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Acute ischemic cardiac injury predisposes one to cognitive impairment, dementia, and depression. Pathophysiologically, recent positron emission tomography data suggest astroglial activation after experimental myocardial infarction (MI). We analyzed peripheral surrogate markers of glial (and neuronal) damage serially within 12 months after the first ST-elevation MI (STEMI). Serum levels of glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) were quantified using ultra-sensitive molecular immunoassays. Sufficient biomaterial was available from 45 STEMI patients (aged 28 to 78 years, median 56 years, 11% female). The median (quartiles) of GFAP was 63.8 (47.0, 89.9) pg/mL and of NfL 10.6 (7.2, 14.8) pg/mL at study entry 0–4 days after STEMI. GFAP after STEMI increased in the first 3 months, with a median change of +7.8 (0.4, 19.4) pg/mL (p = 0.007). It remained elevated without further relevant increases after 6 months (+11.7 (0.6, 23.5) pg/mL; p = 0.015), and 12 months (+10.3 (1.5, 22.7) pg/mL; p = 0.010) compared to the baseline. Larger relative infarction size was associated with a higher increase in GFAP (ρ = 0.41; p = 0.009). In contrast, NfL remained unaltered in the course of one year. Our findings support the idea of central nervous system involvement after MI, with GFAP as a potential peripheral biomarker of chronic glial damage as one pathophysiologic pathway.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.
Aims
There is an ongoing discussion whether the categorization of patients with heart failure according to left ventricular ejection fraction (LVEF) is scientifically justified and clinically relevant. Major efforts are directed towards the identification of appropriate cut-off values to correctly allocate heart failure-specific pharmacotherapy. Alternatively, an LVEF continuum without definite subgroups is discussed. This study aimed to evaluate the natural distribution of LVEF in patients presenting with acutely decompensated heart failure and to identify potential subgroups of LVEF in male and female patients.
Methods and results
We identified 470 patients (mean age 75 ± 11 years, n = 137 female) hospitalized for acute heart failure in whom LVEF could be quantified by Simpson's method in an in-hospital echocardiogram. Non-parametric modelling revealed a bimodal shape of the LVEF distribution. Parametric modelling identified two clusters suggesting two LVEF peaks with mean (variance) of 61% (9%) and 31% (10%), respectively. Sub-differentiation by sex revealed a sex-specific bimodal clustering of LVEF. The respective threshold differentiating between ‘high’ and ‘low’ LVEF was 45% in men and 52% in women.
Conclusions
In patients presenting with acute heart failure, LVEF clustered in two subgroups and exhibited profound sex-specific distributional differences. These findings might enrich the scientific process to identify distinct subgroups of heart failure patients, which might each benefit from respectively tailored (pharmaco)therapies.
Background
Chronic heart failure (HF) is known to increase the risk of developing Alzheimer’s dementia significantly. Thus, detecting and preventing mild cognitive impairment, which is common in patients with HF, is of great importance. Serum biomarkers are increasingly used in neurological disorders for diagnostics, monitoring, and prognostication of disease course. It remains unclear if neuronal biomarkers may help detect cognitive impairment in this high-risk population. Also, the influence of chronic HF and concomitant renal dysfunction on these biomarkers is not well understood.
Methods
Within the monocentric Cognition.Matters-HF study, we quantified the serum levels of phosphorylated tau protein 181 (pTau) and neurofilament light chain (NfL) of 146 extensively phenotyped chronic heart failure patients (aged 32 to 85 years; 15.1% women) using ultrasensitive bead-based single-molecule immunoassays. The clinical work-up included advanced cognitive testing and cerebral magnetic resonance imaging (MRI).
Results
Serum concentrations of NfL ranged from 5.4 to 215.0 pg/ml (median 26.4 pg/ml) and of pTau from 0.51 to 9.22 pg/ml (median 1.57 pg/ml). We detected mild cognitive impairment (i.e., T-score < 40 in at least one cognitive domain) in 60% of heart failure patients. pTau (p = 0.014), but not NfL, was elevated in this group. Both NfL (ρ = − 0.21; p = 0.013) and pTau (ρ = − 0.25; p = 0.002) related to the cognitive domain visual/verbal memory, as well as white matter hyperintensity volume and cerebral and hippocampal atrophy. In multivariable analysis, both biomarkers were independently influenced by age (T = 4.6 for pTau; T = 5.9 for NfL) and glomerular filtration rate (T = − 2.4 for pTau; T = − 3.4 for NfL). Markers of chronic heart failure, left atrial volume index (T = 4.6) and NT-proBNP (T = 2.8), were further cardiological determinants of pTau and NfL, respectively. In addition, pTau was also strongly affected by serum creatine kinase levels (T = 6.5) and ferritin (T = − 3.1).
Conclusions
pTau and NfL serum levels are strongly influenced by age-dependent renal and cardiac dysfunction. These findings point towards the need for longitudinal examinations and consideration of frequent comorbidities when using neuronal serum biomarkers.
Aims
Cognitive dysfunction occurs frequently in patients with heart failure (HF), but early detection remains challenging. Serum glial fibrillary acidic protein (GFAP) is an emerging biomarker of cognitive decline in disorders of primary neurodegeneration such as Alzheimer's disease. We evaluated the utility of serum GFAP as a biomarker for cognitive dysfunction and structural brain damage in patients with stable chronic HF.
Methods and results
Using bead-based single molecule immunoassays, we quantified serum levels of GFAP in patients with HF participating in the prospective Cognition.Matters-HF study. Participants were extensively phenotyped, including cognitive testing of five separate domains and magnetic resonance imaging (MRI) of the brain. Univariable and multivariable models, also accounting for multiple testing, were run. One hundred and forty-six chronic HF patients with a mean age of 63.8 ± 10.8 years were included (15.1% women). Serum GFAP levels (median 246 pg/mL, quartiles 165, 384 pg/mL; range 66 to 1512 pg/mL) did not differ between sexes. In the multivariable adjusted model, independent predictors of GFAP levels were age (T = 5.5; P < 0.001), smoking (T = 3.2; P = 0.002), estimated glomerular filtration rate (T = −4.7; P < 0.001), alanine aminotransferase (T = −2.1; P = 0.036), and the left atrial end-systolic volume index (T = 3.4; P = 0.004). NT-proBNP but not serum GFAP explained global cerebral atrophy beyond ageing. However, serum GFAP levels were associated with the cognitive domain visual/verbal memory (T = −3.0; P = 0.003) along with focal hippocampal atrophy (T = 2.3; P = 0.025).
Conclusions
Serum GFAP levels are affected by age, smoking, and surrogates of the severity of HF. The association of GFAP with memory dysfunction suggests that astroglial pathologies, which evade detection by conventional MRI, may contribute to memory loss beyond ageing in patients with chronic HF.
Background
The guideline recommendation to not measure carotid intima-media thickness (CIMT) for cardiovascular risk prediction is based on the assessment of just one single carotid segment. We evaluated whether there is a segment-specific association between different measurement locations of CIMT and cardiovascular risk factors.
Methods
Subjects from the population-based STAAB cohort study comprising subjects aged 30 to 79 years of the general population from Würzburg, Germany, were investigated. CIMT was measured on the far wall of both sides in three different predefined locations: common carotid artery (CCA), bulb, and internal carotid artery (ICA). Diabetes, dyslipidemia, hypertension, smoking, and obesity were considered as risk factors. In multivariable logistic regression analysis, odds ratios of risk factors per location were estimated for the endpoint of individual age- and sex-adjusted 75th percentile of CIMT.
Results
2492 subjects were included in the analysis. Segment-specific CIMT was highest in the bulb, followed by CCA, and lowest in the ICA. Dyslipidemia, hypertension, and smoking were associated with CIMT, but not diabetes and obesity. We observed no relevant segment-specific association between the three different locations and risk factors, except for a possible interaction between smoking and ICA.
Conclusions
As no segment-specific association between cardiovascular risk factors and CIMT became evident, one simple measurement of one location may suffice to assess the cardiovascular risk of an individual.
Background
Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW.
Methods
We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW.
Results
We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93% of the main findings could be replicated, 68% of sub-findings, and 75% of all findings. One study could be completely replicated with all main and sub-findings.
Conclusion
A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality.
Background: International disease management guidelines recommend the regular assessment of depression and anxiety in heart failure patients. Currently there is little data on the effect of screening for depression and anxiety on the quality of life and the prognosis of heart failure (HF). We will investigate the association between the recognition of current depression/anxiety by the general practitioner (GP) and the quality of life and the patients' prognosis.
Methods/Design: In this multicenter, prospective, observational study 3,950 patients with HF are recruited by general practices in Germany. The patients fill out questionnaires at baseline and 12-month follow-up. At baseline the GPs are interviewed regarding the somatic and psychological comorbidities of their patients. During the follow-up assessment, data on hospitalization and mortality are provided by the general practice. Based on baseline data, the patients are allocated into three observation groups: HF patients with depression and/or anxiety recognized by their GP (P+/+), those with depression and/or anxiety not recognized (P+/-) and patients without depression and/or anxiety (P-/-). We will perform multivariate regression models to investigate the influence of the recognition of depression and/or anxiety on quality of life at 12 month follow-up, as well as its influences on the prognosis (hospital admission, mortality).
Discussion: We will display the frequency of GP-acknowledged depression and anxiety and the frequency of installed therapeutic strategies. We will also describe the frequency of depression and anxiety missed by the GP and the resulting treatment gap. Effects of correctly acknowledged and missed depression/anxiety on outcome, also in comparison to the outcome of subjects without depression/anxiety will be addressed. In case results suggest a treatment gap of depression/anxiety in patients with HF, the results of this study will provide methodological advice for the efficient planning of further interventional research.
Background
Remote monitoring of patients with New York Heart Association (NYHA) functional class III heart failure (HF) using daily transmission of pulmonary artery (PA) pressure values has shown a reduction in HF-related hospitalizations and improved quality of life in patients.
Objectives
PASSPORT-HF is a prospective, randomized, open, multicenter trial evaluating the effects of a hemodynamic-guided, HF nurse-led care approach using the CardioMEMS™ HF-System on clinical end points.
Methods and results
The PASSPORT-HF trial has been commissioned by the German Federal Joint Committee (G-BA) to ascertain the efficacy of PA pressure-guided remote care in the German health-care system. PASSPORT-HF includes adult HF patients in NYHA functional class III, who experienced an HF-related hospitalization within the last 12 months. Patients with reduced ejection fraction must be on stable guideline-directed pharmacotherapy. Patients will be randomized centrally 1:1 to implantation of a CardioMEMS™ sensor or control. All patients will receive post-discharge support facilitated by trained HF nurses providing structured telephone-based care. The trial will enroll 554 patients at about 50 study sites. The primary end point is a composite of the number of unplanned HF-related rehospitalizations or all-cause death after 12 months of follow-up, and all events will be adjudicated centrally. Secondary end points include device/system-related complications, components of the primary end point, days alive and out of hospital, disease-specific and generic health-related quality of life including their sub-scales, and laboratory parameters of organ damage and disease progression.
Conclusions
PASSPORT-HF will define the efficacy of implementing hemodynamic monitoring as a novel disease management tool in routine outpatient care.
Trial registration
ClinicalTrials.gov; NCT04398654, 13-MAY-2020.
Proposals for enhanced health risk assessment and stratification in an integrated care scenario
(2016)
Objectives
Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario.
Settings
The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL).
Participants
Responsible teams for regional data management in the five ACT regions.
Primary and secondary outcome measures
We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction.
Results
There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment.
Conclusions
The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.