Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds.

BACKGROUND: Clinical decision-making for risk stratification for possible myocardial infarction (MI) uses high-sensitivity cardiac troponin (hs-cTn) thresholds that range from the limit of detection to several-fold higher than the upper reference limit (URL). To establish a minimum analytical variation standard, we can quantify the effect of variation on the population clinical measures of safety (sensitivity) and effectiveness [proportion below threshold, or positive predictive value (PPV)]. METHODS: From large datasets of patients investigated for possible MI with the Abbott hs-cTnI and Roche hs-cTnT assays, we synthesized datasets of 1 000 000 simulated patients. Troponin concentrations were randomly varied several times based on absolute deviations of 0.5 to 3 ng/L and relative changes of 2% to 20% around the low-risk threshold (5 ng/L) and URLs, respectively. RESULTS: For both assays at the low-risk thresholds, there were negligible differences in sensitivity (<0.3%) with increasing analytical variation. The proportion of patients characterized as low risk reduced by 30% to 29% (Roche) and 53% to 44% (Abbott). At the URL, increasing analytical variation also did not change sensitivity; the PPV fell by less than 3%. For risk stratification, increased delta thresholds (change between serial troponin concentrations) increased sensitivity at the cost of a decreased percentage of patients below the delta threshold, with the largest changes at the greatest analytical variation. CONCLUSIONS: At the low-risk threshold, analytical variation up to 3 ng/L minimally impacted the safety metric (sensitivity) but marginally reduced effectiveness. Similarly, at the URL even relative variation up to 25% minimally impacted safety metrics and effectiveness. Analytical variation for delta thresholds did not negatively impact sensitivity but decreased effectiveness.

Derivation and Validation of Thresholds Using Synthetic Data Methods for Single-Test Screening of Emergency Department Patients with Possible Acute Myocardial Infarction Using a Point-of-Care Troponin Assay.

BACKGROUND: Single-sample (screening) rule-out of acute myocardial infarction (AMI) with troponin requires derivation of a single-test screening threshold. In data sets with small event numbers, the lowest one or two concentrations of myocardial infarction (MI) patients dictate the threshold. This is not optimal. We aimed to demonstrate a process incorporating both real and synthetic data for deriving such thresholds using a novel pre-production high-precision point-of-care assay. METHODS: cTnI concentrations were measured from thawed plasma using the Troponin I Next (TnI-Nx) assay (i-STAT; Abbott) in adults on arrival to the emergency department with symptoms suggestive of AMI. The primary outcome was an AMI or cardiac death within 30 days. We used internal-external validation with synthetic data production based on clinical and demographic data, plus the measured TnI-Nx concentration, to derive and validate decision thresholds for TnI-Nx. The target low-risk threshold was a sensitivity of 99% and a high-risk threshold specificity of >95%. RESULTS: In total, 1356 patients were included, of whom 191 (14.1%) had the primary outcome. A total of 500 synthetic data sets were constructed. The mean low-risk threshold was determined to be 5 ng/L. This categorized 38% (95% CI, 6%-68%) to low-risk with a sensitivity of 99.0% (95% CI, 98.6%-99.5%) and a negative predictive value of 99.4% (95% CI, 97.6%-99.8%). A similarly derived high-risk threshold of 25 ng/L had a specificity of 95.0% (95% CI, 94.8%-95.1%) and a positive predictive value of 74.8% (95% CI, 71.5%-78.0%). CONCLUSIONS: With the TnI-Nx assay, we successfully demonstrated an approach using synthetic data generation to derive low-risk thresholds for safe and effective screening.

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).

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.

Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds to an individual's probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947-0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.

Diagnostic and prognostic performance of the ratio between high-sensitivity cardiac troponin I and troponin T in patients with chest pain.

BACKGROUND: Elevations of high-sensitivity cardiac troponin (hs-cTn) concentrations not related to type 1 myocardial infarction are common in chest pain patients presenting to emergency departments. The discrimination of these patients from those with type 1 myocardial infarction (MI) is challenging and resource-consuming. We aimed to investigate whether the hs-cTn I/T ratio might provide diagnostic and prognostic increment in this context. METHODS: We calculated the hs-cTn I/T ratio in 888 chest pain patients having hs-cTnI (Abbott Laboratories) or hs-cTnT (Roche Diagnostics) concentrations above the respective 99th percentile at 2 hours from presentation. All patients were followed for one year regarding mortality. RESULTS: The median hs-cTn I/T ratio was 3.45 (25th, 75th percentiles 1.80-6.59) in type 1 MI patients (n = 408 ☯46.0%]), 1.18 (0.81-1.90) in type 2 MI patients (n = 56 ☯6.3%]) and 0.67 (0.39-1.12) in patients without MI. The hs-cTn I/T ratio provided good discrimination of type 1 MI from no type 1 MI (area under the receiver-operator characteristic curve 0.89 ☯95% confidence interval 0.86-0.91]), of type 1 MI from type 2 MI (area under the curve 0.81 ☯95% confidence interval 0.74-0.87]), and was associated with type 1 MI in adjusted analyses. The hs-cTn I/T ratio provided no consistent prognostic value. CONCLUSIONS: The hs-cTn I/T ratio appears to be useful for early diagnosis of type 1 MI and its discrimination from type 2 MI in chest pain patients presenting with elevated hs-cTn. Differences in hs-cTn I/T ratio values may reflect variations in hs-cTn release mechanisms in response to different types of myocardial injury.

Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis.

BACKGROUND: Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. METHODS: The myocardial-ischaemic-injury-index (MI3) algorithm was validated in a prespecified exploratory analysis using data from a multi-centre randomised trial done in Scotland, UK that included consecutive patients with suspected acute coronary syndrome undergoing serial high-sensitivity cardiac troponin I measurement. Patients with ST-segment elevation myocardial infarction were excluded. MI3 incorporates age, sex, and two troponin measurements to compute a value (0-100) reflecting an individual's likelihood of myocardial infarction during the index visit and estimates diagnostic performance metrics (including area under the receiver-operating-characteristic curve, and the sensitivity, specificity, negative predictive value, and positive predictive value) at the computed score. Model performance for an index diagnosis of myocardial infarction (type 1 or type 4b), and for subsequent myocardial infarction or cardiovascular death at 1 year was determined using the previously defined low-probability threshold (1·6) and high-probability MI3 threshold (49·7). The trial is registered with ClinicalTrials.gov, NCT01852123. FINDINGS: In total, 20 761 patients (64 years [SD 16], 9597 [46%] women) enrolled between June 10, 2013, and March 3, 2016, were included from the High-STEACS trial cohort, of whom 3272 (15·8%) had myocardial infarction. MI3 had an area under the receiver-operating-characteristic curve of 0·949 (95% CI 0·946-0·952) identifying 12 983 (62·5%) patients as low-probability for myocardial infarction at the pre-specified threshold (MI3 score <1·6; sensitivity 99·3% [95% CI 99·0-99·6], negative predictive value 99·8% [99·8-99·9]), and 2961 (14·3%) as high-probability at the pre-specified threshold (MI3 score ≥49·7; specificity 95·0% [94·6-95·3], positive predictive value 70·4% [68·7-72·0]). At 1 year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability patients than low-probability patients (520 [17·6%] of 2961 vs 197 [1·5%] of 12 983], p<0·0001). INTERPRETATION: In consecutive patients undergoing serial cardiac troponin measurement for suspected acute coronary syndrome, the MI3 algorithm accurately estimated the likelihood of myocardial infarction and predicted subsequent adverse cardiovascular events. By providing individual probabilities the MI3 algorithm could improve the diagnosis and assessment of risk in patients with suspected acute coronary syndrome. FUNDING: Medical Research Council, British Heart Foundation, National Institute for Health Research, and NHSX.

Machine learning with D-dimer in the risk stratification for pulmonary embolism: a derivation and internal validation study.

AIM: To develop a machine learning model to predict the diagnosis of pulmonary embolism (PE). METHODS AND RESULTS: We undertook a derivation and internal validation study to develop a risk prediction model for use in patients being investigated for possible PE. The machine learning technique, generalized logistic regression using elastic net, was chosen following an assessment of seven machine learning techniques and on the basis that it optimized the area under the receiver operator characteristic curve (AUC) and Brier score. Models were developed both with and without the addition of D-dimer. A total of 3347 patients were included in the study of whom, 219 (6.5%) had PE. Four clinical variables (O2 saturation, previous deep venous thrombosis or PE, immobilization or surgery, and alternative diagnosis equal or more likely than PE) plus D-dimer contributed to the machine learning models. The addition of D-dimer improved the AUC by 0.16 (95% confidence interval 0.13-0.19), from 0.73 to 0.89 (0.87-0.91) and decreased the Brier score by 14% (10-18%). More could be ruled out with a higher positive likelihood ratio than by the Wells score combined with D-dimer, revised Geneva score combined with D-dimer, or the Pulmonary Embolism Rule-out Criteria score. Machine learning with D-dimer maintained a low-false-negative rate at a true-negative rate of nearly 53%, which was better performance than any of the other alternatives. CONCLUSION: A machine learning model outperformed traditional risk scores for the risk stratification of PE in the emergency department. However, external validation is needed.

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.

Reducing Patient Risk and Enhancing Care Through the Development and Implementation of a New Chest Pain Pathway, Expedited by and for the COVID-19 Era.

The COVID-19 pandemic raised major concerns relating to hospital capacity and cross-infection patients and staff in the Emergency Department (ED) of a metropolitan hospital servicing a population of ~500,000. We determined to reduce length of stay and admissions in patients presenting with symptoms of possible myocardial infarction; the most common presentation group. After establishing stakeholder consensus, the existing accelerated diagnostic pathway (ADP) based on the ED Assessment of Chest-pain Score (EDACS), electrocardiogram, and troponin measurements with a high-sensitivity assay (hs-cTn) on presentation and two hours later (EDACS-ADP) was modified to stream patients following an initial troponin measure as follows: (i) to a very-low risk group who could be discharged home without follow-up or further testing, and (ii) to a low-risk group who could be discharged with next-day follow-up community troponin testing. Simulations were run in an extensive research database to determine appropriate hs-cTnI and EDACS thresholds for risk classification. This COVID-ADP was developed in ~2-weeks and was implemented in the ED within a further 3-weeks. A comparison of all chest pain presentations for the 3 months prior to implementation of the COVID-ADP to 3 months following implementation showed that there was a 64.7% increase in patients having only one troponin test in the ED, a 30-minute reduction of mean length of stay of people discharged home from the ED, and a 24.3% reduction in hospital admissions of patients ultimately diagnosed with non-cardiac chest pain.

Early kinetic profiles of troponin I and T measured by high-sensitivity assays in patients with myocardial infarction.

The early concentration kinetic profiles of cardiac troponin in patients with non-ST-elevated myocardial infarction (NSTEMI) measured by high-sensitivity cardiac troponin I (hs-cTnI) and T (hs-cTnT) assays have not been described. In intermediate-to-high-risk of NSTEMI patients we measured serial cTn concentrations on ED arrival, at 1, 2, 3, 6-12, 24 and 48-hours with hs-cTnI and hs-cTnT assays. Log-normal curves were fitted to concentrations from time from symptom onset, and the time to rule-out decision thresholds estimated (hs-cTnI: 2 ng/L and 5 ng/L; hs-cTnT: 5 ng/L). Among 164 patients there were 58 NSTEMI. The hs-cTnI to hs-cTnT ratio increased linearly over the first 6-12 h following symptom onset. The estimated times from symptom onset to the 2 ng/L and 5 ng/L thresholds for hs-cTnI were 1.8 (0.1-3.1) and 1.9 (1.1-3.5) hours, and to the 5 ng/L threshold for hs-cTnT 1.9 (1.1-3.8) hours. The estimated time to exceed 5 ng/L was ≥3 hours in 32.6% (95%CI: 20.0% to 48.1%) cases for hs-cTnI and 33.3% (19.6% to 50.0%) for hs-cTnT. cTnI concentrations increased at a much more rapid rate than cTnT concentrations in patients with NSTEMI. Concentrations of a high proportion of patients took longer than 3 hours from symptom onset to exceed the 5 ng/L rule-out decision threshold.

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.

Validity of a Novel Point-of-Care Troponin Assay for Single-Test Rule-Out of Acute Myocardial Infarction.

Importance: Emergency department (ED) investigations of patients with suspected acute myocardial infarction (AMI) are time consuming, partly because of the turnaround time of laboratory tests. Current point-of-care troponin assays shorten test turnaround times but lack precision at lower concentrations. Development of point-of-care troponin assays with greater analytical precision could reduce the decision-making time in EDs for ruling out AMI. Objective: To determine the clinical accuracy for AMI of a single troponin concentration measured on arrival to ED with a new-generation, higher precision point-of-care assay with a 15-minute turnaround time. Design, Setting, and Participants: This observational study occurred at a single urban regional ED. Adults presenting acutely from the community to the ED with symptoms suggestive of AMI were included. Troponin concentrations were measured on ED arrival with both a novel point-of-care assay (i-STAT TnI-Nx; Abbott Point of Care) and a high-sensitivity troponin I assay (Architect hs-cTnI; Abbott Diagnostics). Main Outcomes and Measures: The primary outcome was type 1 AMI during index presentation. We compared the discrimination ability of the TnI-Nx assay with the hs-cTnI assay using the area under receiver operator characteristic curve (AUC) and sensitivity, negative predictive value, and the proportion of negative test results at thresholds with 100% sensitivity. Results: Of 354 patients (255 [72.0%] men; mean [SD] age, 62 [12] years), 57 (16.1%) experienced an AMI. Eighty-five patients (24.0%) presented to the ED less than 3 hours after symptom onset. No difference was found between the AUC of the TnI-Nx assay (0.975 [95% CI, 0.958-0.993]) and the hs-cTnI assay (0.970 [95% CI, 0.949 to 0.990]; P = .46). A TnI-Nx assay result of less than 11 ng/L identified 201 patients (56.7%) as low risk, with a sensitivity of 100% (95% CI, 93.7%-100%) and a negative predictive value of 100% (95% CI, 98.2%-100%). In comparison, an hs-cTnI assay result of less than 3 ng/L identified 154 patients (43.5%) as low risk, with a sensitivity of 100% (95% CI, 93.7%-100%) and a negative predictive value of 100% (95% CI, 97.6%-100%). Conclusions and Relevance: A novel point-of-care troponin assay that can produce a result 15 minutes after blood sampling had comparable discrimination ability to an hs-cTnI assay for ruling out AMI after a single blood test. Use in the ED may facilitate earlier decision making and could expedite the safe discharge of a large proportion of low-risk patients.

Detectable High-Sensitivity Cardiac Troponin within the Population Reference Interval Conveys High 5-Year Cardiovascular Risk: An Observational Study.

BACKGROUND: Increased cardiac troponin I or T detected by high-sensitivity assays (hs-cTnI or hs-cTnT) confers an increased risk of adverse prognosis. We determined whether patients presenting with putatively normal, detectable cTn concentrations [> limit of detection and < upper reference limit (URL)] have increased risk of major adverse cardiovascular events (MACE) or all-cause mortality. METHODS: A prospective 5-year follow-up of patients recruited in the emergency department with possible acute coronary syndrome (ACS) and cTn concentrations measured with hs-cTnI (Abbott) and hs-cTnT (Roche) assays. Cox regression models were generated with adjustment for covariates in those without MACE on presentation. Hazard ratios (HRs) for hs-cTn were calculated relative to the HRs at the median concentration. RESULTS: Of 1113 patients, 836 were without presentation MACE. Of these, 138 incurred a MACE and 169 died during a median 5.8-year follow-up. HRs for MACE at the URLs were 2.3 (95% CI, 1.7-3.2) for hs-cTnI and 1.8 (95% CI, 1.3-2.4) for hs-cTnT. Corresponding HRs for mortality were 1.7 (95% CI, 1.2-2.2) for hs-cTnI and 2.3 (95 % CI, 1.7-3.1) for hs-cTnT. The HR for MACE increased with increasing hs-cTn concentration similarly for both assays, but the HR for mortality increased at approximately twice the rate for hs-cTnT than hs-cTnI. Patients with hs-cTnI ≥10 ng/L or hs-cTnT ≥16 ng/L had the same percentage of MACE at 5-year follow-up (33%) as patients with presentation MACE. CONCLUSIONS: Many patients with ACS ruled out and putatively normal but detectable hs-cTnI concentrations are at similar long-term risk as those with MACE. hs-cTnT concentrations are more strongly associated with 5-year mortality than hs-cTnI.

Acute Kidney Injury and mortality prognosis in Acute Coronary Syndrome patients: A meta-analysis.

AIM: The aim of this study is to provide a robust estimate of mortality risk in acute coronary syndrome (ACS)-associated acute kidney injury (AKI) to inform clinical practice and policy. METHODS: A meta-analysis of cohort studies evaluating outcomes of ACS and which reported AKI and AKI-associated mortality. Studies were excluded if they incorporated patients not admitted through the emergency department (i.e. for elective procedures), were limited to cardiogenic shock or cardiac arrest, or relied on registry data for outcomes without further adjudication. The predictor was ACS-associated AKI and outcomes early (30 day or in-hospital) mortality and late-mortality (post-hospital discharge). RESULTS: Thirty-six studies with 37 unique cohorts comprising 100 476 patients were included. The pooled rate of ACS-associated AKI was 15.8%. In 32 cohorts reporting early mortality, the crude early mortality rate was 15.0% amongst those with AKI compared with 2.0% amongst those without AKI. The pooled estimate of the relative risk of AKI-associated early mortality was 4.1 (95% confidence interval: 3.3 to 5.0) with high heterogeneity between studies (I 2  = 84% (61% to 88%)). When heterogeneity was accounted for mathematically using credibility ceilings, the risk of mortality was lower, but still clinically significant (3.1 (2.6 to 3.6)). In 19 cohorts reporting late mortality (1 to 10 years), the relative risk of AKI-associated mortality was 2.6 (2.0 to 3.3) with moderate heterogeneity (I 2  = 65 % [35% to 88%]). Following application of credibility ceiling relative risk estimate dropped to 2.2 (1.9 to 2.6). CONCLUSIONS: Acute coronary syndrome-associated AKI is associated with more than a three-fold increase in early mortality and more than two-fold in long-term mortality.

A Risk Assessment Score and Initial High-sensitivity Troponin Combine to Identify Low Risk of Acute Myocardial Infarction in the Emergency Department.

OBJECTIVES: Early discharge of patients with presentations triggering assessment for possible acute coronary syndrome (ACS) is safe when clinical assessment indicates low risk, biomarkers are negative, and electrocardiograms (ECGs) are nonischemic. We hypothesized that the Emergency Department Assessment of Chest Pain Score (EDACS) combined with a single measurement of high-sensitivity cardiac troponin (hs-cTn) could allow early discharge of a clinically meaningful proportion of patients. METHODS: We pooled data from four patient cohorts from New Zealand and Australia presenting to an emergency department with symptoms suggestive of ACS. The primary outcome was major adverse cardiac events (MACE) within 30 days of presentation. In patients with a nonischemic ECG we evaluated the sensitivity for MACE and percentage low risk of every combination of high-sensitivity cardiac troponin T (hs-cTnT) concentration and high-sensitivity cardiac troponin I (hs-cTnI) concentration with EDACS. We used a standard smoothing technique on the probability density function for hs-cTn and EDACS and applied bootstrapping to determine the optimal threshold combinations, namely, the combination that maximized the percentage low risk with ≥98.5% sensitivity for MACE. RESULTS: From 2,536 patients, 2,258 presented without an ischemic ECG of whom 272 (12.1%) had a MACE within 30 days. The optimal threshold for hs-cTnI was 7 ng/L combined with an EDACS threshold of 16 (36.8% patients low risk). The optimal thresholds for hs-cTnT were 8 ng/L combined with an EDACS threshold of 15 (30.2% patients low risk). CONCLUSION: Single measurements of both hs-cTnI and hs-cTnT at presentation combined with EDACS to identify over 30% of patients as low risk and therefore eligible for safe early discharge after only one blood draw.

ICare-ACS (Improving Care Processes for Patients With Suspected Acute Coronary Syndrome): A Study of Cross-System Implementation of a National Clinical Pathway.

BACKGROUND: Efforts to safely reduce length of stay for emergency department patients with symptoms suggestive of acute coronary syndrome (ACS) have had mixed success. Few system-wide efforts affecting multiple hospital emergency departments have ever been evaluated. We evaluated the effectiveness of a nationwide implementation of clinical pathways for potential ACS in disparate hospitals. METHODS: This was a multicenter pragmatic stepped-wedge before-and-after trial in 7 New Zealand acute care hospitals with 31 332 patients investigated for suspected ACS with serial troponin measurements. The implementation was a clinical pathway for the assessment of patients with suspected ACS that included a clinical pathway document in paper or electronic format, structured risk stratification, specified time points for electrocardiographic and serial troponin testing within 3 hours of arrival, and directions for combining risk stratification and electrocardiographic and troponin testing in an accelerated diagnostic protocol. Implementation was monitored for >4 months and compared with usual care over the preceding 6 months. The main outcome measure was the odds of discharge within 6 hours of presentation RESULTS: There were 11 529 participants in the preimplementation phase (range, 284-3465) and 19 803 in the postimplementation phase (range, 395-5039). Overall, the mean 6-hour discharge rate increased from 8.3% (range, 2.7%-37.7%) to 18.4% (6.8%-43.8%). The odds of being discharged within 6 hours increased after clinical pathway implementation. The odds ratio was 2.4 (95% confidence interval, 2.3-2.6). In patients without ACS, the median length of hospital stays decreased by 2.9 hours (95% confidence interval, 2.4-3.4). For patients discharged within 6 hours, there was no change in 30-day major adverse cardiac event rates (0.52% versus 0.44%; P=0.96). In these patients, no adverse event occurred when clinical pathways were correctly followed. CONCLUSIONS: Implementation of clinical pathways for suspected ACS reduced the length of stay and increased the proportions of patients safely discharged within 6 hours. CLINICAL TRIAL REGISTRATION: URL: https://www.anzctr.org.au/ (Australian and New Zealand Clinical Trials Registry). Unique identifier: ACTRN12617000381381.

Association of High-Sensitivity Cardiac Troponin I Concentration With Cardiac Outcomes in Patients With Suspected Acute Coronary Syndrome.

Importance: High-sensitivity cardiac troponin I testing is widely used to evaluate patients with suspected acute coronary syndrome. A cardiac troponin concentration of less than 5 ng/L identifies patients at presentation as low risk, but the optimal threshold is uncertain. Objective: To evaluate the performance of a cardiac troponin I threshold of 5 ng/L at presentation as a risk stratification tool in patients with suspected acute coronary syndrome. Data Sources: Systematic search of MEDLINE, EMBASE, Cochrane, and Web of Science databases from January 1, 2006, to March 18, 2017. Study Selection: Prospective studies measuring high-sensitivity cardiac troponin I concentrations in patients with suspected acute coronary syndrome in which the diagnosis was adjudicated according to the universal definition of myocardial infarction. Data Extraction and Synthesis: The systematic review identified 19 cohorts. Individual patient-level data were obtained from the corresponding authors of 17 cohorts, with aggregate data from 2 cohorts. Meta-estimates for primary and secondary outcomes were derived using a binomial-normal random-effects model. Main Outcomes and Measures: The primary outcome was myocardial infarction or cardiac death at 30 days. Performance was evaluated in subgroups and across a range of troponin concentrations (2-16 ng/L) using individual patient data. Results: Of 11 845 articles identified, 104 underwent full-text review, and 19 cohorts from 9 countries were included. Among 22 457 patients included in the meta-analysis (mean age, 62 [SD, 15.5] years; n = 9329 women [41.5%]), the primary outcome occurred in 2786 (12.4%). Cardiac troponin I concentrations were less than 5 ng/L at presentation in 11 012 patients (49%), in whom there were 60 missed index or 30-day events (59 index myocardial infarctions, 1 myocardial infarction at 30 days, and no cardiac deaths at 30 days). This resulted in a negative predictive value of 99.5% (95% CI, 99.3%-99.6%) for the primary outcome. There were no cardiac deaths at 30 days and 7 (0.1%) at 1 year, with a negative predictive value of 99.9% (95% CI, 99.7%-99.9%) for cardiac death. Conclusions and Relevance: Among patients with suspected acute coronary syndrome, a high-sensitivity cardiac troponin I concentration of less than 5 ng/L identified those at low risk of myocardial infarction or cardiac death within 30 days. Further research is needed to understand the clinical utility and cost-effectiveness of this approach to risk stratification.