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.

Diagnostic and prognostic value of the sex-specific 99th percentile of four high-sensitivity cardiac troponin assays in patients with suspected myocardial infarction.

AIMS: High-sensitivity cardiac troponin (hs-cTn) assays are used for detection of myocardial infarction (MI). Ninety-ninth percentiles show wide inter-assay variation. The use of sex-specific cut-offs is recommended as definitory cut-off for MI. We compared diagnostic performance and prognostic value of sex-specific 99th percentiles of four hs-cTn assays in patients with suspected MI. METHODS AND RESULTS: Concentrations of four hs-cTn assays were measured at presentation and after 3 h in patients with suspected MI. Final diagnoses were adjudicated according to the 4th Universal Definition of MI. Unisex and sex-specific 99th percentiles were evaluated as diagnostic cut-offs following the ESC 0/3 h algorithm. These cut-offs were used in Cox-regression analyses to investigate the association with a composite endpoint of MI, revascularization, cardiac rehospitalization, and death. Non-ST-elevation MI was diagnosed in 368 of 2718 patients. Applying the unisex 99th percentile, Elecsys hs-cTnT provided highest negative predictive value (NPV) of 99.7 and a positive predictive value (PPV) of 75.9. The analysed hs-cTnI assays showed slightly lower NPVs and comparable PPVs [Architect (NPV 98.0, PPV of 71.4); Atellica (NPV 97.7, PPV of 76.1); Pathfast (NPV 97.7, PPV of 66.6)]. Application of sex-specific 99th percentiles did not significantly affect diagnostic performance. Concentrations above 99th percentile were independent predictors for impaired long-term outcome (hazard ratios 1.2-1.5, P < 0.001). CONCLUSION: We describe a good diagnostic accuracy of four hs-cTn assays using the assay-specific 99th percentile for detection of MI. Application of sex-specific 99th percentiles did neither affect diagnostic performance nor prognostic value significantly. Finally, values above the 99th percentile were associated with poor long-term outcome.

GRACE scores or high-sensitivity troponin for timing of coronary angiography in non-ST-elevation acute coronary syndromes.

BACKGROUND: The GRACE risk score is generically recommended by guidelines for timing of invasive coronary angiography without stating which score should be used. The aim was to determine the diagnostic performance of different GRACE risk scores in comparison to the ESC 0/1 h-algorithm using high-sensitivity cardiac troponin (hs-cTn). METHODS: Prospectively enrolled patients presenting with symptoms suggestive of myocardial infarction (MI) in two large studies testing biomarker diagnostic strategies were included. Five GRACE risk scores were calculated. The amount of risk reclassification and the theoretical impact on guideline-recommended timing of invasive coronary angiography was studied. RESULTS: Overall, 8,618 patients were eligible for analyses. Comparing different GRACE risk scores, up to 63.8% of participants were reclassified into a different risk category. The proportion of MIs identified (i.e., sensitivity) dramatically differed between GRACE risk scores (range 23.8-66.5%) and was lower for any score than for the ESC 0/1 h-algorithm (78.1%). Supplementing the ESC 0/1 h-algorithm with a GRACE risk score slightly increased sensitivity (P < 0.001 for all scores). However, this increased the number of false positive results. CONCLUSION: The substantial amount of risk reclassification causes clinically meaningful differences in the proportion of patients meeting the recommended threshold for pursuing early invasive strategy according to the different GRACE scores. The single best test to detect MIs is the ESC 0/1 h-algorithm. Combining GRACE risk scoring with hs-cTn testing slightly increases the detection of MIs but also increases the number of patients with false positive results who would undergo potential unnecessarily early invasive coronary angiography.

Personalized diagnosis in suspected myocardial infarction.

BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).

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.

Prediction of disability-free survival in healthy older people.

Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6-77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registration Clinicaltrials.gov (NCT01038583).

Differences in measurement of high-sensitivity troponin in an on-demand and batch-wise setting.

BACKGROUND: Most studies assessing the diagnostic value of high-sensitivity troponin in the diagnosis of myocardial infarction used batch-wise analyses of frozen samples for high-sensitivity troponin measurements. Whether the accuracy of these batch-wise high-sensitivity troponin measurements described in diagnostic studies is comparable to clinical routine is unknown. METHODS: We enrolled 937 patients presenting with suspected myocardial infarction in this prospective cohort study. Measurements of high-sensitivity troponin I (Abbott Architect) and high-sensitivity troponin T (Roche) were performed in two settings: (a) on-demand in clinical routine using fresh blood samples; and (b) in batches using frozen blood samples from the same individuals at three timepoints (0 hours, 1 hour and 3 hours after presentation). RESULTS: Median troponin levels were not different between on-demand and batch-wise measurements. Troponin levels in the range of 0 to 40 ng/L showed a very high correlation between the on-demand and batch setting (Pearson correlation coefficient (r) was 0.92-0.95 for high-sensitivity troponin I and 0.96 for high-sensitivity troponin T). However, at very low troponin levels (0 to 10 ng/L) correlation between the two settings was moderate (r for high-sensitivity troponin I 0.59-0.66 and 0.65-0.69 for high-sensitivity troponin T). Application of guideline-recommended rapid diagnostic algorithms showed similar diagnostic performance with both methods. CONCLUSIONS: Overall on-demand and batch-wise measurements of high-sensitivity troponin provided similar results, but their correlation was moderate, when focusing on very low troponin levels. The application of rapid diagnostic algorithms was safe in both settings.Trial Registration: www.clinicaltrials.gov (NCT02355457).

Predictive Value of Serial ECGs in Patients with Suspected Myocardial Infarction.

The electrocardiogram (ECG) is an important diagnostic tool for patients with suspected acute myocardial infarction (AMI). Current guidelines recommend serial ECGs in case of persisting symptoms. We aimed to analyze the predictive value of ischemic ECG-signs in patients with suspected AMI. Patients presenting to the emergency department with suspected AMI were included. All patients with ST-elevation AMI were excluded from analyses. Patients received 12-lead-ECG and high-sensitive Troponin T (hs-TnT)-measurement at admission and after 3 h. Four groups were defined: no ischemic signs in either ECG; new ischemic signs in the second ECG; resolved ischemic signs in the second ECG; and persistent ischemic signs in both ECGs. Patients were followed for 2 years to assess the composite endpoint of all-cause-mortality, AMI, and coronary revascularization. Using a 30-day landmark analysis, a Cox regression with ischemic signs as the variable of interest, adjusted by cardiovascular risk factors, was calculated. Of 1675 patients, 1321 showed no ischemic signs, in 25 new-, in 92 resolved- and in 237 patients, persistent ischemic signs were documented. Patients with persistent ischemic signs had significantly worse outcomes, compared to those without. Compared to no ischemic signs, adjusted hazard ratios for the combined endpoint were 0.81 (95% CI 0.20, 3.31; p-value = 0.77) for new-, 0.59 (95% CI 0.26, 1.34; p-value = 0.21) for resolved-, and 1.47 (95% CI 1.102, 2.13; p-value = 0.041) for persistent ischemic signs. In patients with suspected AMI, persistent ischemic ECG-signs are predictive of a higher rate of all-cause-mortality, AMI, and revascularization.

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.

Differences in measurement of high-sensitivity troponin in an on-demand and batch-wise setting.

BACKGROUND: Most studies assessing the diagnostic value of high-sensitivity troponin in the diagnosis of myocardial infarction used batch-wise analyses of frozen samples for high-sensitivity troponin measurements. Whether the accuracy of these batch-wise high-sensitivity troponin measurements described in diagnostic studies is comparable to clinical routine is unknown. METHODS: We enrolled 937 patients presenting with suspected myocardial infarction in this prospective cohort study. Measurements of high-sensitivity troponin I (Abbott Architect) and high-sensitivity troponin T (Roche) were performed in two settings: (a) on-demand in clinical routine using fresh blood samples; and (b) in batches using frozen blood samples from the same individuals at three timepoints (0 hours, 1 hour and 3 hours after presentation). RESULTS: Median troponin levels were not different between on-demand and batch-wise measurements. Troponin levels in the range of 0 to 40 ng/L showed a very high correlation between the on-demand and batch setting (Pearson correlation coefficient (r) was 0.92-0.95 for high-sensitivity troponin I and 0.96 for high-sensitivity troponin T). However, at very low troponin levels (0 to 10 ng/L) correlation between the two settings was moderate (r for high-sensitivity troponin I 0.59-0.66 and 0.65-0.69 for high-sensitivity troponin T). Application of guideline-recommended rapid diagnostic algorithms showed similar diagnostic performance with both methods. CONCLUSIONS: Overall on-demand and batch-wise measurements of high-sensitivity troponin provided similar results, but their correlation was moderate, when focusing on very low troponin levels. The application of rapid diagnostic algorithms was safe in both settings. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02355457).

Evaluation of a new ultra-sensitivity troponin I assay in patients with suspected myocardial infarction.

AIMS: Troponin is the gold-standard for diagnostic evaluation of patients with suspected myocardial infarction (MI). We aimed to evaluate the diagnostic and prognostic performance of a new ultra-sensitivity troponin I (us-TnI) assay in patients with suspected MI. METHODS AND RESULTS: 1534 patients with suspected MI were included. Us-TnI measurements were performed directly on admission and after one hour. One-year rates of mortality and incident MI were assessed. For diagnostic evaluation the negative and positive predictive value (NPV/PPV) using admission us-TnI concentrations and 0/1h delta were calculated. For rule-out an NPV > 99.5% (100% for single-admission-value) and for rule-in a PPV > 80% was targeted. Internal derivation/validation was used. In the derivation dataset 155/767 (20.2%) patients were diagnosed with having non-ST-elevation MI (NSTEMI). For rule-out of NSTEMI an us-TnI < 1 ng/L directly on admission resulted in an NPV of 100.0% (CI 98.2-100.0). Using serial sampling an admission us-TnI < 2 ng/L and a 0/1h delta < 1 ng/L resulted in an NPV of 99.7% (CI 98.4-100.0) and ruled-out NSTEMI in 46.8% of all patients. The respective one-year rate of death or MI was 0.6%. For rule-in of NSTEMI an us-TnI ≥ 25 ng/L on admission or a 0/1h delta ≥ 6 ng/L resulted in a PPV of 81.3% (CI 73.7-87.5) and ruled-in NSTEMI in 18.5% of all patients. The one-year event rate was 12.7%. Results were similar in 767 patients from the validation cohort. CONCLUSION: Application of an us-TnI assay allows the accurate triage of a large proportion of patients with suspected MI using a 0/1h algorithm. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02355457).

Right bundle branch block in patients with suspected myocardial infarction.

AIMS:: The new European Society of Cardiology guideline for ST-segment elevation myocardial infarction recommends that left and right bundle branch block should be considered equal for recommending urgent angiography in patients with suspected myocardial infarction. We aimed to evaluate this novel recommendation in two prospective studies of patients with suspected myocardial infarction. METHODS AND RESULTS:: We included 4067 patients presenting to the emergency department with suspected myocardial infarction. All patients had an ECG recorded immediately upon admission. Patients were classified as having right bundle branch block (RBBB), left bundle branch block (LBBB), bifascicular block (BFB) or no bundle branch block. All patients were followed for up to two years to assess mortality. In the overall population 125 (3.1%) patients had RBBB, 281 (6.9%) LBBB and 60 (1.5%) BFB. The final diagnosis of myocardial infarction was adjudicated in 20.8% (RBBB), 28.5% (LBBB), 23.3% (BFB) and 21.6% (no complete block) of patients. The mortality rate after one year was 10.7% (RBBB), 7% (LBBB), 17.5% (BFB) and 3.2% (no complete block). The adjusted hazard ratios were 1.29 (95% confidence interval (CI) 0.71-2.34; P=0.40) for RBBB, 1.71 (95% CI 1.17-2.50; P=0.006) for LBBB and 2.27 (95% CI 1.28-4.05; P=0.005) for BFB. CONCLUSION:: Our results support the new European Society of Cardiology ST-segment elevation myocardial infarction guideline describing RBBB as a high risk for mortality in patients with suspected myocardial infarction. However, the data challenge the concept of RBBB as a trigger of acute angiography because the likelihood of myocardial infarction in a chest pain unit setting is equally frequent in patients without bundle branch block.

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.

Impact of age on the performance of the ESC 0/1h-algorithms for early diagnosis of myocardial infarction.

Aims: We aimed to evaluate the impact of age on the performance of the European Society of Cardiology (ESC) 0/1h-algorithms and to derive and externally validate alternative cut-offs specific to older patients. Methods and results: We prospectively enrolled patients presenting to the emergency department (ED) with symptoms suggestive of acute myocardial infarction in three large diagnostic studies. Final diagnoses were adjudicated by two independent cardiologists. High-sensitivity cardiac troponin (hs-cTn) T and I concentrations were measured at presentation and after 1 h. Patients were stratified according to age [<55 years (young), ≥55 to <70 years (middle-age), ≥70 years (old)]. Rule-out safety of the ESC hs-cTnT 0/1h-algorithm was very high in all age-strata: sensitivity 100% [95% confidence interval (95% CI) 94.9-100] in young, 99.3% (95% CI 96.0-99.9) in middle-age, and 99.3% (95% CI 97.5-99.8) in old patients. Accuracy of rule-in decreased with age: specificity 97.0% (95% CI 95.8-97.9) in young, 96.1% (95% CI 94.5-97.2) in middle-age, and 92.7% (95% CI 90.7-94.3) in older patients. Triage efficacy decreased with increasing age (young 93%, middle-age 80%, old 55%, P < 0.001). Similar results were found for the ESC hs-cTnT 0/1h-algorithm. Alternative, slightly higher cut-off concentrations optimized for older patients maintained very high safety of rule-out, increased specificity of rule-in (P < 0.01), reduced overall efficacy for hs-cTnT (P < 0.01), while maintaining efficacy for hs-cTnI. Findings were confirmed in two validation cohorts (n = 2767). Conclusion: While safety of the ESC 0/1h-algorithms remained very high, increasing age significantly reduced overall efficacy and the accuracy of rule-in. Alternative slightly higher cut-off concentrations may be considered for older patients, particularly if using hs-cTnI. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT00470587, number NCT00470587 and NCT02355457 (BACC).