Sex-specific evaluation and redevelopment of the GRACE score in non-ST-segment elevation acute coronary syndromes in populations from the UK and Switzerland: a multinational analysis with external cohort validation.

BACKGROUND The Global Registry of Acute Coronary Events (GRACE) 2.0 score was developed and validated in predominantly male patient populations. We aimed to assess its sex-specific performance in non-ST-segment elevation acute coronary syndromes (NSTE-ACS) and to develop an improved score (GRACE 3.0) that accounts for sex differences in disease characteristics. METHODS We evaluated the GRACE 2.0 score in 420 781 consecutive patients with NSTE-ACS in contemporary nationwide cohorts from the UK and Switzerland. Machine learning models to predict in-hospital mortality were informed by the GRACE variables and developed in sex-disaggregated data from 386 591 patients from England, Wales, and Northern Ireland (split into a training cohort of 309 083 [80·0%] patients and a validation cohort of 77 508 [20·0%] patients). External validation of the GRACE 3.0 score was done in 20 727 patients from Switzerland. FINDINGS Between Jan 1, 2005, and Aug 27, 2020, 400 054 patients with NSTE-ACS in the UK and 20 727 patients with NSTE-ACS in Switzerland were included in the study. Discrimination of in-hospital death by the GRACE 2.0 score was good in male patients (area under the receiver operating characteristic curve [AUC] 0·86, 95% CI 0·86-0·86) and notably lower in female patients (0·82, 95% CI 0·81-0·82; p<0·0001). The GRACE 2.0 score underestimated in-hospital mortality risk in female patients, favouring their incorrect stratification to the low-to-intermediate risk group, for which the score does not indicate early invasive treatment. Accounting for sex differences, GRACE 3.0 showed superior discrimination and good calibration with an AUC of 0·91 (95% CI 0·89-0·92) in male patients and 0·87 (95% CI 0·84-0·89) in female patients in an external cohort validation. GRACE 3·0 led to a clinically relevant reclassification of female patients to the high-risk group. INTERPRETATION The GRACE 2.0 score has limited discriminatory performance and underestimates in-hospital mortality in female patients with NSTE-ACS. The GRACE 3.0 score performs better in men and women and reduces sex inequalities in risk stratification. FUNDING Swiss National Science Foundation, Swiss Heart Foundation, Lindenhof Foundation, Foundation for Cardiovascular Research, and Theodor-Ida-Herzog-Egli Foundation

Bleeding complications following acute myocardial infarction : time trends, risk assessment and associated prognosis

Background: In patients with acute myocardial infarction (MI), bleeding complications are common and associated with worse prognosis. This thesis aimed to investigate the epidemiology, risk assessment and associated outcomes of bleeding complications in patients with acute MI. Methods and results: Study I: Patients with acute MI enrolled in the SWEDEHEART registry from 1995–2018 were included (n=371 431). The incidence of in-hospital and out-of-hospital bleeding at one-year was investigated parallel to treatment changes and ischemic outcomes. From 1995 to 2018, in-hospital bleeding increased from 0.5% to 1.3% and out-of-hospital bleeding increased from 2.5% to 4.8% along with increased use of invasive revascularisation and more efficient antithrombotic treatment. Meanwhile in-hospital and out-of-hospital ischemic outcomes decreased from 12.1% to 5.6% and 27.5% to 15.1%, respectively. Study II: Patients with acute MI enrolled in the SWEDEHEART registry from 2009– 2014 were included (n=97 597). A prediction model for in-hospital bleeding was created using logistic regression and the performance was compared to that of the CRUSADE and ACTION scores. Due to miscalibration, the CRUSADE and ACTION scores were recalibrated. The SWEDEHEART score, consisting of five baseline variables (haemoglobin, age, sex, creatinine, and C-reactive protein) plus one interaction term (haemoglobin and sex) had a C-index of 0.80 as compared with 0.72 and 0.73 for the recalibrated CRUSADE and ACTION scores, respectively. Study III: Patients with acute MI enrolled in the SWEDEHEART registry from 2007–2016 and discharged alive on any antithrombotic treatment were included (n=149 447). The incidence, associated outcomes and predictors of upper gastrointestinal bleeding (UGIB) was investigated. The incidence of UGIB within one year after discharge was 1.5% and experiencing UGIB was associated with increased risk of mortality and stroke, but not significantly associated with MI. Using both logistic regression and machine-learning models, new potential predictors of UGIB were found, such as smoking status and blood glucose. Study IV: Patients with acute MI enrolled in the SWEDEHEART registry and discharged alive on any antithrombotic treatment from 2012–2017 were included (n=86 736). The incidence and associated mortality risk of ischemic (MI or ischemic stroke) and bleeding events was investigated. Within one year after discharge, the incidence rate of ischemic and bleeding events was 5.7/100 person years and 4.8/100 person years, respectively. Both ischemic and bleeding events were associated with higher risk of mortality as compared with no event, with adjusted hazard ratios (HR)s of 4.16 (95% CI 3.91 to 4.43) and 3.43 (95% CI 3.17 to 3.71), respectively. In a direct comparison of ischemic vs bleeding event, the adjusted HR was 1.27 (95% CI 1.15 to 1.40.) Conclusion: In the past two decades, the incidence of both short- and long-term bleeding events has nearly doubled in patients with acute MI. The five-item SWEDEHEART score predicts inhospital bleeding in patients with acute MI more accurately than the recalibrated CRUSADE and ACTION scores. Among patients with a recent MI, upper gastrointestinal bleeding is common and associated with poorer prognosis. Beyond the known risk factors for bleeding, other predictors for upper gastrointestinal bleeding may be present. In patients discharged after an acute MI, ischemic events were more common and associated with higher risk of mortality than bleeding events

Current and Future Use of Artificial Intelligence in Electrocardiography.

Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.Manuel Marina-Breysse has received funding from European Union’s Horizon 2020 research and innovation program under the grant agreement number 965286; Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation, MAESTRIA Consortium; and EIT Health, a body of the European Union.S

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