Long COVID and cardiovascular disease: a prospective cohort study.

BACKGROUND: Pre-existing cardiovascular disease (CVD) or cardiovascular risk factors have been associated with an increased risk of complications following hospitalisation with COVID-19, but their impact on the rate of recovery following discharge is not known. OBJECTIVES: To determine whether the rate of patient-perceived recovery following hospitalisation with COVID-19 was affected by the presence of CVD or cardiovascular risk factors. METHODS: In a multicentre prospective cohort study, patients were recruited following discharge from the hospital with COVID-19 undertaking two comprehensive assessments at 5 months and 12 months. Patients were stratified by the presence of either CVD or cardiovascular risk factors prior to hospitalisation with COVID-19 and compared with controls with neither. Full recovery was determined by the response to a patient-perceived evaluation of full recovery from COVID-19 in the context of physical, physiological and cognitive determinants of health. RESULTS: From a total population of 2545 patients (38.8% women), 472 (18.5%) and 1355 (53.2%) had CVD or cardiovascular risk factors, respectively. Compared with controls (n=718), patients with CVD and cardiovascular risk factors were older and more likely to have had severe COVID-19. Full recovery was significantly lower at 12 months in patients with CVD (adjusted OR (aOR) 0.62, 95% CI 0.43 to 0.89) and cardiovascular risk factors (aOR 0.66, 95% CI 0.50 to 0.86). CONCLUSION: Patients with CVD or cardiovascular risk factors had a delayed recovery at 12 months following hospitalisation with COVID-19. Targeted interventions to reduce the impact of COVID-19 in patients with cardiovascular disease remain an unmet need. TRAIL REGISTRATION NUMBER: ISRCTN10980107.

Review of codelists used to define hypertension in electronic health records and development of a codelist for research.

BACKGROUND AND AIMS: Hypertension is a leading risk factor for cardiovascular disease. Electronic health records (EHRs) are routinely collected throughout a person's care, recording all aspects of health status, including current and past conditions, prescriptions and test results. EHRs can be used for epidemiological research. However, there are nuances in the way conditions are recorded using clinical coding; it is important to understand the methods which have been applied to define exposures, covariates and outcomes to enable interpretation of study findings. This study aimed to identify codelists used to define hypertension in studies that use EHRs and generate recommended codelists to support reproducibility and consistency. ELIGIBILITY CRITERIA: Studies included populations with hypertension defined within an EHR between January 2010 and August 2023 and were systematically identified using MEDLINE and Embase. A summary of the most frequently used sources and codes is described. Due to an absence of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codelists in the literature, a recommended SNOMED CT codelist was developed to aid consistency and standardisation of hypertension research using EHRs. FINDINGS: 375 manuscripts met the study criteria and were eligible for inclusion, and 112 (29.9%) reported codelists. The International Classification of Diseases (ICD) was the most frequently used clinical terminology, 59 manuscripts provided ICD 9 codelists (53%) and 58 included ICD 10 codelists (52%). Informed by commonly used ICD and Read codes, usage recommendations were made. We derived SNOMED CT codelists informed by National Institute for Health and Care Excellence guidelines for hypertension management. It is recommended that these codelists be used to identify hypertension in EHRs using SNOMED CT codes. CONCLUSIONS: Less than one-third of hypertension studies using EHRs included their codelists. Transparent methodology for codelist creation is essential for replication and will aid interpretation of study findings. We created SNOMED CT codelists to support and standardise hypertension definitions in EHR studies.

Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK.

BACKGROUND: Determining heart failure (HF) phenotypes in routine electronic health records (EHR) is challenging. We aimed to develop and validate EHR algorithms for identification of specific HF phenotypes, using Read codes in combination with selected patient characteristics. METHODS: We used The Healthcare Improvement Network (THIN). The study population included a random sample of individuals with HF diagnostic codes (HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF) and non-specific HF) selected from all participants registered in the THIN database between 1 January 2015 and 30 September 2017. Confirmed diagnoses were determined in a randomly selected subgroup of 500 patients via GP questionnaires including a review of all available cardiovascular investigations. Confirmed diagnoses of HFrEF and HFpEF were based on four criteria. Based on these data, we calculated a positive predictive value (PPV) of predefined algorithms which consisted of a combination of Read codes and additional information such as echocardiogram results and HF medication records. RESULTS: The final cohort from which we drew the 500 patient random sample consisted of 10 275 patients. Response rate to the questionnaire was 77.2%. A small proportion (18%) of the overall HF patient population were coded with specific HF phenotype Read codes. For HFrEF, algorithms achieving over 80% PPV included definite, possible or non-specific HF HFrEF codes when combined with at least two of the drugs used to treat HFrEF. Only in non-specific HF coding did the use of three drugs (rather than two) contribute to an improvement of the PPV for HFrEF. HFpEF was only accurately defined with specific codes. In the absence of specific coding for HFpEF, the PPV was consistently below 50%. CONCLUSIONS: Prescription for HF medication can reliably be used to find HFrEF patients in the UK, even in the absence of a specific Read code for HFrEF. Algorithms using non-specific coding could not reliably find HFpEF patients.

Impact of comorbidities on peak troponin levels and mortality in acute myocardial infarction.

OBJECTIVES: To characterise peak cardiac troponin levels, in patients presenting with acute myocardial infarction (AMI), according to their comorbid condition and determine the influence of peak cardiac troponin (cTn) levels on mortality. METHODS: We included patients with the first admission for AMI in the UK. We used linear regression to estimate the association between eight common comorbidities (diabetes mellitus, previous angina, peripheral arterial disease, previous myocardial infarction (MI), chronic kidney disease (CKD), cerebrovascular disease, chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD)) and peak cTn. Peak cTn levels were adjusted for age, sex, smoking status and comorbidities. Logistic regression and restricted cubic spline models were employed to investigate the association between peak cTn and 180-day mortality for each comorbidity. RESULTS: 330 367 patients with ST elevation myocardial infarction and non-ST elevation myocardial infarction were identified. Adjusted peak cTn levels were significantly higher in patients with CKD (adjusted % difference in peak cTnT for CKD=42%, 95% CI 13.1 to 78.4) and significantly lower for patients with COPD, previous angina, previous MI and CHF when compared with patients without the respective comorbidities (reference group) (cTnI; COPD=-21.7%, 95% CI -29.1 to -13.4; previous angina=-24.2%, 95% CI -29.6 to -8.3; previous MI=-13.5%, 95% CI -20.6 to -5.9; CHF=-28%, 95% CI -37.2 to -17.6). Risk of 180-day mortality in most of the comorbidities did not change substantially after adjusting for peak cTn. In general, cTnI had a stronger association with mortality than cTnT. CONCLUSIONS: In this nationwide analysis of patients presenting with AMI, comorbidities substantially influenced systemic concentrations of peak cTn. Comorbid illness is a significant predictor of mortality regardless of peak cTn levels and should be taken into consideration while interpreting cTn both as a diagnostic and prognostic biomarker.

Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.

BACKGROUND: COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. METHODS: We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. RESULTS: We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. CONCLUSIONS: COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients.

Mortality after admission for heart failure in the UK compared with Japan.

Objective: Mortality amongst patients hospitalised for heart failure (HHF) in Western and Asian countries may differ, but this has not been investigated using individual patient-level data (IPLD). We sought to remedy this through rigorous statistical analysis of HHF registries and variable selection from a systematic literature review. Methods and results: IPLD from registries of HHF in Japan (n=3781) and the UK (n=894) were obtained. A systematic literature review identified 23 models for predicting outcome of HHF. Five variables appearing in 10 or more reports were strongly related to prognosis (systolic blood pressure, serum sodium concentration, age, blood urea nitrogen and creatinine). To compare mortality in the UK and Japan, variables were imputed in a propensity model using inverse probability of treatment weighting (IPTW) and IPTW with logistic regression (doubly robust IPTW). Overall, patients in the UK were sicker and in-patient and post-discharge mortalities were greater, suggesting that the threshold for hospital admission was higher. Covariate-adjusted in-hospital mortality was similar in the UK and Japan (IPTW OR: 1.14, 95% CI 0.70 to 1.86), but 180-day postdischarge mortality was substantially higher in the UK (doubly robust IPTW OR: 2.33, 95% CI 1.58 to 3.43). Conclusions: Despite robust methods to adjust for differences in patient characteristics and disease severity, HHF patients in the UK have roughly twice the mortality at 180 days compared with those in Japan. Similar analyses should be done using other data sets and in other countries to determine the consistency of these findings and identify factors that might inform healthcare policy and improve outcomes.

No association between exacerbation frequency and stroke in patients with COPD.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have a higher risk of stroke than the general population. Chronic inflammation associated with COPD is thought to contribute to this risk. Exacerbations of COPD are associated with a rise in inflammation, suggesting that there may be an association between exacerbation frequency and the risk of stroke. This study examined that association. METHODS: Using the UK Clinical Practice Research Datalink, COPD patients with a first stroke between January 2004 and December 2013 were identified as cases and matched on age, sex, and general practice to controls with COPD but without a stroke (6,441 cases and 19,323 controls). Frequent exacerbators (FEs) were defined as COPD patients with ≥2 exacerbations, and infrequent exacerbators (IEs) have ≤1 exacerbation in the year prior to their stroke. Conditional logistic regression was used to estimate the association between exacerbation frequency and stroke overall, and by stroke subtype (hemorrhagic, ischemic, or transient ischemic attack). Exacerbations were also categorized into 0, 1, 2, or ≥3 exacerbations in the year prior to stroke. RESULTS: There was no evidence that FE had an increased odds of stroke compared to IE (OR [odds ratio] =0.95, 95% CI [confidence interval] =0.89-1.01). There was strong evidence that the risk of stroke decreased with each exacerbation of COPD experienced per year (P trend =0.003). In the subgroup analysis investigating stroke subtype, FE had 33% lower odds of hemorrhagic stroke than IE (OR =0.67, 95% CI =0.51-0.88, P=0.003). No association was found within other stroke types. CONCLUSION: This study found no evidence of a difference in the odds of stroke between IE and FE, suggesting that exacerbation frequency is unlikely to be the reason for increased stroke risk among COPD patients. Further research is needed to explore the association through investigation of stroke risk and the severity, duration, treatment of exacerbations, and concurrent treatment of cardiovascular risk factors.

The neutrophil in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous collection of conditions characterized by irreversible expiratory airflow limitation. The disease involves a multifaceted progressive inflammatory process leading to the development of mucus hypersecretion, tissue destruction, and disruption to the normal repair and defense mechanisms. The result is increased resistance to airflow in small conducting airways, change in lung compliance, and the premature collapse of airways during expiration that leads to air trapping. Neutrophils are necessary in healthy lungs; they are an important component of innate immunity, protecting healthy individuals against infection. However, in COPD, they play a role in the destructive processes that characterize the disease. They can be responsible for significant damage when they accumulate at sites of inflammation and are harmful to healthy tissue. In recent years, increased understanding of the role of neutrophils has led to improved knowledge of the pathogenesis of COPD and allowed new avenues of treatment to be investigated.