Loop diuretic utilisation with or without heart failure: impact on prognosis.

BACKGROUND & AIMS: Many patients are prescribed loop diuretics without a diagnostic record of heart failure. Little is known about their characteristics and prognosis. METHODS: Glasgow regional health records (2009-2016) were obtained for adults with cardiovascular disease or taking loop diuretics. Outcomes were investigated using Cox models with hazard ratios adjusted for age, sex, socioeconomic deprivation, and co-morbid disease (adjHR). RESULTS: Of 198,898 patients (median age 65 years; 55% women), 161,935 (81%) neither took loop diuretics nor had a diagnostic record of heart failure (reference group), 23,963 (12%) were taking loop diuretics but had no heart failure recorded, 7,844 (4%) had heart failure recorded and took loop diuretics and 5,156 (3%) had heart failure recorded but were not receiving loop diuretics.Five-year mortality was only slightly higher for heart failure in absence of loop diuretics (22%; adjHR: 1.2 [95% CI 1.1-1.3]), substantially higher for those taking loop diuretics with no heart failure recorded (40%; adjHR: 1.8 [95% CI 1.7-1.8]) and highest for heart failure treated with loop diuretics (52%; adjHR: 2.2 [95% CI 2.0-2.2]). CONCLUSIONS: For patients with cardiovascular disease, many are prescribed loop diuretics without a diagnosis of heart failure being recorded. Mortality is more strongly associated with loop diuretic use than with a heart failure record. The diagnosis of heart failure may be often missed, or loop diuretic use is associated with other conditions with a prognosis similar to heart failure, or inappropriate loop diuretic use increases mortality; all might be true.

Sex differences in cancer outcomes across the range of eGFR.

BACKGROUND AND HYPOTHESIS: People with chronic kidney disease (CKD) have increased incidence and mortality from most cancer types. We hypothesised that odds of presenting with advanced cancer may vary according to differences in eGFR, that this could contribute to increased all-cause mortality and that sex differences may exist. METHODS: Data were from Secure Anonymised Information Linkage Databank, including people with de-novo cancer diagnosis (2011-2017) and two kidney function tests within two years prior to diagnosis to determine baseline eGFR (mL/min/1.73m2). Logistic regression models determined odds of presenting with advanced cancer by baseline eGFR. Cox proportional hazards models tested associations between baseline eGFRcr and all-cause mortality. RESULTS: eGFR < 30 was associated with higher odds of presenting with advanced cancer of prostate, breast and female genital organs, but not other cancer sites. Compared to eGFR > 75-90, eGFR < 30 was associated with greater hazards of all-cause mortality in both sexes, but the association was stronger in females (female: HR 1.71, 95%CI 1.56-1.88; male versus female comparison HR 0.88, 95%CI 0.78-0.90). CONCLUSIONS: Lower or higher eGFR was not associated with substantially higher odds of presenting with advanced cancer across most cancer sites, but was associated with reduced survival. A stronger assocation with all-cause mortality in females compared to males with eGFR < 30 is concerning and warrants further scrutiny.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: A cross-sectional study using a large, primary care population dataset.

BACKGROUND: Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. METHODS AND FINDINGS: We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies. CONCLUSIONS: In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).

High-Sensitivity Cardiac Troponin on Presentation to Rule Out Myocardial Infarction: A Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: High-sensitivity cardiac troponin assays enable myocardial infarction to be ruled out earlier, but the safety and efficacy of this approach is uncertain. We investigated whether an early rule-out pathway is safe and effective for patients with suspected acute coronary syndrome. METHODS: We performed a stepped-wedge cluster randomized controlled trial in the emergency departments of 7 acute care hospitals in Scotland. Consecutive patients presenting with suspected acute coronary syndrome between December 2014 and December 2016 were included. Sites were randomized to implement an early rule-out pathway where myocardial infarction was excluded if high-sensitivity cardiac troponin I concentrations were <5 ng/L at presentation. During a previous validation phase, myocardial infarction was ruled out when troponin concentrations were <99th percentile at 6 to 12 hours after symptom onset. The coprimary outcome was length of stay (efficacy) and myocardial infarction or cardiac death after discharge at 30 days (safety). Patients were followed for 1 year to evaluate safety and other secondary outcomes. RESULTS: We enrolled 31 492 patients (59±17 years of age [mean±SD]; 45% women) with troponin concentrations <99th percentile at presentation. Length of stay was reduced from 10.1±4.1 to 6.8±3.9 hours (adjusted geometric mean ratio, 0.78 [95% CI, 0.73-0.83]; P<0.001) after implementation and the proportion of patients discharged increased from 50% to 71% (adjusted odds ratio, 1.59 [95% CI, 1.45-1.75]). Noninferiority was not demonstrated for the 30-day safety outcome (upper limit of 1-sided 95% CI for adjusted risk difference, 0.70% [noninferiority margin 0.50%]; P=0.068), but the observed differences favored the early rule-out pathway (0.4% [57/14 700] versus 0.3% [56/16 792]). At 1 year, the safety outcome occurred in 2.7% (396/14 700) and 1.8% (307/16 792) of patients before and after implementation (adjusted odds ratio, 1.02 [95% CI, 0.74-1.40]; P=0.894), and there were no differences in hospital reattendance or all-cause mortality. CONCLUSIONS: Implementation of an early rule-out pathway for myocardial infarction reduced length of stay and hospital admission. Although noninferiority for the safety outcome was not demonstrated at 30 days, there was no increase in cardiac events at 1 year. Adoption of this pathway would have major benefits for patients and health care providers. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03005158.

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies.

BACKGROUND: Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample. METHODS AND FINDINGS: These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included. CONCLUSIONS: In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

High-sensitivity cardiac troponin and the diagnosis of myocardial infarction in patients with kidney impairment.

The benefit and utility of high-sensitivity cardiac troponin (hs-cTn) in the diagnosis of myocardial infarction in patients with kidney impairment is unclear. Here, we describe implementation of hs-cTnI testing on the diagnosis, management, and outcomes of myocardial infarction in patients with and without kidney impairment. Consecutive patients with suspected acute coronary syndrome enrolled in a stepped-wedge, cluster-randomized controlled trial were included in this pre-specified secondary analysis. Kidney impairment was defined as an eGFR under 60mL/min/1.73m2. The index diagnosis and primary outcome of type 1 and type 4b myocardial infarction or cardiovascular death at one year were compared in patients with and without kidney impairment following implementation of hs-cTnI assay with 99th centile sex-specific diagnostic thresholds. Serum creatinine concentrations were available in 46,927 patients (mean age 61 years; 47% women), of whom 9,080 (19%) had kidney impairment. hs-cTnIs were over 99th centile in 46% and 16% of patients with and without kidney impairment. Implementation increased the diagnosis of type 1 infarction from 12.4% to 17.8%, and from 7.5% to 9.4% in patients with and without kidney impairment (both significant). Patients with kidney impairment and type 1 myocardial infarction were less likely to undergo coronary revascularization (26% versus 53%) or receive dual anti-platelets (40% versus 68%) than those without kidney impairment, and this did not change post-implementation. In patients with hs-cTnI above the 99th centile, the primary outcome occurred twice as often in those with kidney impairment compared to those without (24% versus 12%, hazard ratio 1.53, 95% confidence interval 1.31 to 1.78). Thus, hs-cTnI testing increased the identification of myocardial injury and infarction but failed to address disparities in management and outcomes between those with and without kidney impairment.

The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function.

BACKGROUND: Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes. METHODS: Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006-2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006-2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m2). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)). RESULTS: Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m2, most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m2, clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m2, in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31-3.07) and MACE HR is 4.18 (CI 3.65-4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22-3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m2, in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62-2.46) and MACE HR is 4.09 (CI 3.39-4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18-1.61) and MACE HR is 1.58 (CI 1.42-1.76). CONCLUSIONS: Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data.

BACKGROUND: The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care. METHODS: This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity. RESULTS: For 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55-0.64; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.87 (0.58-1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most. CONCLUSIONS: Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

The time-varying cardiovascular benefits of glucagon-like peptide-1 receptor agonist therapy in patients with type 2 diabetes mellitus: Evidence from large multinational trials.

AIMS: To evaluate the time-varying cardio-protective effect of glucagon-like peptide-1 receptor agonists (GLP-1RAs) using pooled data from eight contemporary cardiovascular outcome trials using the difference in the restricted mean survival time (ΔRMST) as the effect estimate. MATERIAL AND METHODS: Data from eight multinational cardiovascular outcome randomized controlled trials of GLP-1RAs for type 2 diabetes mellitus were pooled. Flexible parametric survival models were fit from published Kaplan-Meier plots. The differences between arms in RMST (ΔRMST) were calculated at 12, 24, 36 and 48 months. ΔRMST values were pooled using an inverse variance-weighted random-effects model; heterogeneity was tested with Cochran's Q statistic. The endpoints studied were: three-point major adverse cardiovascular events (MACE), all-cause mortality, stroke, cardiovascular mortality and myocardial infarction. RESULTS: We included eight large (3183-14 752 participants, total = 60 080; median follow-up range: 1.5 to 5.4 years) GLP-1RA trials. Among GLP-1RA recipients, we observed an average delay in three-point MACE of 0.03, 0.15, 0.37 and 0.63 months at 12, 24, 36 and 48 months, respectively. At 48 months, while cardiovascular mortality was comparable in both arms (pooled ΔRMST 0.163 [-0.112, 0.437]; P = 0.24), overall survival was higher (ΔRMST = 0.261 [0.08-0.43] months) and stroke was delayed (ΔRMST 0.22 [0.15-0.33]) in patients receiving GLP-1RAs. CONCLUSIONS: Glucagon-like peptide-1 receptor agonists may delay the occurrence of MACE by an average of 0.6 months at 48 months, with meaningfully larger gains in patients with cardiovascular disease. This metric may be easier for clinicians and patients to interpret than hazard ratios, which assume a knowledge of absolute risk in the absence of treatment.

Performance of the GRACE 2.0 score in patients with type 1 and type 2 myocardial infarction.

AIMS: The Global Registry of Acute Coronary Events (GRACE) score was developed to evaluate risk in patients with myocardial infarction. However, its performance in type 2 myocardial infarction is uncertain. METHODS AND RESULTS: In two cohorts of consecutive patients with suspected acute coronary syndrome from 10 hospitals in Scotland (n = 48 282) and a tertiary care hospital in Sweden (n = 22 589), we calculated the GRACE 2.0 score to estimate death at 1 year. Discrimination was evaluated by the area under the receiver operating curve (AUC), and compared for those with an adjudicated diagnosis of type 1 and type 2 myocardial infarction using DeLong's test. Type 1 myocardial infarction was diagnosed in 4981 (10%) and 1080 (5%) patients in Scotland and Sweden, respectively. At 1 year, 720 (15%) and 112 (10%) patients died with an AUC for the GRACE 2.0 score of 0.83 [95% confidence interval (CI) 0.82-0.85] and 0.85 (95% CI 0.81-0.89). Type 2 myocardial infarction occurred in 1121 (2%) and 247 (1%) patients in Scotland and Sweden, respectively, with 258 (23%) and 57 (23%) deaths at 1 year. The AUC was 0.73 (95% CI 0.70-0.77) and 0.73 (95% CI 0.66-0.81) in type 2 myocardial infarction, which was lower than for type 1 myocardial infarction in both cohorts (P < 0.001 and P = 0.008, respectively). CONCLUSION: The GRACE 2.0 score provided good discrimination for all-cause death at 1 year in patients with type 1 myocardial infarction, and moderate discrimination for those with type 2 myocardial infarction. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01852123.

Incidence, Microbiology, and Outcomes in Patients Hospitalized With Infective Endocarditis.

BACKGROUND: Despite improvements in management, infective endocarditis remains associated with high mortality and morbidity. We describe temporal changes in the incidence, microbiology, and outcomes of infective endocarditis and the effect of changes in national antibiotic prophylaxis guidelines on incident infective endocarditis. METHODS: Using a Scotland-wide, individual-level linkage approach, all patients hospitalized with infective endocarditis from 1990 to 2014 were identified and linked to national microbiology, prescribing, and morbidity and mortality datasets. Linked data were used to evaluate trends in the crude and age- and sex-adjusted incidence and outcomes of infective endocarditis hospitalizations. From 2008, microbiology data and associated outcomes adjusted for patient demographics and comorbidity were also analyzed. An interrupted time series analysis was performed to evaluate incidence before and after changes to national antibiotic prophylaxis guidelines. RESULTS: There were 7638 hospitalizations (65±17 years, 51% females) with infective endocarditis. The estimated crude hospitalization rate increased from 5.3/100 000 (95% CI, 4.8-5.9) to 8.6/100 000 (95% CI, 8.1-9.1) between 1990 and 1995 but remained stable thereafter. There was no change in crude incidence following the 2008 change in antibiotic prophylaxis guidelines (relative risk of change 1.06 [95% CI, 0.94-1.20]). The incidence rate in patients >80 years of age doubled from 1990 to 2014 (17.7/100 000 [95% CI, 13.4-23.3] to 37.9/100 000 [95% CI, 31.5-45.5]). The predicted 1-year age- and comorbidity-adjusted case fatality rate for a 65-year-old patient decreased in women (27.3% [95% CI, 24.6-30.2] to 23.7% [95% CI, 21.1-26.6]) and men (30.7% [95% CI, 27.7-33.8] to 26.8% [95% CI, 24.0-29.7]) from 1990 to 2014. Blood culture data were available from 2008 (n=2267/7638, 30%), with positive blood cultures recorded in 42% (950/2267). Staphylococcus (403/950, 42.4%) and streptococcus (337/950, 35.5%) species were most common. Staphylococcus aureus and enterococcus had the highest 1-year mortality (adjusted odds ratio 4.34 [95% CI, 3.12-6.05] and 3.41 [95% CI, 2.04-5.70], respectively). CONCLUSIONS: Despite changes in antibiotic prophylaxis guidelines, the crude incidence of infective endocarditis has remained stable. However, the incidence rate has doubled in the elderly. Positive blood cultures were observed in less than half of patients, with Staphylococcus aureus and enterococcus bacteremia associated with worse outcomes.

High-Sensitivity Cardiac Troponin and the Universal Definition of Myocardial Infarction.

BACKGROUND: The introduction of more sensitive cardiac troponin assays has led to increased recognition of myocardial injury in acute illnesses other than acute coronary syndrome. The Universal Definition of Myocardial Infarction recommends high-sensitivity cardiac troponin testing and classification of patients with myocardial injury based on pathogenesis, but the clinical implications of implementing this guideline are not well understood. METHODS: In a stepped-wedge cluster randomized, controlled trial, we implemented a high-sensitivity cardiac troponin assay and the recommendations of the Universal Definition in 48 282 consecutive patients with suspected acute coronary syndrome. In a prespecified secondary analysis, we compared the primary outcome of myocardial infarction or cardiovascular death and secondary outcome of noncardiovascular death at 1 year across diagnostic categories. RESULTS: Implementation increased the diagnosis of type 1 myocardial infarction by 11% (510/4471), type 2 myocardial infarction by 22% (205/916), and acute and chronic myocardial injury by 36% (443/1233) and 43% (389/898), respectively. Compared with those without myocardial injury, the rate of the primary outcome was highest in those with type 1 myocardial infarction (cause-specific hazard ratio [HR] 5.64 [95% CI, 5.12-6.22]), but was similar across diagnostic categories, whereas noncardiovascular deaths were highest in those with acute myocardial injury (cause specific HR 2.65 [95% CI, 2.33-3.01]). Despite modest increases in antiplatelet therapy and coronary revascularization after implementation in patients with type 1 myocardial infarction, the primary outcome was unchanged (cause specific HR 1.00 [95% CI, 0.82-1.21]). Increased recognition of type 2 myocardial infarction and myocardial injury did not lead to changes in investigation, treatment or outcomes. CONCLUSIONS: Implementation of high-sensitivity cardiac troponin assays and the recommendations of the Universal Definition of Myocardial Infarction identified patients at high-risk of cardiovascular and noncardiovascular events but was not associated with consistent increases in treatment or improved outcomes. Trials of secondary prevention are urgently required to determine whether this risk is modifiable in patients without type 1 myocardial infarction. CLINICAL TRIAL REGISTRATION: https://www.clinicaltrials.gov. Unique identifier: NCT01852123.

Relation of severe COVID-19 in Scotland to transmission-related factors and risk conditions eligible for shielding support: REACT-SCOT case-control study.

BACKGROUND: Clinically vulnerable individuals have been advised to shield themselves during the COVID-19 epidemic. The objectives of this study were to investigate (1) the rate ratio of severe COVID-19 associated with eligibility for the shielding programme in Scotland across the first and second waves of the epidemic and (2) the relation of severe COVID-19 to transmission-related factors in those in shielding and the general population. METHODS: In a matched case-control design, all 178,578 diagnosed cases of COVID-19 in Scotland from 1 March 2020 to 18 February 2021 were matched for age, sex and primary care practice to 1,744,283 controls from the general population. This dataset (REACT-SCOT) was linked to the list of 212,702 individuals identified as eligible for shielding. Severe COVID-19 was defined as cases that entered critical care or were fatal. Rate ratios were estimated by conditional logistic regression. RESULTS: With those without risk conditions as reference category, the univariate rate ratio for severe COVID-19 was 3.21 (95% CI 3.01 to 3.41) in those with moderate risk conditions and 6.3 (95% CI 5.8 to 6.8) in those eligible for shielding. The highest rate was in solid organ transplant recipients: rate ratio 13.4 (95% CI 9.6 to 18.8). Risk of severe COVID-19 increased with the number of adults but decreased with the number of school-age children in the household. Severe COVID-19 was strongly associated with recent exposure to hospital (defined as 5 to 14 days before presentation date): rate ratio 12.3 (95% CI 11.5 to 13.2) overall. The population attributable risk fraction for recent exposure to hospital peaked at 50% in May 2020 and again at 65% in December 2020. CONCLUSIONS: The effectiveness of shielding vulnerable individuals was limited by the inability to control transmission in hospital and from other adults in the household. Mitigating the impact of the epidemic requires control of nosocomial transmission.

Sex Differences in Cardiac Troponin I and T and the Prediction of Cardiovascular Events in the General Population.

BACKGROUND: Cardiac troponin concentrations differ in women and men, but how this influences risk prediction and whether a sex-specific approach is required is unclear. We evaluated whether sex influences the predictive ability of cardiac troponin I and T for cardiovascular events in the general population. METHODS: High-sensitivity cardiac troponin (hs-cTn) I and T were measured in the Generation Scotland Scottish Family Health Study of randomly selected volunteers drawn from the general population between 2006 and 2011. Cox-regression models evaluated associations between hs-cTnI and hs-cTnT and the primary outcome of cardiovascular death, myocardial infarction, or stroke. RESULTS: In 19 501 (58% women, mean age 47 years) participants, the primary outcome occurred in 2.7% (306/11 375) of women and 5.1% (411/8126) of men during the median follow-up period of 7.9 (IQR, 7.1-9.2) years. Cardiac troponin I and T concentrations were lower in women than men (P < 0.001 for both), and both were more strongly associated with cardiovascular events in women than men. For example, at a hs-cTnI concentration of 10 ng/L, the hazard ratio relative to the limit of blank was 9.7 (95% CI 7.6-12.4) and 5.6 (95% CI 4.7-6.6) for women and men, respectively. The hazard ratio for hs-cTnT at a concentration of 10 ng/L relative to the limit of blank was 3.7 (95% CI 3.1-4.3) and 2.2 (95% CI 2.0-2.5) for women and men, respectively. CONCLUSIONS: Cardiac troponin concentrations differ in women and men and are stronger predictors of cardiovascular events in women. Sex-specific approaches are required to provide equivalent risk prediction.

Association of cerebral venous thrombosis with recent COVID-19 vaccination: case-crossover study using ascertainment through neuroimaging in Scotland.

BACKGROUND: To investigate the association of primary acute cerebral venous thrombosis (CVT) with COVID-19 vaccination through complete ascertainment of all diagnosed CVT in the population of Scotland. METHODS: Case-crossover study comparing cases of CVT recently exposed to vaccination (1-14 days after vaccination) with cases less recently exposed. Cases in Scotland from 1 December 2020 were ascertained through neuroimaging studies up to 17 May 2021 and diagnostic coding of hospital discharges up to 28 April 2021, linked to national vaccination records. The main outcome measure was primary acute CVT. RESULTS: Of 50 primary acute CVT cases, 29 were ascertained only from neuroimaging studies, 2 were ascertained only from hospital discharges, and 19 were ascertained from both sources. Of these 50 cases, 14 had received the Astra-Zeneca ChAdOx1 vaccine and 3 the Pfizer BNT162b2 vaccine. The incidence of CVT per million doses in the first 14 days after vaccination was 2.2 (95% credible interval 0.9 to 4.1) for ChAdOx1 and 1 (95% credible interval 0.1 to 2.9) for BNT162b2. The rate ratio for CVT associated with exposure to ChAdOx1 in the first 14 days compared with exposure 15-84 days after vaccination was 3.2 (95% credible interval 1.1 to 9.5). CONCLUSIONS: These findings support a causal association between CVT and the AstraZeneca vaccine. The absolute risk of post-vaccination CVT in this population-wide study in Scotland was lower than has been reported for populations in Scandinavia and Germany; the explanation for this is not clear.

Impact of COVID-19 on care-home mortality and life expectancy in Scotland.

BACKGROUND: COVID-19 deaths are commoner among care-home residents, but the mortality burden has not been quantified. METHODS: Care-home residency was identified via a national primary care registration database linked to mortality data. Life expectancy was estimated using Makeham-Gompertz models to (i) describe yearly life expectancy from November 2015 to October 2020 (ii) compare life expectancy (during 2016-18) between care-home residents and the wider population and (iii) apply care-home life expectancy estimates to COVID-19 death counts to estimate years of life lost (YLL). RESULTS: Among care-home residents, life expectancy in 2015/16 to 2019/20 ranged from 2.7 to 2.3 years for women and 2.3 to 1.8 years for men. Age-sex-specific life expectancy in 2016-18 in care-home residents was lower than in the Scottish population (10 and 2.5 years in those aged 70 and 90, respectively). Applying care home-specific life expectancies to COVID-19 deaths yield mean YLLs for care-home residents of 2.6 and 2.2 for women and men, respectively. In total YLL care-home residents have lost 3,560 years in women and 2,046 years in men. Approximately half of deaths and a quarter of YLL attributed to COVID-19 were accounted for by the 5% of over-70s who were care-home residents. CONCLUSION: COVID-19 infection has led to the loss of substantial years of life in care-home residents aged 70 years and over in Scotland. Prioritising the 5% of older adults who are care-home residents for vaccination is justified not only in terms of total deaths, but also in terms of YLL.

Sex associations and computed tomography coronary angiography-guided management in patients with stable chest pain.

AIMS: The relative benefits of computed tomography coronary angiography (CTCA)-guided management in women and men with suspected angina due to coronary heart disease (CHD) are uncertain. METHODS AND RESULTS: In this post hoc analysis of an open-label parallel-group multicentre trial, we recruited 4146 patients referred for assessment of suspected angina from 12 cardiology clinics across the UK. We randomly assigned (1:1) participants to standard care alone or standard care plus CTCA. Fewer women had typical chest pain symptoms (n = 582, 32.0%) when compared with men (n = 880, 37.9%; P < 0.001). Amongst the CTCA-guided group, more women had normal coronary arteries [386 (49.6%) vs. 263 (26.2%)] and less obstructive CHD [105 (11.5%) vs. 347 (29.8%)]. A CTCA-guided strategy resulted in more women than men being reclassified as not having CHD {19.2% vs. 13.1%; absolute risk difference, 5.7 [95% confidence interval (CI): 2.7-8.7, P < 0.001]} or having angina due to CHD [15.0% vs. 9.0%; absolute risk difference, 5.6 (2.3-8.9, P = 0.001)]. After a median of 4.8 years follow-up, CTCA-guided management was associated with similar reductions in the risk of CHD death or non-fatal myocardial infarction in women [hazard ratio (HR) 0.50, 95% CI 0.24-1.04], and men (HR 0.63, 95% CI 0.42-0.95; Pinteraction = 0.572). CONCLUSION: Following the addition of CTCA, women were more likely to be found to have normal coronary arteries than men. This led to more women being reclassified as not having CHD, resulting in more downstream tests and treatments being cancelled. There were similar prognostic benefits of CTCA for women and men.

Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions.

BACKGROUND: Frailty is common in clinical practice, but trials rarely report on participant frailty. Consequently, clinicians and guideline-developers assume frailty is largely absent from trials and have questioned the relevance of trial findings to frail people. Therefore, we examined frailty in phase 3/4 industry-sponsored clinical trials of pharmacological interventions for three exemplar conditions: type 2 diabetes mellitus (T2DM), rheumatoid arthritis (RA), and chronic obstructive pulmonary disease (COPD). METHODS: We constructed a 40-item frailty index (FI) in 19 clinical trials (7 T2DM, 8 RA, 4 COPD, mean age 42-65 years) using individual-level participant data. Participants with a FI > 0.24 were considered 'frail'. Baseline disease severity was assessed using HbA1c for T2DM, Disease Activity Score-28 (DAS28) for RA, and % predicted FEV1 for COPD. Using generalised gamma regression, we modelled FI on age, sex, and disease severity. In negative binomial regression, we modelled serious adverse event rates on FI and combined results for each index condition in a random-effects meta-analysis. RESULTS: All trials included frail participants: prevalence 7-21% in T2DM trials, 33-73% in RA trials, and 15-22% in COPD trials. The 99th centile of the FI ranged between 0.35 and 0.45. Female sex was associated with higher FI in all trials. Increased disease severity was associated with higher FI in RA and COPD, but not T2DM. Frailty was associated with age in T2DM and RA trials, but not in COPD. Across all trials, and after adjusting for age, sex, and disease severity, higher FI predicted increased risk of serious adverse events; the pooled incidence rate ratios (per 0.1-point increase in FI scale) were 1.46 (95% CI 1.21-1.75), 1.45 (1.13-1.87), and 1.99 (1.43-2.76) for T2DM, RA, and COPD, respectively. CONCLUSION: The upper limit of frailty in trials is lower than has been described in the general population. However, mild to moderate frailty was common, suggesting trial data may be harnessed to inform disease management in people living with frailty. Participants with higher FI experienced more serious adverse events, suggesting screening for frailty in trial participants would enable identification of those that merit closer monitoring. Frailty is identifiable and prevalent among middle-aged and older participants in phase 3/4 drug trials and has clinically important safety implications.