standard vascular imaging techniques. Neurovascular ultrasound is portable, cheaper and non-invasive and could, therefore, represent a reasonable alternative to fill this knowledge gap. We explored the feasibility of neurovascular ultrasound in Malawian adults with acute stroke-like syndrome to inform the design of future large stroke studies comparing its diagnostic performance to that of gold standard vascular imaging techniques in sub-Saharan Africa. METHODS: We enrolled consecutive patients diagnosed with acute stroke-like syndrome based on the World Health Organization definition. Clinical and demographic data were recorded, and a comprehensive neurovascular ultrasound was performed. Fisher's exact and Kruskal-Wallis tests were used to study the relationship between atherosclerosis and potential risk factors. RESULTS: Sixty-six patients were enrolled (mean age: 58.7 years). The frequency of extracranial atherosclerosis was 39.4% (n = 26, 95% CI: 28.6-52.2). There were 12 patients with abnormal carotid intima media thickness (18.2%, 95% CI: 9.8-29.6) and 14 patients with a carotid plaque (21.2%, 95% CI: 12.1-33.0). The frequency of intracranial atherosclerosis was 19.2% (95%CI: 6.6-39.4) in 26 patients with successful transcranial insonation. Hypertension (80.8 versus 52.5%, p = 0.03) and hypercholesterolemia (11.5 versus 0.0%, p = 0.05) were more prevalent in patients with extracranial atherosclerosis. CONCLUSIONS: This study demonstrates the feasibility of neurovascular ultrasound to assess cervical arteries in adults with stroke-like syndrome in sub-Saharan Africa. There is a high rate of transcranial insonation failure in this setting, highlighting the need for echocontrast agents.
Subject(s)
Cerebral Angiography , Stroke/diagnosis , Ultrasonography , Adult , Aged , Carotid Artery Diseases/complications , Cerebral Angiography/methods , Cross-Sectional Studies , Female , Humans , Intracranial Arteriosclerosis/complications , Malawi/epidemiology , Male , Middle Aged , Risk Assessment , Risk Factors , Severity of Illness Index , Stroke/epidemiology , Stroke/etiology , Ultrasonography/methodsABSTRACT
BACKGROUND: Clinical prediction models are widely used to guide medical advice and therapeutic interventions. Asthma is one of the most common chronic diseases globally and is characterised by acute deteriorations. These exacerbations are largely preventable, so there is interest in using clinical prediction models in this area. The objective of this review was to identify studies which have developed such models, determine whether consistent and appropriate methodology was used and whether statistically reliable prognostic models exist. METHODS: We searched online databases MEDLINE (1948 onwards), CINAHL Plus (1937 onwards), The Cochrane Library, Web of Science (1898 onwards) and ClinicalTrials.gov, using index terms relating to asthma and prognosis. Data was extracted and assessment of quality was based on GRADE and an early version of PROBAST (Prediction study Risk of Bias Assessment Tool). A meta-analysis of the discrimination and calibration measures was carried out to determine overall performance across models. RESULTS: Ten unique prognostic models were identified. GRADE identified moderate risk of bias in two of the studies, but more detailed quality assessment via PROBAST highlighted that most models were developed using highly selected and small datasets, incompletely recorded predictors and outcomes, and incomplete methodology. None of the identified models modelled recurrent exacerbations, instead favouring either presence/absence of an event, or time to first or specified event. Preferred methodologies were logistic regression and Cox proportional hazards regression. The overall pooled c-statistic was 0.77 (95% confidence interval 0.73 to 0.80), though individually some models performed no better than chance. The meta-analysis had an I2 value of 99.75% indicating a high amount of heterogeneity between studies. The majority of studies were small and did not include internal or external validation, therefore the individual performance measures are likely to be optimistic. CONCLUSIONS: Current prognostic models for asthma exacerbations are heterogeneous in methodology, but reported c-statistics suggest a clinically useful model could be created. Studies were consistent in lacking robust validation and in not modelling serial events. Further research is required with respect to incorporating recurrent events, and to externally validate tools in large representative populations to demonstrate the generalizability of published results.