Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods.

BACKGROUND: In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis. METHODS: Three approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States' participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran's Q test was used to detect if ARR varied significantly by subgroup. RESULTS: The proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0-26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1-24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4-39.4%). Cochran's Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants. CONCLUSIONS: The choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

Prognostic Value of Cardiovascular Biomarkers in the Population.

Importance: Identification of individuals at high risk for atherosclerotic cardiovascular disease within the population is important to inform primary prevention strategies. Objective: To evaluate the prognostic value of routinely available cardiovascular biomarkers when added to established risk factors. Design, Setting, and Participants: Individual-level analysis including data on cardiovascular biomarkers from 28 general population-based cohorts from 12 countries and 4 continents with assessments by participant age. The median follow-up was 11.8 years. Exposure: Measurement of high-sensitivity cardiac troponin I, high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, B-type natriuretic peptide, or high-sensitivity C-reactive protein. Main Outcomes and Measures: The primary outcome was incident atherosclerotic cardiovascular disease, which included all fatal and nonfatal events. The secondary outcomes were all-cause mortality, heart failure, ischemic stroke, and myocardial infarction. Subdistribution hazard ratios (HRs) for the association of biomarkers and outcomes were calculated after adjustment for established risk factors. The additional predictive value of the biomarkers was assessed using the C statistic and reclassification analyses. Results: The analyses included 164 054 individuals (median age, 53.1 years [IQR, 42.7-62.9 years] and 52.4% were women). There were 17 211 incident atherosclerotic cardiovascular disease events. All biomarkers were significantly associated with incident atherosclerotic cardiovascular disease (subdistribution HR per 1-SD change, 1.13 [95% CI, 1.11-1.16] for high-sensitivity cardiac troponin I; 1.18 [95% CI, 1.12-1.23] for high-sensitivity cardiac troponin T; 1.21 [95% CI, 1.18-1.24] for N-terminal pro-B-type natriuretic peptide; 1.14 [95% CI, 1.08-1.22] for B-type natriuretic peptide; and 1.14 [95% CI, 1.12-1.16] for high-sensitivity C-reactive protein) and all secondary outcomes. The addition of each single biomarker to a model that included established risk factors improved the C statistic. For 10-year incident atherosclerotic cardiovascular disease in younger people (aged <65 years), the combination of high-sensitivity cardiac troponin I, N-terminal pro-B-type natriuretic peptide, and high-sensitivity C-reactive protein resulted in a C statistic improvement from 0.812 (95% CI, 0.8021-0.8208) to 0.8194 (95% CI, 0.8089-0.8277). The combination of these biomarkers also improved reclassification compared with the conventional model. Improvements in risk prediction were most pronounced for the secondary outcomes of heart failure and all-cause mortality. The incremental value of biomarkers was greater in people aged 65 years or older vs younger people. Conclusions and Relevance: Cardiovascular biomarkers were strongly associated with fatal and nonfatal cardiovascular events and mortality. The addition of biomarkers to established risk factors led to only a small improvement in risk prediction metrics for atherosclerotic cardiovascular disease, but was more favorable for heart failure and mortality.

Validation of a 0/1 h Algorithm for Rapid Diagnosis of Myocardial Infarction Using a High-Sensitivity Troponin I Assay.

BACKGROUND: Current guidelines recommend 0/1 h algorithms using high-sensitivity cardiac troponin (hs-cTn) for fast diagnosis of myocardial infarction (MI). Yet, for some assays, existing data is limited. We aimed to evaluate the diagnostic performance and the prognostic value of a rapid 0/1 h algorithm for the Access hs-cTnI assay. METHODS: In consecutive patients presenting with suspected MI, we measured concentrations of Access hs-cTnI at presentation and after 1 hour. Final diagnosis was adjudicated independently by 2 cardiologists. Parameters for diagnostic performance were calculated, applying the recently derived European Society of Cardiology (ESC) 0/1 h algorithm for Access hs-cTnI. Additionally, we assessed the prognostic utility of Access hs-cTnI for the composite end point of all-cause mortality and incident MI at 3 years. RESULTS: In 1879 patients, 257 non-ST-elevation MIs occurred. Application of the 0/1 h algorithm classified 44.5% as rule-out, 20.3% as rule-in, and triaged 35.1% to the observe group. High rule-out safety was confirmed with a sensitivity of 97.7% (95% CI, 95.0%-99.1%) and a negative predictive value of 99.3% (95% CI, 98.4%-99.7%). Rule-in capacity was moderate with a specificity of 88.0% (95% CI, 86.3%-89.6%) and a positive predictive value of 50.8% (95% CI, 45.7%-55.9%). After exclusion of patients with ST-elevation MI the results showed strong prognostic value, even after adjustment for cardiovascular risk factors and comorbidities, with adjusted hazard ratios of 2.51 (95% CI, 1.56-4.04) in the observe and 3.55 (95% CI, 2.18-5.79) in the rule-in group for the composite end point of all-cause mortality and incident MI at 3 years, compared to ruled-out patients. CONCLUSION: The ESC 0/1 h algorithm for Access hs-cTnI allows safe and efficient triage of patients with suspected MI and has strong prognostic utility up to 3 years after the initial evaluation.

Left ventricular myocardial strain responding to chronic pressure overload in patients with resistant hypertension evaluated by feature-tracking CMR.

OBJECTIVES: The study aimed to investigate the alterations of myocardial deformation responding to long-standing pressure overload and the effects of focal myocardial fibrosis using feature-tracking cardiac magnetic resonance (FT-CMR) in patients with resistant hypertension (RH). METHODS: Consecutive RH patients were prospectively recruited and underwent CMR at a single institution. FT-CMR analyses based on cine images were applied to measure left ventricular (LV) peak systolic global longitudinal (GLS), radial (GRS), and circumferential strain (GCS). Functional and morphological CMR variables, and late gadolinium enhancement (LGE) imaging were also obtained. RESULTS: A total of 50 RH patients (63 ± 12 years, 32 men) and 18 normotensive controls (57 ± 8 years, 12 men) were studied. RH patients had a higher average systolic blood pressure than controls (166 ± 21 mmHg vs. 116 ± 8 mmHg, p < 0.001) with the intake of 5 ± 1 antihypertensive drugs. RH patients showed increased LV mass index (78 ± 15 g/m2 vs. 61 ± 9 g/m2, p < 0.001), decreased GLS (- 16 ± 3% vs. - 19 ± 2%, p = 0.001) and GRS (41 ± 12% vs. 48 ± 8%, p = 0.037), and GCS was reduced by trend (- 17 ± 4% vs. - 19 ± 4%, p = 0.078). Twenty-one (42%) RH patients demonstrated a LV focal myocardial fibrosis (LGE +). LGE + RH patients had higher LV mass index (85 ± 14 g/m2 vs. 73 ± 15 g/m2, p = 0.007) and attenuated GRS (37 ± 12% vs. 44 ± 12%, p = 0.048) compared to LGE - RH patients, whereas GLS (p = 0.146) and GCS (p = 0.961) were similar. CONCLUSION: Attenuation of LV GLS and GRS, and GCS decline by tendency, might be adaptative changes responding to chronic pressure overload. There is a high incidence of focal myocardial fibrosis in RH patients, which is associated with reduced LV GRS. CLINICAL RELEVANCE STATEMENT: Feature-tracking CMR-derived myocardial strain offers insights into the influence of long-standing pressure overload and of a myocardial fibrotic process on cardiac deformation in patients with resistant hypertension. KEY POINTS: • Variations of left ventricular strain are attributable to the degree of myocardial impairment in resistant hypertensive patients. • Focal myocardial fibrosis of the left ventricle is associated with attenuated global radial strain. • Feature-tracking CMR provides additional information on the attenuation of myocardial deformation responding to long-standing high blood pressure.

Association of a healthy lifestyle with mortality in older people.

BACKGROUND: Unhealthy lifestyle behaviours such as smoking, high alcohol consumption, poor diet or low physical activity are associated with morbidity and mortality. Public health guidelines provide recommendations for adherence to these four factors, however, their relationship to the health of older people is less certain. METHODS: The study involved 11,340 Australian participants (median age 7.39 [Interquartile Range (IQR) 71.7, 77.3]) from the ASPirin in Reducing Events in the Elderly study, followed for a median of 6.8 years (IQR: 5.7, 7.9). We investigated whether a point-based lifestyle score based on adherence to guidelines for a healthy diet, physical activity, non-smoking and moderate alcohol consumption was associated with subsequent all-cause and cause-specific mortality. RESULTS: In multivariable adjusted models, compared to those in the unfavourable lifestyle group, individuals in the moderate lifestyle group (Hazard Ratio (HR) 0.73 [95% CI 0.61, 0.88]) and favourable lifestyle group (HR 0.68 [95% CI 0.56, 0.83]) had lower risk of all-cause mortality. A similar pattern was observed for cardiovascular related mortality and non-cancer/non-cardiovascular related mortality. There was no association of lifestyle with cancer-related mortality. CONCLUSIONS: In a large cohort of initially healthy older people, reported adherence to a healthy lifestyle is associated with reduced risk of all-cause and cause-specific mortality. Adherence to all four lifestyle factors resulted in the strongest protection.

Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis.

BACKGROUND: The 2020 European Society of Cardiology (ESC) guidelines recommend using the 0/1-hour and 0/2-hour algorithms over the 0/3-hour algorithm as the first and second choices of high-sensitivity cardiac troponin (hs-cTn)-based strategies for triage of patients with suspected acute myocardial infarction (AMI). PURPOSE: To evaluate the diagnostic accuracies of the ESC 0/1-hour, 0/2-hour, and 0/3-hour algorithms. DATA SOURCES: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from 1 January 2011 to 31 December 2020. (PROSPERO: CRD42020216479). STUDY SELECTION: Prospective studies that evaluated the ESC 0/1-hour, 0/2-hour, or 0/3-hour algorithms in adult patients presenting with suspected AMI. DATA EXTRACTION: The primary outcome was index AMI. Twenty unique cohorts were identified. Primary data were obtained from investigators of 16 cohorts and aggregate data were extracted from 4 cohorts. Two independent authors assessed each study for methodological quality. DATA SYNTHESIS: A total of 32 studies (20 cohorts) with 30 066 patients were analyzed. The 0/1-hour algorithm had a pooled sensitivity of 99.1% (95% CI, 98.5% to 99.5%) and negative predictive value (NPV) of 99.8% (CI, 99.6% to 99.9%) for ruling out AMI. The 0/2-hour algorithm had a pooled sensitivity of 98.6% (CI, 97.2% to 99.3%) and NPV of 99.6% (CI, 99.4% to 99.8%). The 0/3-hour algorithm had a pooled sensitivity of 93.7% (CI, 87.4% to 97.0%) and NPV of 98.7% (CI, 97.7% to 99.3%). Sensitivity of the 0/3-hour algorithm was attenuated in studies that did not use clinical criteria (GRACE score <140 and pain-free) compared with studies that used clinical criteria (90.2% [CI, 82.9 to 94.6] vs. 98.4% [CI, 88.6 to 99.8]). All 3 algorithms had similar specificities and positive predictive values for ruling in AMI, but heterogeneity across studies was substantial. Diagnostic performance was similar across the hs-cTnT (Elecsys; Roche), hs-cTnI (Architect; Abbott), and hs-cTnI (Centaur/Atellica; Siemens) assays. LIMITATION: Diagnostic accuracy, inclusion and exclusion criteria, and cardiac troponin sampling time varied among studies. CONCLUSION: The ESC 0/1-hour and 0/2-hour algorithms have higher sensitivities and NPVs than the 0/3-hour algorithm for index AMI. PRIMARY FUNDING SOURCE: National Taiwan University Hospital.

High-sensitivity cardiac troponin I after coronary artery bypass grafting for post-operative decision-making.

AIMS: Current troponin cut-offs suggested for the post-operative workup of patients following coronary artery bypass graft (CABG) surgery are based on studies using non-high-sensitive troponin assays or are arbitrarily chosen. We aimed to identify an optimal cut-off and timing for a proprietary high-sensitivity cardiac troponin I (hs-cTnI) assay to facilitate post-operative clinical decision-making. METHODS AND RESULTS: We performed a retrospective analysis of all patients undergoing elective isolated CABG at our centre between January 2013 and May 2019. Of 4684 consecutive patients, 161 patients (3.48%) underwent invasive coronary angiography after surgery, of whom 86 patients (53.4%) underwent repeat revascularization. We found an optimal cut-off value for peak hs-cTnI of >13 000 ng/L [>500× the upper reference limit (URL)] to be significantly associated with repeat revascularization within 48 h after surgery, which was internally validated through random repeated sampling with 1000 iterations. The same cut-off also predicted 30-day major adverse cardiovascular events and all-cause mortality after a median follow-up of 3.1 years, which was validated in an external cohort. A decision tree analysis of serial hs-cTnI measurements showed no added benefit of hs-cTnI measurements in patients with electrocardiographic or echocardiographic abnormalities or haemodynamic instability. Likewise, early post-operative hs-cTnI elevations had a low yield for clinical decision-making and only later elevations (at 12-16 h post-operatively) using a threshold of 8000 ng/L (307× URL) were significantly associated with repeat revascularization with an area under the curve of 0.92 (95% confidence interval 0.88-0.95). CONCLUSION: Our data suggest that for hs-cTnI, higher cut-offs than currently recommended should be used in the post-operative management of patients following CABG.

The Effects of Statins on Cardiovascular and Inflammatory Biomarkers in Primary Prevention: A Systematic Review and Meta-Analysis.

BACKGROUND: Statins are well-established for their treatment of cardiovascular disease (CVD) due to their cholesterol-lowering effects and potential anti-inflammatory properties. Although previous systematic reviews demonstrate that statins reduce inflammatory biomarkers in the secondary prevention of CVD, none examine their effects on cardiac and inflammatory biomarkers in a primary prevention setting. METHODS: We conducted a systematic review and meta-analysis to examine the effects of statins on cardiovascular and inflammatory biomarkers among individuals without established CVD. The biomarkers included are: cardiac troponin, N-terminal pro B-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), soluble vascular cell adhesion molecule (sVCAM), soluble intercellular adhesion molecule (sICAM), soluble E-selectin (sE-selectin) and endothelin-1 (ET-1). A literature search was performed through Ovid MEDLINE, Embase and CINAHL Plus for randomised controlled trials (RCTs) published up to June 2021. RESULTS: Overall, 35 RCTs with 26,521 participants were included in our meta-analysis. Data was pooled using random effects models presented as standardised mean differences (SMD) with 95% confidence intervals (CI). Combining 36 effect sizes from 29 RCTs, statin use resulted in a significant reduction in CRP levels (SMD -0.61; 95% CI -0.91, -0.32; P<0.001). This reduction was observed for both hydrophilic (SMD -0.39; 95% CI -0.62, -0.16; P<0.001) and lipophilic statins (SMD -0.65; 95% CI -1.01, -0.29; P<0.001). There were no significant changes in serum concentrations of cardiac troponin, NT-proBNP, TNF-α, IL-6, sVCAM, sICAM, sE-selectin and ET-1. CONCLUSION: This meta-analysis demonstrates that statin use reduces serum CRP levels in a primary prevention setting for CVD, with no clear effect on the other eight biomarkers studied.

Application of High-Sensitivity Troponin in Suspected Myocardial Infarction.

BACKGROUND: Data regarding high-sensitivity troponin concentrations in patients presenting to the emergency department with symptoms suggestive of myocardial infarction may be useful in determining the probability of myocardial infarction and subsequent 30-day outcomes. METHODS: In 15 international cohorts of patients presenting to the emergency department with symptoms suggestive of myocardial infarction, we determined the concentrations of high-sensitivity troponin I or high-sensitivity troponin T at presentation and after early or late serial sampling. The diagnostic and prognostic performance of multiple high-sensitivity troponin cutoff combinations was assessed with the use of a derivation-validation design. A risk-assessment tool that was based on these data was developed to estimate the risk of index myocardial infarction and of subsequent myocardial infarction or death at 30 days. RESULTS: Among 22,651 patients (9604 in the derivation data set and 13,047 in the validation data set), the prevalence of myocardial infarction was 15.3%. Lower high-sensitivity troponin concentrations at presentation and smaller absolute changes during serial sampling were associated with a lower likelihood of myocardial infarction and a lower short-term risk of cardiovascular events. For example, high-sensitivity troponin I concentrations of less than 6 ng per liter and an absolute change of less than 4 ng per liter after 45 to 120 minutes (early serial sampling) resulted in a negative predictive value of 99.5% for myocardial infarction, with an associated 30-day risk of subsequent myocardial infarction or death of 0.2%; a total of 56.5% of the patients would be classified as being at low risk. These findings were confirmed in an external validation data set. CONCLUSIONS: A risk-assessment tool, which we developed to integrate the high-sensitivity troponin I or troponin T concentration at emergency department presentation, its dynamic change during serial sampling, and the time between the obtaining of samples, was used to estimate the probability of myocardial infarction on emergency department presentation and 30-day outcomes. (Funded by the German Center for Cardiovascular Research [DZHK]; ClinicalTrials.gov numbers, NCT00470587, NCT02355457, NCT01852123, NCT01994577, and NCT03227159; and Australian New Zealand Clinical Trials Registry numbers, ACTRN12611001069943, ACTRN12610000766011, ACTRN12613000745741, and ACTRN12611000206921.).

Morphological Characters Can Strongly Influence Early Animal Relationships Inferred from Phylogenomic Data Sets.

There are considerable phylogenetic incongruencies between morphological and phylogenomic data for the deep evolution of animals. This has contributed to a heated debate over the earliest-branching lineage of the animal kingdom: the sister to all other Metazoa (SOM). Here, we use published phylogenomic data sets ($\sim $45,000-400,000 characters in size with $\sim $15-100 taxa) that focus on early metazoan phylogeny to evaluate the impact of incorporating morphological data sets ($\sim $15-275 characters). We additionally use small exemplar data sets to quantify how increased taxon sampling can help stabilize phylogenetic inferences. We apply a plethora of common methods, that is, likelihood models and their "equivalent" under parsimony: character weighting schemes. Our results are at odds with the typical view of phylogenomics, that is, that genomic-scale data sets will swamp out inferences from morphological data. Instead, weighting morphological data 2-10$\times $ in both likelihood and parsimony can in some cases "flip" which phylum is inferred to be the SOM. This typically results in the molecular hypothesis of Ctenophora as the SOM flipping to Porifera (or occasionally Placozoa). However, greater taxon sampling improves phylogenetic stability, with some of the larger molecular data sets ($>$200,000 characters and up to $\sim $100 taxa) showing node stability even with $\geqq100\times $ upweighting of morphological data. Accordingly, our analyses have three strong messages. 1) The assumption that genomic data will automatically "swamp out" morphological data is not always true for the SOM question. Morphological data have a strong influence in our analyses of combined data sets, even when outnumbered thousands of times by molecular data. Morphology therefore should not be counted out a priori. 2) We here quantify for the first time how the stability of the SOM node improves for several genomic data sets when the taxon sampling is increased. 3) The patterns of "flipping points" (i.e., the weighting of morphological data it takes to change the inferred SOM) carry information about the phylogenetic stability of matrices. The weighting space is an innovative way to assess comparability of data sets that could be developed into a new sensitivity analysis tool. [Metazoa; Morphology; Phylogenomics; Weighting.].

Predictive Performance of a Polygenic Risk Score for Incident Ischemic Stroke in a Healthy Older Population.

Background and Purpose: Polygenic risk scores (PRSs) can be used to predict ischemic stroke (IS). However, further validation of PRS performance is required in independent populations, particularly older adults in whom the majority of strokes occur. Methods: We predicted risk of incident IS events in a population of 12 792 healthy older individuals enrolled in the ASPREE trial (Aspirin in Reducing Events in the Elderly). The PRS was calculated using 3.6 million genetic variants. Participants had no previous history of cardiovascular events, dementia, or persistent physical disability at enrollment. The primary outcome was IS over 5 years, with stroke subtypes as secondary outcomes. A multivariable model including conventional risk factors was applied and reevaluated after adding PRS. Area under the curve and net reclassification were evaluated. Results: At baseline, mean population age was 75 years. In total, 173 incident IS events occurred over a median follow-up of 4.7 years. When PRS was added to the multivariable model as a continuous variable, it was independently associated with IS (hazard ratio, 1.41 [95% CI, 1.20­1.65] per SD of the PRS; P<0.001). The PRS alone was a better discriminator for IS events than most conventional risk factors. PRS as a categorical variable was a significant predictor in the highest tertile (hazard ratio, 1.74; P=0.004) compared with the lowest. The area under the curve of the conventional model was 66.6% (95% CI, 62.2­71.1) and after inclusion of the PRS, improved to 68.5 ([95% CI, 64.0­73.0] P=0.095). In subgroup analysis, the continuous PRS remained an independent predictor for large vessel and cardioembolic stroke subtypes but not for small vessel stroke. Reclassification was improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.17­0.43). Conclusions: PRS predicts incident IS in a healthy older population but only moderately improves prediction over conventional risk factors. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01038583.

Role of Cardiac Biomarkers in Epidemiology and Risk Outcomes.

BACKGROUND: The use of biomarkers associated with cardiovascular disease (CVD) is established for diagnostic purposes. Cardiac troponins, as specific markers of myocardial injury, and natriuretic peptides, reflecting myocardial dilation, are routinely used for diagnosis in clinical practice. In addition, a substantial body of research has shed light on the ability of biomarkers to reflect the risk of future major cardiovascular events. Among biomarkers, troponin and members of the natriuretic peptide family have been investigated extensively in the general population, in those at higher risk, and in patients with known CVD. Both biomarkers have been shown to contribute substantially to statistical models describing cardiovascular risk, in addition to and independently of important clinical characteristics. The more precise identification of individuals at risk by appropriate use of biomarkers might lead to an earlier initiation of preventive therapies and potentially avoid significant events. CONTENT: We summarize the current evidence concerning risk prediction using cardiac biomarkers at different stages in the development of CVD and provide examples of observational studies and large-scale clinical trials testing such application. Beyond the focus on troponin and natriuretic peptides, we also discuss other important and emerging biomarkers in the field with potential for such application, including growth differentiation factor-15, soluble ST2 (alias for IL1RL1 [interleukin 1 receptor like 1), and galectin-3. SUMMARY: Incorporating biomarkers in risk prediction models might allow more precise identification of individuals at risk. Among the various biomarkers, cardiac troponin appears to be the most promising for prediction of future cardiovascular events in a wide variety of patient populations.

Diagnostic Validation of a High-Sensitivity Cardiac Troponin I Assay.

BACKGROUND: Emergency departments worldwide are increasingly adopting rapid diagnosis of patients with suspected myocardial infarction (MI) based on high-sensitivity troponin. We set out to assess the diagnostic accuracy of a high-sensitivity cardiac troponin I (hs-cTnI) assay in a prospective study. METHODS: In a cohort study including 1800 patients presenting with suspected acute MI, we developed and temporally validated a 0/1 h diagnostic algorithm using the Siemens Atellica IM hs-cTnI assay. The algorithm was established in the first 928 patients and validated in the following 872 patients. RESULTS: The derived algorithm consisted of a baseline rule-out of non-ST-segment elevation MI using a cutoff <3 ng/L in patients with symptom onset ≥3 h or an admission troponin I level <6 ng/L with a Δ change of <3 ng/L from 0 h to 1 h. For rule-in, an admission troponin I level ≥120 ng/L or an increase within the first hour ≥12 ng/L was required. Application of the algorithm to the validation cohort showed a negative predictive value of 99.8% (95% CI, 98.7%-100.0%), sensitivity of 99.1% (95% CI, 95.1%-100.0%), and 48.3% of patients ruled out, whereas 15.1% were ruled in with a positive predictive value of 68.0% (95% CI, 59.1%-75.9%) and specificity of 94.4% (95% CI, 92.5%-96.0%). The diagnostic performance was comparable to guideline-recommended application of an established hs-cTnI assay in a rapid 0/1 h strategy. CONCLUSIONS: The Siemens hs-cTnI assay is well suited for application in rapid diagnostic stratification of patients with suspected MI. STUDY REGISTRATION: www.clinicaltrials.gov (NCT02355457).

Comparative Analysis of Circulating Noncoding RNAs Versus Protein Biomarkers in the Detection of Myocardial Injury.

RATIONALE: Noncoding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long noncoding RNAs (lncRNAs), are proposed novel biomarkers of myocardial injury. Their release kinetics have not been explored without confounding by heparin nor has their relationship to myocardial protein biomarkers. OBJECTIVE: To compare ncRNA types in heparinase-treated samples with established and emerging protein biomarkers for myocardial injury. METHODS AND RESULTS: Screening of 158 circRNAs and 21 lncRNAs in human cardiac tissue identified 12 circRNAs and 11 lncRNAs as potential biomarkers with cardiac origin. Eleven miRNAs were included. At low spike-in concentrations of myocardial tissue, significantly higher regression coefficients were observed across ncRNA types compared with cardiac troponins and cMyBP-C (cardiac myosin-binding protein C). Heparinase treatment of serial plasma and serum samples of patients undergoing transcoronary ablation of septal hypertrophy removed spurious correlations between miRNAs in non-heparinase-treated samples. After transcoronary ablation of septal hypertrophy, muscle-enriched miRNAs (miR-1 and miR-133a) showed a steeper and earlier increase than cardiac-enriched miRNAs (miR-499 and miR-208b). Putative cardiac lncRNAs, including LIPCAR (long intergenic noncoding RNA predicting cardiac remodeling and survival), did not rise, refuting a predominant cardiac origin. Cardiac circRNAs remained largely undetectable. In a validation cohort of acute myocardial infarction, receiver operating characteristic curve analysis revealed noninferiority of cardiac-enriched miRNAs, but miRNAs failed to identify cases presenting with low troponin values. cMyBP-C was validated as a biomarker with highly sensitive properties, and the combination of muscle-enriched miRNAs with high-sensitive cardiac troponin T and cMyBP-C returned the highest area under the curve values. CONCLUSIONS: In a comparative assessment of ncRNAs and protein biomarkers for myocardial injury, cMyBP-C showed properties as the most sensitive cardiac biomarker while miRNAs emerged as promising candidates to integrate ncRNAs with protein biomarkers. Sensitivity of current miRNA detection is inferior to cardiac proteins but a multibiomarker combination of muscle-enriched miRNAs with cMyBP-C and cardiac troponins could open a new path of integrating complementary characteristics of different biomarker types.

Social isolation, social support and loneliness as predictors of cardiovascular disease incidence and mortality.

BACKGROUND: Poor social health is associated with increased risk of cardiovascular disease (CVD). Recent research suggests that different social health domains should be considered separately as the implications for health and possible interventions may differ. AIM: To assess social isolation, low social support and loneliness as predictors of CVD. METHODS: Secondary analysis of 11,486 community-dwelling, Australians, aged 70 years and over, free of CVD, dementia, or significant physical disability, from the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Social isolation, social support (Revised Lubben Social Network Scale), and loneliness were assessed as predictors of CVD using Cox proportional-hazard regression. CVD events included fatal CVD, heart failure hospitalization, myocardial infarction and stroke. Analyses were adjusted for established CVD risk factors. RESULTS: Individuals with poor social health were 42 % more likely to develop CVD (p = 0.01) and twice as likely to die from CVD (p = 0.02) over a median 4.5 years follow-up. Interaction effects indicated that poorer social health more strongly predicted CVD in smokers (HR 4.83, p = 0.001, p-interaction = 0.01), major city dwellers (HR 1.94, p < 0.001, p-interaction=0.03), and younger older adults (70-75 years; HR 2.12, p < 0.001, p-interaction = 0.01). Social isolation (HR 1.66, p = 0.04) and low social support (HR 2.05, p = 0.002), but not loneliness (HR 1.4, p = 0.1), predicted incident CVD. All measures of poor social health predicted ischemic stroke (HR 1.73 to 3.16). CONCLUSIONS: Among healthy older adults, social isolation and low social support may be more important than loneliness as cardiovascular risk factors. Social health domains should be considered in future CVD risk prediction models.

Precursor proadrenomedullin influences cardiomyocyte survival and local inflammation related to myocardial infarction.

Increased adrenomedullin (ADM) levels are associated with various cardiac diseases such as myocardial infarction (MI). ADM is cleaved off from the full-length precursor protein proadrenomedullin (ProADM) during its posttranslational processing. To date, no biological effect of ProADM is reported, while ADM infusion leads to antiapoptotic effects and improved cardiac function. Using an MI mouse model, we found an induction of ProADM gene as well as protein expression during the early phase of MI. This was accompanied by apoptosis and increasing inflammation, which substantially influence the post-MI remodeling processes. Simulating ischemia in vitro, we demonstrate that ProADM expression was increased in cardiomyocytes and cardiac fibroblasts. Subsequently, we treated ischemic cardiomyocytes with either ProADM or ADM and found that both proteins increased survival. This effect was diminishable by blocking the ADM1 receptor. To investigate whether ProADM and ADM play a role in the regulation of cardiac inflammation, we analyzed chemokine expression after treatment of cells with both proteins. While ProADM induced an expression of proinflammatory cytokines, thus promoting inflammation, ADM reduced chemokine expression. On leukocytes, both proteins repressed chemokine expression, revealing antiinflammatory effects. However, ProADM but not ADM dampened concurrent activation of leukocytes. Our data show that the full-length precursor ProADM is biologically active by reducing apoptosis to a similar extent as ADM. We further assume that ProADM induces local inflammation in affected cardiac tissue but attenuates exaggerated inflammation, whereas ADM has low impact. Our data suggest that both proteins are beneficial during MI by influencing apoptosis and inflammation.

Clinical application of the 4th Universal Definition of Myocardial Infarction.

AIMS: The recently released 4th version of the Universal Definition of Myocardial Infarction (UDMI) introduces an increased emphasis on the entities of acute and chronic myocardial injury. We applied the 4th UDMI retrospectively in patients presenting to the emergency department with symptoms potentially indicating myocardial infarction (MI) to investigate its effect on diagnosis and prognosis. METHODS AND RESULTS: We included 2302 patients presenting to the emergency department with symptoms suggestive of MI. The final diagnosis was adjudicated sequentially according to the 3rd and 4th UDMI. Reclassification after readjudication was assessed. Established diagnostic algorithms for patients with suspected MI were applied to compare diagnostic accuracy. All patients were followed to assess mortality, recurrent MI, revascularization, and rehospitalization to investigate the effect of the 4th UDMI on prognosis. After readjudication, 697 patients were reclassified. Most of these patients were reclassified as having acute (n = 78) and chronic myocardial injury (n = 585). Four hundred and thirty-four (18.9%) patients were diagnosed with MI, compared with 501 (21.8%) MIs when adjudication was based on the 3rd UDMI. In the non-MI population, patients with myocardial injury (n = 663) were older, more often female and had worse renal function compared with patients without myocardial injury (n = 1205). Application of diagnostic algorithms for patients with suspected MI revealed a high accuracy after readjudication. Reclassified patients had a substantially higher rate of cardiovascular events compared with not-reclassified patients, particularly patients reclassified to the category of myocardial injury. CONCLUSION: By accentuating the categories of acute and chronic myocardial injury the 4th UDMI succeeds to identify patients with higher risk for cardiovascular events and poorer outcome and thus seems to improve risk assessment in patients with suspected MI. Application of established diagnostic algorithms remains safe when using the 4th UDMI.

Machine Learning to Predict the Likelihood of Acute Myocardial Infarction.

BACKGROUND: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. METHODS: A machine learning algorithm (myocardial-ischemic-injury-index [MI3]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI3 uses gradient boosting to compute a value (0-100) reflecting an individual's likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. RESULTS: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956-0.971] in the test set and similar performance in early and late presenters. Example MI3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5-99.8%) and sensitivity of 97.8% (96.7-98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9-75.0%) and specificity of 96.7% (96.3-97.1%). Using these thresholds, MI3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5-88.8%]; specificity, 92.2% [90.7-93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4-91.6%]); specificity, 89.3% [88.6-90.0%]). CONCLUSIONS: Using machine learning, MI3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. CLINICAL TRIAL REGISTRATION: URL: https://www.anzctr.org.au. Unique identifier: ACTRN12616001441404.

High-sensitivity troponin I and all-cause mortality in patients with stable COPD: an analysis of the COSYCONET study.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death with a considerable part of the population dying from cardiovascular diseases. High-sensitivity troponin I (hs-TnI) might help to better identify COPD patients at high risk of mortality. We aimed to study the predictive value of hs-TnI for all-cause mortality beyond established COPD assessments, and after consideration of relevant cardiovascular risk factors and prevalent cardiovascular diseases, in a broad population with stable COPD.Circulating hs-TnI concentrations together with a wide range of respiratory and cardiovascular markers were evaluated in 2085 patients with stable COPD across all severity stages enrolled in the multicentre COSYCONET cohort study. The primary outcome was all-cause mortality over 3 years of follow-up.Hs-TnI was detectable in 2020 (96.9%) patients. The median hs-TnI concentration was 3.8 ng·L-1 (interquartile range 2.5-6.6 ng·L-1), with levels above the 99th percentile reference limit of 27 ng·L-1 observed in 1.8% of patients. In Cox regression analyses including adjustments for airflow limitation, dyspnoea grade, exercise capacity and history of severe exacerbations, as well as traditional cardiovascular risk factors, estimated glomerular filtration rate, ankle-brachial index, N-terminal pro-brain natriuretic peptides and prevalent cardiovascular diseases, hs-TnI was a significant predictor for all-cause mortality, both as a continuous variable (hazard ratio (HR) for log hs-TnI 1.28, 95% CI 1.01-1.62) and categorised according to the cut-off of 6 ng·L-1 (HR 1.63, 95% CI 1.10-2.42).In patients with stable COPD, hs-TnI is a strong predictor of all-cause mortality beyond established COPD mortality predictors, and independent of a broad range of cardiovascular risk factors and prevalent cardiovascular diseases. Hs-TnI concentrations well below the upper reference limit provide further prognostic value for all patients with COPD when added to established risk assessments.

Application of the SCAI classification in a cohort of patients with cardiogenic shock.

BACKGROUND: The Society of Cardiovascular Angiography and Interventions (SCAI) have recently proposed a new classification of cardiogenic shock (CS) dividing patients into five subgroups. OBJECTIVE: Aim of this study was to apply the SCAI classification to a cohort of patients presenting with CS and to evaluate its ability to predict 30-day survival. METHODS: SCAI CS subgroups were interpreted based on the recent consensus statement and then applied to N = 1,007 consecutive patients presenting with CS or large myocardial infarction (MI) between October 2009 and October 2017. The association between SCAI classification and 30-day all-cause mortality was assessed by logistic regression analysis. RESULTS: Mean age in the study cohort was 67 (±15) years, 72% were male. Mean lactate at baseline was 6.05 (±5.13) mmol/l and 51% of the patients had prior cardiac arrest. Overall survival probability was 50.6% (95% confidence interval [CI] 47.5-54.0%). In view of the SCAI classification, the survival probability was 96.4% (95% CI 93.7-99.0%) in class A, 66.1% (95% CI 50.2-87.1%) in class B, 46.1% (95% CI 40.6-52.4%) in class C, 33.1% (95% CI 26.6-41.1%) in class D, and 22.6% (95% CI 17.1-30.0%) in class E. Higher SCAI classification was significantly associated with lower 30-day survival (p < .01). CONCLUSION: In this large clinical cohort, the SCAI classification was significantly associated with 30-day survival. This finding supports the rationale of the SCAI CS classification and calls for a validation in a prospective trial.