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Acute exposure to air pollution has been linked to myocardial infarction, but its effect on heart failure is uncertain. We did a systematic review and meta-analysis to assess the association between air pollution and acute decompensated heart failure including hospitalisation and heart failure mortality. Five databases were searched for studies investigating the association between daily increases in gaseous (carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone) and particulate (diameter <2·5 μm [PM2·5] or <10 μm [PM10]) air pollutants, and heart failure hospitalisations or heart failure mortality. We used a random-effects model to derive overall risk estimates per pollutant. Of 1146 identified articles, 195 were reviewed in-depth with 35 satisfying inclusion criteria. Heart failure hospitalisation or death was associated with increases in carbon monoxide (3·52% per 1 part per million; 95% CI 2·52–4·54), sulphur dioxide (2·36% per 10 parts per billion; 1·35–3·38), and nitrogen dioxide (1·70% per 10 parts per billion; 1·25–2·16), but not ozone (0·46% per 10 parts per billion; −0·10 to 1·02) concentrations. Increases in particulate matter concentration were associated with heart failure hospitalisation or death (PM2·5 2·12% per 10 μg/m3, 95% CI 1·42–2·82; PM10 1·63% per 10 μg/m3, 95% CI 1·20–2·07). Strongest associations were seen on the day of exposure, with more persistent effects for PM2·5. In the USA, we estimate that a mean reduction in PM2·5 of 3·9 μg/m3 would prevent 7978 heart failure hospitalisations and save a third of a billion US dollars a year. Air pollution has a close temporal association with heart failure hospitalisation and heart failure mortality. Although more studies from developing nations are required, air pollution is a pervasive public health issue with major cardiovascular and health economic consequences, and it should remain a key target for global health policy. British Heart Foundation.

In this report from Scotland, vaccination of health care workers for SARS-CoV-2 was associated with a decrease in household transmission.

There is limited evidence on the use of high-sensitivity C-reactive protein (hsCRP) as a biomarker for selecting patients for advanced cardiovascular (CV) therapies in the modern era. The prognostic value of mildly elevated hsCRP beyond troponin in a large real-world cohort of unselected patients presenting with suspected acute coronary syndrome (ACS) is unknown. We evaluated whether a mildly elevated hsCRP (up to 15 mg/L) was associated with mortality risk, beyond troponin level, in patients with suspected ACS. We conducted a retrospective cohort study based on the National Institute for Health Research Health Informatics Collaborative data of 257,948 patients with suspected ACS who had a troponin measured at 5 cardiac centres in the United Kingdom between 2010 and 2017. Patients were divided into 4 hsCRP groups (<2, 2 to 4.9, 5 to 9.9, and 10 to 15 mg/L). The main outcome measure was mortality within 3 years of index presentation. The association between hsCRP levels and all-cause mortality was assessed using multivariable Cox regression analysis adjusted for age, sex, haemoglobin, white cell count (WCC), platelet count, creatinine, and troponin. Following the exclusion criteria, there were 102,337 patients included in the analysis (hsCRP <2 mg/L (n = 38,390), 2 to 4.9 mg/L (n = 27,397), 5 to 9.9 mg/L (n = 26,957), and 10 to 15 mg/L (n = 9,593)). On multivariable Cox regression analysis, there was a positive and graded relationship between hsCRP level and mortality at baseline, which remained at 3 years (hazard ratio (HR) (95% CI) of 1.32 (1.18 to 1.48) for those with hsCRP 2.0 to 4.9 mg/L and 1.40 (1.26 to 1.57) and 2.00 (1.75 to 2.28) for those with hsCRP 5 to 9.9 mg/L and 10 to 15 mg/L, respectively. This relationship was independent of troponin in all suspected ACS patients and was further verified in those who were confirmed to have an ACS diagnosis by clinical coding. The main limitation of our study is that we did not have data on underlying cause of death; however, the exclusion of those with abnormal WCC or hsCRP levels >15 mg/L makes it unlikely that sepsis was a major contributor. These multicentre, real-world data from a large cohort of patients with suspected ACS suggest that mildly elevated hsCRP (up to 15 mg/L) may be a clinically meaningful prognostic marker beyond troponin and point to its potential utility in selecting patients for novel treatments targeting inflammation. ClinicalTrials.gov - NCT03507309.

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning. Using a cohort of 80,000 patients from the CALIBER programme, we compared traditional modelling and machine-learning approaches in EHR. First, we used Cox models and random survival forests with and without imputation on 27 expert-selected, preprocessed variables to predict all-cause mortality. We then used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input. We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values. We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate. This demonstrates that machine-learning approaches applied to raw EHR data can be used to build models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

Background A raised stress hyperglycaemia ratio (SHR) has been associated with all-cause mortality and may better discriminate than an absolute glucose value. The aim of this meta analysis and systematic review is to synthesise the evidence assessing the relationship between the SHR and all-cause mortality across three common cardiovascular presentations. Methods We undertook a comprehensive search of Medline, Embase, Cochrane CENTRAL and Web of Science from the date of inception to 1st March 2024, and selected articles meeting the following criteria: studies of patients hospitalised for acute myocardial infarction, ischaemic stroke or acute heart failure reporting the risk (odds ratio or hazard ratio) for all-cause mortality associated with the SHR. A random effects model was used for primary analysis. Subgroup analysis by diabetes status and of mortality in the short and long term was undertaken. Risk of bias assessment was performed using the Newcastle Ottawa quality assessment scale. Results A total of 32 studies were included: 26 studies provided 31 estimates for the meta-analysis. The total study population in the meta analysis was 80,010. Six further studies were included in the systematic review. Participants admitted to hospital with cardiovascular disease and an SHR in the highest category had a significantly higher risk ratio of all-cause mortality in both the short and longer term compared with those with a lower SHR (RR = 1.67 [95% CI 1.46–1.91], p < 0.001). This finding was driven by studies in the myocardial infarction (RR = 1.75 [95% CI 1.52–2.01]), and ischaemic stroke cohorts (RR = 1.78 [95% CI 1.26–2.50]). The relationship was present amongst those with and without diabetes (diabetes: RR 1.49 [95% CI 1.14–1.94], p < 0.001, no diabetes: RR 1.85 [95% CI 1.49–2.30], p < 0.001) with p = 0.21 for subgroup differences, and amongst studies that reported mortality as a single outcome (RR of 1.51 ([95% CI 1.29–1.77]; p < 0.001) and those that reported mortality as part of a composite outcome (RR 2.02 [95% CI 1.58–2.59]; p < 0.001). On subgroup analysis by length of follow up, higher SHR values were associated with increased risk of mortality at 90 day, 1 year and > 1year follow up, with risk ratios of 1.84 ([95% CI 1.32–2.56], p < 0.001), 1.69 ([95% CI 1.32–2.16], p < 0.001) and 1.58 ([95% CI 1.34–1.86], p < 0.001) respectively. Conclusions A raised SHR is associated with an increased risk of all-cause mortality following myocardial infarction and ischaemic stroke. Further work is required to define reference values for the SHR, and to investigate the potential effects of relative hypoglycaemia. Interventional trials targeting to the SHR rather than the absolute glucose value should be undertaken. PROSPERO database registration CRD 42023456421 https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023456421

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. 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. 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. A prespecified protocol was registered on PROSPERO (CRD42018048202).

Few data compare cardiac troponin T (cTnT) and cardiac troponin I (cTnI) in a general population. We sought to evaluate the distribution and association between cTnT, cTnI, and cardiovascular risk factors in a large general population cohort. High-sensitivity cTnT and cTnI were measured in serum from 19501 individuals in the Generation Scotland Scottish Family Health Study. Associations with cardiovascular risk factors were compared using age- and sex-adjusted regression. Observed age- and sex-stratified 99th centiles were compared with 99th centiles for cTnT (men, 15.5 ng/L; women, 9.0 ng/L) and cTnI (men, 34.2 ng/L; women, 15.6 ng/L) used in clinical practice. cTnT and cTnI concentrations were detectable in 53.3% and 74.8% of participants, respectively, and were modestly correlated in unadjusted analyses ( = 21.3%) and only weakly correlated after adjusting for age and sex ( = 9.5%). Cardiovascular risk factors were associated with both troponins, but in age- and sex-adjusted analyses, cTnI was more strongly associated with age, male sex, body mass index, and systolic blood pressure ( < 0.0001 for all vs cTnT). cTnT was more strongly associated with diabetes ( < 0.0001 vs cTnI). The observed 99th centiles were broadly consistent with recommended 99th centiles in younger men and women. After the age of 60 years, observed 99th centiles increased substantially for cTnT, and beyond 70 years of age, the 99th centiles approximately doubled for both troponins. In the general population, cTnT and cTnI concentrations are weakly correlated and are differentially associated with cardiovascular risk factors. The 99th centiles currently in use are broadly appropriate for men and women up to but not beyond the age of 60 years.

AbstractObjectiveTo assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.DesignNationwide linkage cohort study.SettingScotland, UK, 1 March to 6 June 2020.ParticipantsHealthcare workers aged 18-65 years, their households, and other members of the general population.Main outcome measureAdmission to hospital with covid-19.ResultsThe cohort comprised 158 445 healthcare workers, most of them (90 733; 57.3%) being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazard ratio 0.81 (95% confidence interval 0.52 to 1.26) and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk (hazard ratio 3.30, 2.13 to 5.13), as were household members of patient facing healthcare workers (1.79, 1.10 to 2.91). After sub-division of patient facing healthcare workers into those who worked in “front door,” intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk (hazard ratio 2.09, 1.49 to 2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.ConclusionsHealthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19.

The associations of blood pressure with the different manifestations of incident cardiovascular disease in a contemporary population have not been compared. In this study, we aimed to analyse the associations of blood pressure with 12 different presentations of cardiovascular disease. We used linked electronic health records from 1997 to 2010 in the CALIBER (CArdiovascular research using LInked Bespoke studies and Electronic health Records) programme to assemble a cohort of 1·25 million patients, 30 years of age or older and initially free from cardiovascular disease, a fifth of whom received blood pressure-lowering treatments. We studied the heterogeneity in the age-specific associations of clinically measured blood pressure with 12 acute and chronic cardiovascular diseases, and estimated the lifetime risks (up to 95 years of age) and cardiovascular disease-free life-years lost adjusted for other risk factors at index ages 30, 60, and 80 years. This study is registered at ClinicalTrials.gov, number NCT01164371. During 5·2 years median follow-up, we recorded 83 098 initial cardiovascular disease presentations. In each age group, the lowest risk for cardiovascular disease was in people with systolic blood pressure of 90–114 mm Hg and diastolic blood pressure of 60–74 mm Hg, with no evidence of a J-shaped increased risk at lower blood pressures. The effect of high blood pressure varied by cardiovascular disease endpoint, from strongly positive to no effect. Associations with high systolic blood pressure were strongest for intracerebral haemorrhage (hazard ratio 1·44 [95% CI 1·32–1·58]), subarachnoid haemorrhage (1·43 [1·25–1·63]), and stable angina (1·41 [1·36–1·46]), and weakest for abdominal aortic aneurysm (1·08 [1·00–1·17]). Compared with diastolic blood pressure, raised systolic blood pressure had a greater effect on angina, myocardial infarction, and peripheral arterial disease, whereas raised diastolic blood pressure had a greater effect on abdominal aortic aneurysm than did raised systolic pressure. Pulse pressure associations were inverse for abdominal aortic aneurysm (HR per 10 mm Hg 0·91 [95% CI 0·86–0·98]) and strongest for peripheral arterial disease (1·23 [1·20–1·27]). People with hypertension (blood pressure ≥140/90 mm Hg or those receiving blood pressure-lowering drugs) had a lifetime risk of overall cardiovascular disease at 30 years of age of 63·3% (95% CI 62·9–63·8) compared with 46·1% (45·5–46·8) for those with normal blood pressure, and developed cardiovascular disease 5·0 years earlier (95% CI 4·8–5·2). Stable and unstable angina accounted for most (43%) of the cardiovascular disease-free years of life lost associated with hypertension from index age 30 years, whereas heart failure and stable angina accounted for the largest proportion (19% each) of years of life lost from index age 80 years. The widely held assumptions that blood pressure has strong associations with the occurrence of all cardiovascular diseases across a wide age range, and that diastolic and systolic associations are concordant, are not supported by the findings of this high-resolution study. Despite modern treatments, the lifetime burden of hypertension is substantial. These findings emphasise the need for new blood pressure-lowering strategies, and will help to inform the design of randomised trials to assess them. Medical Research Council, National Institute for Health Research, and Wellcome Trust.

The universal definition of myocardial infarction (UDMI) standardizes the approach to the diagnosis and management of myocardial infarction. High-sensitivity cardiac troponin testing is recommended because these assays have improved precision at low concentrations, but concerns over specificity may have limited their implementation. We undertook a global survey of 1902 medical centers in 23 countries evenly distributed across 5 continents to assess adoption of key recommendations from the UDMI. Respondents involved in the diagnosis and management of patients with suspected acute coronary syndrome completed a structured telephone questionnaire detailing the primary biomarker, diagnostic thresholds, and clinical pathways used to identify myocardial infarction. Cardiac troponin was the primary diagnostic biomarker at 96% of surveyed sites. Only 41% of centers had adopted high-sensitivity assays, with wide variation from 7% in North America to 60% in Europe. Sites using high-sensitivity troponin more frequently used serial sampling pathways (91% vs 78%) and the 99th percentile diagnostic threshold (74% vs 66%) than sites using previous-generation assays. Furthermore, high-sensitivity institutions more often used earlier serial sampling (≤3 h) and accelerated diagnostic pathways. Fewer than 1 in 5 high-sensitivity sites had adopted sex-specific thresholds (18%). There has been global progress toward the recommendations of the UDMI, particularly in the use of the 99th percentile diagnostic threshold and serial sampling. However, high-sensitivity assays are still used by a minority of sites, and sex-specific thresholds by even fewer. Additional efforts are required to improve risk stratification and diagnosis of patients with myocardial infarction.