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ObjectivesThe Manchester Acute Coronary Syndromes ECG (MACS-ECG) prediction model calculates a score based on objective ECG measurements to give the probability of a non-ST elevation myocardial infarction (NSTEMI). The model showed good performance in the emergency department (ED), but its accuracy in the pre-hospital setting is unknown. We aimed to externally validate MACS-ECG in the pre-hospital environment.MethodsWe undertook a secondary analysis from the Pre-hospital Evaluation of Sensitive Troponin (PRESTO) study, a multi-centre prospective study to validate decision aids in the pre-hospital setting (26 February 2019 to 23 March 2020). Patients with chest pain where the treating paramedic suspected acute coronary syndrome were included. Paramedics collected demographic and historical data and interpreted ECGs contemporaneously (as ‘normal’ or ‘abnormal’). After completing recruitment, we analysed ECGs to calculate the MACS-ECG score, using both a pre-defined threshold and a novel threshold that optimises sensitivity to differentiate AMI from non-AMI. This was compared with subjective ECG interpretation by paramedics. The diagnosis of AMI was adjudicated by two investigators based on serial troponin testing in hospital.ResultsOf 691 participants, 87 had type 1 AMI and 687 had complete data for paramedic ECG interpretation. The MACS-ECG model had a C-index of 0.68 (95% CI: 0.61 to 0.75). At the pre-determined cut-off, MACS-ECG had 2.3% (95% CI: 0.3% to 8.1%) sensitivity, 99.5% (95% CI: 98.6% to 99.9%) specificity, 40.0% (95% CI: 10.2% to 79.3%) positive predictive value (PPV) and 87.6% (87.3% to 88.0%) negative predictive value (NPV). At the optimal threshold for sensitivity, MACS-ECG had 50.6% sensitivity (39.6% to 61.5%), 83.1% specificity (79.9% to 86.0%), 30.1% PPV (24.7% to 36.2%) and 92.1% NPV (90.4% to 93.5%). In comparison, paramedics had a sensitivity of 71.3% (95% CI: 60.8% to 80.5%) with 53.8% (95% CI: 53.8% to 61.8%) specificity, 19.7% (17.2% to 22.45%) PPV and 93.3% (90.8% to 95.1%) NPV.ConclusionNeither MACS-ECG nor paramedic ECG interpretation had a sufficiently high PPV or NPV to ‘rule in’ or ‘rule out’ NSTEMI alone.

Aims Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants ([Formula: see text]) is appropriate relative to the number of events ([Formula: see text]) and the number of predictor parameters ([Formula: see text]) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes. Proposed criteria The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted [Formula: see text] Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated Cox-snell [Formula: see text] of distinct ‘one-to-one’ logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell [Formula: see text] of the multinomial logistic regression. Evaluation of criteria We tested the performance of the proposed criteria (i) through a simulation study and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) were natural extensions from previously proposed criteria for binary outcomes and did not require evaluation through simulation. Summary We illustrated how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules.

IntroductionThis study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.MethodsWe considered five methods: product (multiply marginal risks), dual-outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.ResultsDiscrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual-outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.DiscussionWe recommend the dual-outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.

Background/Aims As recommended by the NICE guidelines, methotrexate (MTX) is typically prescribed as the first line therapy for patients with rheumatoid arthritis (RA). However, around 40% of patients do not respond to MTX at 6 months and around 80% experience adverse events (AEs). Our previous systematic review identified that clinical prediction models of MTX outcomes suffered from methodological limitations, including a lack of validation, suboptimal handling of missing data, and no consideration of competing risks, such as patients discontinuing due to adverse events (AEs). These shortcomings resulted in high risk of bias and should be addressed to aid the implementation of outcome prediction in clinical practice. Therefore, this study aimed to (i) develop a multinomial prediction model for estimating an individual’s risks of not achieving low disease activity (LDA) and discontinuing due to AEs at 6 months after starting MTX, using information observable at the starting point of treatment, (ii) update prognosis at 3 months, and (iii) internally validate these models to assess performance. Methods Data came from the national multi-centre (n = 38) Rheumatoid Arthritis Medication Study (RAMS), which recruited patients with a diagnosis of RA or undifferentiated polyarthritis starting MTX for the first time. We conducted a Patient and Public Involvement focus group with past and current MTX users, which informed our outcome choice of LDA (DAS28 ≤3.2). A multinomial logistic regression (MLR) was used to develop a prediction model that estimated the probabilities of three outcomes: 1) not in LDA, 2) achieved LDA, and 3) discontinued due to AEs. The updated model at 3 months was conditional on patients not having achieved LDA. Missing data was handled using multiple imputation and models were internally validated through bootstrapping. Calibration, discrimination, and variance explained was assessed to quantify model performance. Results A total of 1632 patients were included in the analysis. At 6 months, 756 (46%) patients achieved LDA, 730 (45%) were not in LDA, and 146 (9%) discontinued due to AEs. The updated model at 3 months included 1179 (72%) patients as they had not achieved LDA. For the LDA outcome pair, the c-statistic was 0.73 (0.70, 0.75), while it was 0.54 (0.49, 0.59) for the AEs outcome. The calibration slope was 0.99 (0.85, 1.12) and 0.74 (0.23, 1.23), respectively. Predictive performance at 3 months was similar to baseline. Conclusion Our MLR models handled outcomes of disease activity and AEs simultaneously. We were able to predict outcomes of LDA more accurately than AEs and performance was better in terms of calibration, which is crucial for the clinical utility of a prediction model. This modelling approach could provide more clinically useful and realistic predictions of MTX outcomes, as competing risks such as AEs have previously been ignored. Disclosure C.K. Gehringer: None. G.P. Martin: None. K.L. Hyrich: Honoraria; Abbvie. Grants/research support; BMS and Pfizer. S.M.M. Verstappen: None. J.C. Sergeant: None.

Objectives: No clear guidance exists on handling missing data at each stage of developing, validating and implementing a clinical prediction model (CPM). We aimed to review the approaches to handling missing data that underly the CPMs currently recommended for use in UK healthcare.Study Design and Setting: A descriptive cross-sectional meta-epidemiological study aiming to identify CPMs recommended by the National Institute for Health and Care Excellence (NICE), which summarized how missing data is handled across their pipelines.Results: A total of 23 CPMs were included through “sampling strategy.” Six missing data strategies were identified: complete case analysis (CCA), multiple imputation, imputation of mean values, k-nearest neighbours imputation, using an additional category for missingness, considering missing values as risk-factor-absent. 52% of the development articles and 48% of the validation articles did not report how missing data were handled. CCA was the most common approach used for development (40%) and validation (44%). At implementation, 57% of the CPMs required complete data entry, whilst 43% allowed missing values. Three CPMs had consistent paths in their pipelines.Conclusion: A broad variety of methods for handling missing data underly the CPMs currently recommended for use in UK healthcare. Missing data handling strategies were generally inconsistent. Better quality assurance of CPMs needs greater clarity and consistency in handling of missing data.

This was an observational study of the 1-year outcomes of the 20,000 patients included in the original CHARIOT study. The aim of the study was to assess the association between high sensitivity troponin I (hs-cTnI) concentration and 1 year mortality in this cohort. The original CHARIOT study included a consecutive cohort of in- and out-patients undergoing blood tests for any reason. Hs-cTnI concentrations were measured regardless of whether the clinician requested them. These results were nested and not revealed to the team unless requested for clinical reasons. One year mortality data was obtained from NHS Digital as originally planned. Overall, 1782 (8.9%) patients had died at 1 year. Multivariable Cox regression analysis showed that a hs-cTnI concentration above the upper limit of normal was independently associated with the hazard of mortality (HR 2.23; 95% confidence intervals 1.97 to 2.52). Furthermore, the log (10) hs-cTnI concentration was independently associated with the hazard of 1 year mortality (HR 1.77; 95% confidence intervals 1.64 to 1.91). In conclusion, in a large, unselected hospital population of both in- and out-patients, in 18,282 (91.4%) of whom there was no clinical indication for testing, hs-cTnI concentration was associated with 1 year mortality.

Aims To determine if remotely monitored physiological data from cardiac implantable electronic devices (CIEDs) can be used to identify patients at high risk of mortality. Methods and results This study evaluated whether a risk score based on CIED physiological data (Triage-Heart Failure Risk Status, ‘Triage-HFRS’, previously validated to predict heart failure (HF) events) can identify patients at high risk of death. Four hundred and thirty-nine adults with CIEDs were prospectively enrolled. Primary observed outcome was all-cause mortality (median follow-up: 702 days). Several physiological parameters [including heart rate profile, atrial fibrillation/tachycardia (AF/AT) burden, ventricular rate during AT/AF, physical activity, thoracic impedance, therapies for ventricular tachycardia/fibrillation] were continuously monitored by CIEDs and dynamically combined to produce a Triage-HFRS every 24 h. According to transmissions patients were categorized into ‘high-risk’ or ‘never high-risk’ groups. During follow-up, 285 patients (65%) had a high-risk episode and 60 patients (14%) died (50 in high-risk group; 10 in never high-risk group). Significantly more cardiovascular deaths were observed in the high-risk group, with mortality rates across groups of high vs. never-high 10.3% vs. Conclusion Remote monitoring data from CIEDs can be used to identify patients at higher risk of all-cause mortality as well as HF events. Distinct from other prognostic scores, this approach is automated and continuously updated.

Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development.

Background Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model. Methods We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital’s admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model. Discussion CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time. Trial registration ISRCTN number: ISRCTN41008456

In the management of rheumatoid arthritis (RA), there is a clinical need to identify which patients are at high-risk of not responding to methotrexate (MTX), or experiencing adverse events (AEs), to enable earlier alternative treatments. Many clinical prediction models (CPMs) have previously been developed, but a summary of such models and their methodological quality is lacking. This systematic review aimed to (i) identify and summarize previously published CPMs of MTX outcomes in biologic-naïve RA patients, and (ii) critically appraise their methodological properties.Medline and Embase were searched to identify studies developing or validating CPMs of MTX outcomes in RA patients. The risk of bias (ROB) was assessed using PROBAST (prediction model risk of bias assessment tool). A fixed effects meta-analysis summarised discrimination for models with multiple external validations.The systematic review identified 20 CPMs across 13 studies, and 4 validation studies. Three outcome types were used: a state of disease activity (n = 14, 70%); EULAR response criteria (n = 4, 20%); or discontinuation due to AEs (n = 2, 10%). Only one model accounted for potential competing risks, and nine (45%) were internally validated. Eight (40%) models used multiple imputation for missing data, others were often limited to complete case analysis. There was overall high ROB. The meta-analysis summarised c-statistics for two models with multiple external validations was 0.77 (95% CI: 0.69, 0.84) and 0.68 (0.64, 0.71).This review highlights several methodological shortcomings that should be addressed in future model development to increase potential for implementation into practice.

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.

Background: Excess body fatness, approximated by body mass index (BMI), is associated with higher risk of at least 13 cancer types and is the second-largest avoidable cause of cancer in many populations. Current epidemiologic evidence linking body fatness with cancer risk is largely based on a ‘once-only’ BMI measure which may fail to capture the life-course exposure to body fatness. Here, we test whether a novel metric that combines repeated BMI measures over an individual’s adult lifetime, the overweight-years metric, improves the performance characteristics for associations with cancer compared with a ‘once-only’ BMI measure. Methods: Within the ABACus 2 Consortium (4 US cohorts; 1 European cohort), we derived the overweight-years metric - a product of the degree of overweight (BMI minus 24.9 kg/m2) and the duration of overweight (in years). Using a random effects two-stage meta-analysis, we calculated the association between the overweight-years metric, and separately the cumulative degree and cumulative duration of overweight exposure with incident cancer by fitting multivariable-adjusted Cox proportional hazards models and comparing each metrics performance with BMI measured at a single time using Harrell’s C-statistic. Results: Out of 1,419,850 participants in the ABACus 2 consortium, 716,909 participants were included. Per standard deviation overweight-years, the multivariable-adjusted hazard ratio for obesity-related cancers in men was 1.15 (95% CI: 1.13, 1.17, I2: 0) and for women was 1.11 (95% CI: 1.04, 1.18, I2: 0.94). For ‘once-only’ BMI, the per standard deviation multivariable-adjusted hazard ratio for obesity-related cancers in men was 1.16 (95% CI: 1.15,1.18, I2: 0) and for women was 1.13 (95% CI: 1.09,1.18, I2: 0.82). For most obesity-related cancers, both degree and duration of overweight were significantly associated with risk. The overweight-years metric had a C-statistic of 0.610 (95% CI: 0.569, 0.649) and once-only BMI had a C-statistic of 0.608 (95% CI: 0.566, 0.648) for combined obesity-related cancers in men. In women, the C-statistic for overweight-years was 0.562 (95% CI: 0.537, 0.587) and once-only BMI had a C-statistic of 0.566 (95% CI: 0.544, 0.588) for combined obesity-related cancers. The C-statistic of the overweight-years metric and BMI measured at a single time combined was 0.609 (95% CI: 0.567, 0.650) in men and 0.573 (95% CI: 0.546, 0.600) in women for combined obesity-related cancers. Conclusion: Overall, there were marginal differences in the predictive performance between the overweight-years metric and a ‘once-only’ baseline-BMI measure. These findings show that excess weight throughout adulthood is important in cancer development. Funding: CRUK, NIHR, NHLBI, NCI, NPCR. Citation Format: Nadin Hawwash, Matthew Sperrin, Glen P. Martin, Michael Cook, Charles E. Matthews, Marian L. Neuhouser, Corinne E. Joshu, Elizabeth A. Platz, Heinz Freisling, Marc Gunter, Rob Bristow, Andrew G. Renehan. Excess weight by degree and duration and cancer risk: an individual participant data (IPD) meta - analyses of over 1.4 million participants (ABACus2 consortium) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3034.

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.

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.

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.

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.

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.

This study examines the safety and feasibility of same-day discharge (SDD) in patients undergoing percutaneous coronary intervention (PCI) to coronary chronic total occlusions (CTOs) and explores independent associations of clinical and procedural characteristics with SDD.While the recently published consensus statements recommend SDD following uncomplicated CTO-PCI, there are limited studies to support this approach.Data were obtained from the British Cardiovascular Intervention Society (BCIS) registry dataset including 21,330 patients who underwent CTO-PCI electively from 2007 to 2014 in England and Wales. We used multiple logistic regression to evaluate associations with SDD and the BCIS national risk model to examine for safety of SDD.Although overnight stay remained the standard of care following elective CTO-PCI, SDD practice increased from 21.7% to 44.7%. Women were less likely to have SDD than males. SDD was more common in higher CTO volume centers (36.3%) than low CTO volume centers (31.6%), and SDD patient profiles grew riskier over time, with the average age of SDD patients increasing from 61.4 years to 63.2 years. Transradial PCI was most strongly independently associated with SDD (odds ratio [OR], 1.94; 95% confidence interval [CI], 1.80-2.09). Finally, the SDD observed 30-day mortality rates were not different vs those predicted by the BCIS risk model, and SDD was not independently associated with 30-day mortality (OR, 0.54; 95% CI, 0.25-1.15).This study illustrates that SDD is safe in selected patients undergoing CTO-PCI.

BACKGROUND: Excess deaths during the COVID-19 pandemic compared with those expected from historical trends have been unequally distributed, both geographically and socioeconomically. Not all excess deaths have been directly related to COVID-19 infection. We investigated geographical and socioeconomic patterns in excess deaths for major groups of underlying causes during the pandemic.METHODS: Weekly mortality data from 27/12/2014 to 2/10/2020 for England and Wales were obtained from the Office of National Statistics. Negative binomial regressions were used to model death counts based on pre-pandemic trends for deaths caused directly by COVID-19 (and other respiratory causes) and those caused indirectly by it (cardiovascular disease or diabetes, cancers, and all other indirect causes) over the first 30 weeks of the pandemic (7/3/2020-2/10/2020).FINDINGS: There were 62,321 (95% CI: 58,849 to 65,793) excess deaths in England and Wales in the first 30 weeks of the pandemic. Of these, 46,221 (95% CI: 45,439 to 47,003) were attributable to respiratory causes, including COVID-19, and 16,100 (95% CI: 13,410 to 18,790) to other causes. Rates of all-cause excess mortality ranged from 78 per 100,000 in the South West of England and in Wales to 130 per 100,000 in the West Midlands; and from 93 per 100,000 in the most affluent fifth of areas to 124 per 100,000 in the most deprived. The most deprived areas had the highest rates of death attributable to COVID-19 and other indirect deaths, but there was no socioeconomic gradient for excess deaths from cardiovascular disease/diabetes and cancer.INTERPRETATION: During the first 30 weeks of the COVID-19 pandemic there was significant geographic and socioeconomic variation in excess deaths for respiratory causes, but not for cardiovascular disease, diabetes and cancer. Pandemic recovery plans, including vaccination programmes, should take account of individual characteristics including health, socioeconomic status and place of residence.FUNDING: None.

OBJECTIVES The ability to accurately estimate the risk of peri-operative mortality after lung resection is important. There are concerns about the performance and validity of existing models developed for this purpose, especially when predicting mortality within 90 days of surgery. The aim of this study was therefore to develop a clinical prediction model for mortality within 90 days of undergoing lung resection. METHODS A retrospective database of patients undergoing lung resection in two UK centres between 2012 and 2018 was used to develop a multivariable logistic risk prediction model, with bootstrap sampling used for internal validation. Apparent and adjusted measures of discrimination (area under receiving operator characteristic curve) and calibration (calibration-in-the-large and calibration slope) were assessed as measures of model performance. RESULTS Data were available for 6600 lung resections for model development. Predictors included in the final model were age, sex, performance status, percentage predicted diffusion capacity of the lung for carbon monoxide, anaemia, serum creatinine, pre-operative arrhythmia, right-sided resection, number of resected bronchopulmonary segments, open approach and malignant diagnosis. Good model performance was demonstrated, with adjusted area under receiving operator characteristic curve, calibration-in-the-large and calibration slope values (95% confidence intervals) of 0.741 (0.700, 0.782), 0.006 (−0.143, 0.156) and 0.870 (0.679, 1.060), respectively. CONCLUSIONS The RESECT-90 model demonstrates good statistical performance for the prediction of 90-day mortality after lung resection. A project to facilitate large-scale external validation of the model to ensure that the model retains accuracy and is transferable to other centres in different geographical locations is currently underway.

IntroductionSudden cardiac death (SCD) is the leading cause of death in patients with myocardial infarction (MI) and can be prevented by the implantable cardioverter defibrillator (ICD). Currently, risk stratification for SCD and decision on ICD implantation are based solely on impaired left ventricular ejection fraction (LVEF). However, this strategy leads to over- and under-treatment of patients because LVEF alone is insufficient for accurate assessment of prognosis. Thus, there is a need for better risk stratification. This is the study protocol for developing and validating a prediction model for risk of SCD in patients with prior MI.Methods and AnalysisThe EU funded PROFID project will analyse 23 datasets from Europe, Israel and the US (∼225,000 observations). The datasets include patients with prior MI or ischemic cardiomyopathy with reduced LVEFEthics and disseminationLocal ethical approval was obtained. The final model will be disseminated through scientific publications and a web-calculator. Statistical code will be published through open-source repositories.

BackgroundDeaths in the first year of the COVID-19 pandemic in England & Wales have been shown to be unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups.MethodsYLL for registered deaths in England & Wales, from 27th December 2014 until 25th December 2020, were calculated using 2019 single year sex-specific life tables for England & Wales. Panel time-series models were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7th March 2020 and 25th December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease & diabetes, cancer, and other indirect deaths - all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group.FindingsBetween 7th March 2020 and 25th December 2020 there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England & Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from (916; 95% CI: 820 to 1,012) for the least deprived quintile to (1,645; 95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, an average of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, an average of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in both in the North West.InterpretationDuring 2020, the first calendar year of the COVID-19 pandemic, longstanding socioeconomic and geographical health inequalities in England & Wales were exacerbated, with the most deprived areas suffering the greatest losses in potential years of life lost.FundingNone

Background: The association between socioeconomic status (SES), sex, race / ethnicity and outcomes during hospitalization for heart failure (HF) has not previously been investigated. Methods and Results: We analyzed HF hospitalizations in the United States National Inpatient Sample between 2015 and 2017. Using a hierarchical, multivariable Poisson regression model to adjust for hospital- and patient-level factors, we assessed the association between SES, sex, and race / ethnicity and all-cause in-hospital mortality. We estimated the direct costs (USD) across SES groups. Among 4,287,478 HF hospitalizations, 40.8% were in high SES, 48.7% in female, and 70.0% in White patients. Relative to these comparators, low SES (homelessness or lowest quartile of median neighborhood income) (relative risk [RR] 1.02, 95% confidence interval [CI] 1.00–1.05) and male sex (RR 1.09, 95% CI 1.07–1.11) were associated with increased risk, whereas Black (RR 0.79, 95% CI 0.76–0.81) and Hispanic (RR 0.90, 95% CI 0.86–0.93) race / ethnicity were associated with a decreased risk of in-hospital mortality (5.1% of all hospitalizations). There were significant interactions between race / ethnicity and both, SES (P < .01) and sex (P = .04), such that racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients. The median direct cost of admission was lower in low vs high SES groups ($9324.60 vs $10,940.40), female vs male patients ($9866.60 vs $10,217.10), and Black vs White patients ($9077.20 vs $10,019.80). The median costs increased with SES in all demographic groups primarily related to greater procedural utilization. Conclusions: SES, sex, and race / ethnicity were independently associated with in-hospital mortality during HF hospitalization, highlighting possible care disparities. Racial/ethnic differences in outcome were more pronounced in low SES groups and in male patients.