10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK.

OBJECTIVES: To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019. PARTICIPANTS: 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0-100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code. PRIMARY OUTCOME MEASURES: The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts. RESULTS: The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group. CONCLUSION: People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.

Clinical Frailty Scale as a predictor of adverse outcomes following aortic valve replacement: a systematic review and meta-analysis.

OBJECTIVES: Assessment of frailty prior to aortic valve intervention is recommended in European and North American valvular heart disease guidelines. However, there is a lack of consensus on how it is best measured. The Clinical Frailty Scale (CFS) is a well-validated measure of frailty that is relatively quick to calculate. This meta-analysis sought to examine whether the CFS predicts mortality and morbidity following either transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR). METHODS: Nine electronic databases were searched systematically for data on clinical outcomes post-TAVI/SAVR, where patients had undergone preoperative frailty assessment using the CFS. The primary endpoint was 12-month mortality. TAVI and SAVR data were assessed and reported separately. For each individual study, the incidence of adverse outcomes was extracted according to a CFS score of 5-9 (ie, frail) versus 1-4 (ie, non-frail), with meta-analysis performed using a random effects model. RESULTS: Of 2612 records screened, nine were included in the review (five TAVI, three SAVR and one which included both interventions). Among 4923 TAVI patients, meta-analysis showed 12-month mortality rates of 19.1% for the frail cohort versus 9.8% for the non-frail cohort (RR 2.53 (1.63 to 3.95), p<0.001, I2=83%). For the smaller cohort of SAVR patients (n=454), mortality rates were 20.3% versus 3.9% for the frail and non-frail cohorts, respectively (RR 5.08 (2.31 to 11.15), p<0.001, I2=5%). CONCLUSIONS: Frailty, as determined by the CFS, was associated with an increased mortality risk in the 12 months following either TAVI or SAVR. These data would support its use in the preoperative assessment of elderly patients undergoing aortic valve interventions.

Association between troponin level and medium-term mortality in 20 000 hospital patients.

INTRODUCTION: Cardiac troponin (cTn) concentrations above the manufacturer recommended upper limit of normal (ULN) are frequently seen in hospital patients without a clinical presentation consistent with type 1 myocardial infarction, and the significance of this is uncertain. The aim of this study was to assess the relationship between medium-term mortality and cTn concentration in a large consecutive hospital population, regardless of whether there was a clinical indication for performing the test. METHOD: This prospective observational study included 20 000 consecutive in-hospital and outpatient patients who had a blood test for any reason at a large teaching hospital, and in whom a hs-cTnI assay was measured, regardless of the original clinical indication. Mortality was obtained via NHS Digital. RESULTS: A total of 20 000 patients were included in the analysis and 18 282 of these (91.4%) did not have a clinical indication for cardiac troponin I (cTnI) testing. Overall, 2825 (14.1%) patients died at a median of 809 days. The mortality was significantly higher if the cTnI concentration was above the ULN (45.3% vs 12.3% p<0.001 log rank). Multivariable Cox analysis demonstrated that the log10 cTnI concentration was independently associated with mortality (HR 1.76 (95% CI 1.65 to 1.88)). Landmark analysis, excluding deaths within 30 days, showed the relationship between cTnI concentration and mortality persisted. CONCLUSION: In a large, unselected hospital population, in 91.4% of whom there was no clinical indication for testing, cTnI concentration was independently associated with medium-term cardiovascular and non-cardiovascular mortality in the statistical model tested.

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

Background: In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome cannot be in the imputation model, as recommended under multiple imputation. Regression imputation uses a fitted model to impute the predicted value of missing predictors from observed data, and could offer a pragmatic alternative at deployment. Moreover, the use of missing indicators has been proposed to handle informative missingness, but it is currently unknown how well this method performs in the context of clinical prediction models. Methods: We simulated data under various missing data mechanisms to compare the predictive performance of clinical prediction models developed using both imputation methods. We consider deployment scenarios where missing data is permitted or prohibited, imputation models that use or omit the outcome, and clinical prediction models that include or omit missing indicators. We assume that the missingness mechanism remains constant across the model pipeline. We also apply the proposed strategies to critical care data. Results: With complete data available at deployment, our findings were in line with existing recommendations; that the outcome should be used to impute development data when using multiple imputation and omitted under regression imputation. When missingness is allowed at deployment, omitting the outcome from the imputation model at the development was preferred. Missing indicators improved model performance in many cases but can be harmful under outcome-dependent missingness. Conclusion: We provide evidence that commonly taught principles of handling missing data via multiple imputation may not apply to clinical prediction models, particularly when data can be missing at deployment. We observed comparable predictive performance under multiple imputation and regression imputation. The performance of the missing data handling method must be evaluated on a study-by-study basis, and the most appropriate strategy for handling missing data at development should consider whether missing data are allowed at deployment. Some guidance is provided.

Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol.

INTRODUCTION: Cardiovascular disease (CVD) remains one of the leading causes of preventable death in Europe, therefore any opportunity to intervene and improve care should be maximised. Known CVD risk factors are routinely collected in the emergency department (ED), yet they are often not acted on. If the risk factors have prognostic value and a pathway can be created, then this would provide more holistic care for patients and reduce health system inefficiency. METHODS AND ANALYSIS: In this mixed-methods study, we will use quantitative methods to investigate the prognostic characteristics of routinely collected data for long-term CVD outcomes, and qualitative methods to investigate how to use and implement this knowledge. The quantitative arm will use a database of approximately 21 000 chest pain patient episodes with a mean follow-up of 7.3 years. We will use Cox regression to evaluate the prognostic characteristics of routinely collected ED data for long-term CVD outcomes. We will also use a series of semi-structured interviews to co-design a prototype care pathway with stakeholders via thematic analysis. To enable the development of prototypes, themes will be structured into a logic model consisting of situation, inputs, outputs and mechanism. ETHICS AND DISSEMINATION: This work has been approved by Research Ethics Committee (Wales REC7) and the Human Research Authority under reference 19/WA/0312 and 19/WA/0311. It has also been approved by the Confidentiality Advisory Group reference 19/CAG/0209. Dissent recorded in the NHS' opt-out scheme will be applied to the dataset by NHS Digital. This work will be disseminated through peer-review publication, conference presentation and a public dissemination strategy. TRIAL REGISTRATION NUMBER: ISRCTN41008456. PROTOCOL VERSION: V.1.0-7 June 2021.

Racial differences in management and outcomes of acute myocardial infarction during COVID-19 pandemic.

OBJECTIVE: There are concerns that healthcare and outcomes of black, Asian and minority ethnic (BAME) communities are disproportionately impacted by the COVID-19 pandemic. We investigated admission rates, treatment and mortality of BAME with acute myocardial infarction (AMI) during COVID-19. METHODS: Using multisource national healthcare records, patients hospitalised with AMI in England during 1 February-27 May 2020 were included in the COVID-19 group, whereas patients admitted during the same period in the previous three consecutive years were included in a pre-COVID-19 group. Multilevel hierarchical regression analyses were used to quantify the changes in-hospital and 7-day mortality in BAME compared with whites. RESULTS: Of 73 746 patients, higher proportions of BAME patients (16.7% vs 10.1%) were hospitalised with AMI during the COVID-19 period compared with pre-COVID-19. BAME patients admitted during the COVID-19 period were younger, male and likely to present with ST-elevation acute myocardial infarction. COVID-19 BAME group admitted with non-ST-elevation acute myocardial infarction less frequently received coronary angiography (86.1% vs 90.0%, p<0.001) and had a longer median delay to reperfusion (4.1 hours vs 3.7 hours, p<0.001) compared with whites. BAME had higher in-hospital (OR 1.68, 95% CI 1.27 to 2.28) and 7-day mortality (OR 1.81 95% CI 1.31 to 2.19) during COVID-19 compared with pre-COVID-19 period. CONCLUSION: In this multisource linked cohort study, compared with whites, BAME patients had proportionally higher hospitalisation rates with AMI, less frequently received guidelines indicated care and had higher early mortality during COVID-19 period compared with pre-COVID-19 period. There is a need to develop clinical pathways to achieve equity in the management of these vulnerable populations.

How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies.

BACKGROUND: Analysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has been limited research into such biases in a competing risks framework. METHODS: We designed simulations to examine bias in the estimated treatment effect under Cox and Fine & Gray models with unmeasured confounding present. We varied the strength of the unmeasured confounding (i.e. the unmeasured variable's effect on the probability of treatment and both outcome events) in different scenarios. RESULTS: In both the Cox and Fine & Gray models, correlation between the unmeasured confounder and the probability of treatment created biases in the same direction (upward/downward) as the effect of the unmeasured confounder on the event-of-interest. The association between correlation and bias is reversed if the unmeasured confounder affects the competing event. These effects are reversed for the bias on the treatment effect of the competing event and are amplified when there are uneven treatment arms. CONCLUSION: The effect of unmeasured confounding on an event-of-interest or a competing event should not be overlooked in observational studies as strong correlations can lead to bias in treatment effect estimates and therefore cause inaccurate results to lead to false conclusions. This is true for cause specific perspective, but moreso for a subdistribution perspective. This can have ramifications if real-world treatment decisions rely on conclusions from these biased results. Graphical visualisation to aid in understanding the systems involved and potential confounders/events leading to sensitivity analyses that assumes unmeasured confounders exists should be performed to assess the robustness of results.

Readmission and processes of care across weekend and weekday hospitalisation for acute myocardial infarction, heart failure or stroke: an observational study of the National Readmission Database.

OBJECTIVES: Variation in hospital resource allocations across weekdays and weekends have led to studies of the 'weekend effect' for ST elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), heart failure (HF) and stroke. However, few studies have explored the 'weekend effect' on unplanned readmission. We aimed to investigate 30-day unplanned readmissions and processes of care across weekend and weekday hospitalisations for STEMI, NSTEMI, HF and stroke. DESIGN: We grouped hospitalisations for STEMI, NSTEMI, HF or stroke into weekday or weekend admissions. Multivariable adjusted ORs for binary outcomes across weekend versus weekday (reference) groups were estimated using logistic regression. SETTING: We included all non-elective hospitalisations for STEMI, NSTEMI, HF or stroke, which were recorded in the US Nationwide Readmissions Database between 2010 and 2014. PARTICIPANTS: The analysis sample included 659 906 hospitalisations for STEMI, 1 420 600 hospitalisations for NSTEMI, 3 027 699 hospitalisations for HF, and 2 574 168 hospitalisations for stroke. MAIN OUTCOME MEASURES: The primary outcome was unplanned 30-day readmission. As secondary outcomes, we considered length of stay and the following processes of care: coronary angiography, primary percutaneous coronary intervention, coronary artery bypass graft, thrombolysis, brain scan/imaging, thrombectomy, echocardiography and cardiac resynchronisation therapy/implantable cardioverter-defibrillator. RESULTS: Unplanned 30-day readmission rates were 11.0%, 15.1%, 23.0% and 10.9% for STEMI, NSTEMI, HF and stroke, respectively. Weekend hospitalisations for HF were associated with a statistically significant but modest increase in 30-day readmissions (OR of 1.045, 95% CI 1.033 to 1.058). Weekend hospitalisation for STEMI, NSTEMI or stroke was not associated with increased risk of 30-day readmission. CONCLUSION: There was no clinically meaningful evidence against the supposition that weekend and weekday hospitalisations have the same 30-day unplanned readmissions. Thirty-day readmission rates were high, especially for HF, which has implications for service provision. Strategies to reduce readmission rates should be explored, regardless of day of hospitalisation.

Temporal trends in relative survival following percutaneous coronary intervention.

OBJECTIVE: Percutaneous coronary intervention (PCI) has seen substantial shifts in patient selection in recent years that have increased baseline patient mortality risk. It is unclear to what extent observed changes in mortality are attributable to background mortality risk or the indication and selection for PCI itself. PCI-attributable mortality can be estimated using relative survival, which adjusts observed mortality by that seen in a matched control population. We report relative survival ratios and compare these across different time periods. METHODS: National Health Service PCI activity in England and Wales from 2007 to 2014 is considered using data from the British Cardiovascular Intervention Society PCI Registry. Background mortality is as reported in Office for National Statistics life tables. Relative survival ratios up to 1 year are estimated, matching on patient age, sex and procedure date. Estimates are stratified by indication for PCI, sex and procedure date. RESULTS: 549 305 procedures were studied after exclusions for missing age, sex, indication and mortality status. Comparing from 2007 to 2008 to 2013-2014, differences in crude survival at 1 year were consistently lower in later years across all strata. For relative survival, these differences remained but were smaller, suggesting poorer survival in later years is partly due to demographic characteristics. Relative survival was higher in older patients. CONCLUSIONS: Changes in patient demographics account for some but not all of the crude survival changes seen during the study period. Relative survival is an under-used methodology in interventional settings like PCI and should be considered wherever survival is compared between populations with different demographic characteristics, such as between countries or time periods.

Discharge against medical advice after hospitalisation for acute myocardial infarction.

BACKGROUND: Discharge against medical advice (AMA) occurs infrequently but is associated with poor outcomes. There are limited descriptions of discharges AMA in national cohorts of patients with acute myocardial infarction (AMI). This study aims to evaluate discharge AMA in AMI and how it affects readmissions. METHODS: We conducted a cohort study of patients with AMI in USA in the Nationwide Readmission Database who were admitted between the years 2010 and 2014. Descriptive statistics were presented for variables according to discharge home or AMA. The primary end point was all-cause 30-day unplanned readmissions and their causes. RESULTS: 2663 019 patients were admitted with AMI of which 10.3% (n=162 070) of 1569 325 patients had an unplanned readmission within 30 days. The crude rate of discharge AMA remained stable between 2010 and 2014 at 1.5%. Discharge AMA was an independent predictor of unplanned all-cause readmissions (OR 2.27 95% CI 2.14 to 2.40); patients who discharged AMA had >twofold increased crude rate of readmission for AMI (30.4% vs 13.4%) and higher crude rate of admissions for neuropsychiatric reasons (3.2% vs 1.3%). After adjustment, discharge AMA was associated with increased odds of readmissions for AMI (OR 3.65 95% CI 3.31 to 4.03, p<0.001). We estimate that there are 1420 excess cases of AMI among patients who discharged AMA. CONCLUSIONS: Discharge AMA occurs in 1.5% of the population with AMI and these patients are at higher risk of early readmissions for re-infarction. Interventions should be developed to reduce discharge AMA in high-risk groups and initiate interventions to avoid adverse outcomes and readmission.

Association of comorbid burden with clinical outcomes after transcatheter aortic valve implantation.

OBJECTIVES: To investigate the association of the CharlsonComorbidity Index (CCI) with clinical outcomes after transcatheter aortic valve implantation (TAVI). BACKGROUND: Patients undergoing TAVI have high comorbid burden; however, there is limited evidence of its impact on clinical outcomes. METHODS: Data from 1887 patients from the UK, Canada, Spain, Switzerland and Italy were collected between 2007 and 2016. The association of CCI with 30-day mortality, Valve Academic Research Consortium-2 (VARC-2) composite early safety, long-term survival and length of stay (LoS) was calculated using logistic regression and Cox proportional hazard models, as a whole cohort and at a country level, through a two-stage individual participant data (IPD) random effect meta-analysis. RESULTS: Most (60%) of patients had a CCI ≥3. A weak correlation was found between the total CCI and four different preoperative risks scores (ρ=0.16 to 0.29), and approximately 50% of patients classed as low risk from four risk prediction models still presented with a CCI ≥3. Per-unit increases in total CCI were not associated with increased odds of 30-day mortality (OR 1.09, 95% CI 0.96 to 1.24) or VARC-2 early safety (OR 1.04, 95% CI 0.96 to 1.14) but were associated with increased hazard of long-term mortality (HR 1.10, 95% CI 1.05 to 1.16). The two-stage IPD meta-analysis indicated that CCI was not associated with LoS (HR 0.97, 95% CI 0.93 to 1.02). CONCLUSION: In this multicentre international study, patients undergoing TAVI had significant comorbid burden. We found a weak correlation between the CCI and well-established preoperative risks scores. The CCI had a moderate association with long-term mortality up to 5 years post-TAVI.