Risk calculator for incident atrial fibrillation across a range of prediction horizons.

BACKGROUND: The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years. METHODS: We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc and C2HEST scores. RESULTS: Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA2DS2-VASc and C2HEST scores. The models are available as a web-based FIND-AF calculator. CONCLUSIONS: The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.

Risk of atrial fibrillation and association with other diseases: protocol of the derivation and international external validation of a prediction model using nationwide population-based electronic health records.

INTRODUCTION: Atrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. METHODS AND ANALYSIS: We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states. ETHICS AND DISSEMINATION: Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION NUMBER: A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).

Post-COVID-19 Peripartum Cardiomyopathy: Experience from a Large Tertiary Referral Center.

BACKGROUND: Peripartum cardiomyopathy (PPCM) is a rare but potentially devastating complication of pregnancy. Although the pathophysiology of PPCM is not fully understood, there are known risk factors for developing PPCM, which are maternal and gestation related. In the first wave of the coronavirus disease 2019 (COVID-19) pandemic, we witnessed an elevated incidence of PPCM among COVID-19 survivors. OBJECTIVES: To present a single-center case series of three patients diagnosed with peripartum cardiomyopathy after recovered from COVID-19 during the index pregnancy. METHODS: In this single center case study, all patients diagnosed with PPCM at our institute during the examined time frame were included. Electronic medical records were studied. RESULTS: Three patients previously diagnosed with asymptomatic or mildly symptomatic COVID-19 disease during pregnancy presented with PPCM before or shortly after delivery. Patients underwent testing to rule out residual COVID-19 myocarditis, were treated pharmacologically and with wearable defibrillators as needed, and were examined in follow-up 1-9 months after delivery. CONCLUSIONS: Residual endothelial damage due to COVID-19 disease, even if originally mild in presentation, could predispose pregnant patients to PPCM and should be considered as a risk factor when assessing patients with new onset symptoms of heart failure. Further research is needed to confirm this hypothesis and fully determine the underlying pathophysiology. These preliminary findings warrant a high index of suspicion for PPCM in COVID-19 recoverers.

Prediction models for heart failure in the community: A systematic review and meta-analysis.

AIMS: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS: From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

Incident cardiovascular, renal, metabolic diseases and death in individuals identified for risk-guided atrial fibrillation screening: a nationwide cohort study.

OBJECTIVE: Risk-guided atrial fibrillation (AF) screening may be an opportunity to prevent adverse events in addition to stroke. We compared events rates for new diagnoses of cardio-renal-metabolic diseases and death in individuals identified at higher versus lower-predicted AF risk. METHODS: From the UK Clinical Practice Research Datalink-GOLD dataset, 2 January 1998-30 November 2018, we identified individuals aged ≥30 years without known AF. The risk of AF was estimated using the FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation) risk score. We calculated cumulative incidence rates and fit Fine and Gray's models at 1, 5 and 10 years for nine diseases and death adjusting for competing risks. RESULTS: Of 416 228 individuals in the cohort, 82 942 were identified as higher risk for AF. Higher-predicted risk, compared with lower-predicted risk, was associated with incident chronic kidney disease (cumulative incidence per 1000 persons at 10 years 245.2; HR 6.85, 95% CI 6.70 to 7.00; median time to event 5.44 years), heart failure (124.7; 12.54, 12.08 to 13.01; 4.06), diabetes mellitus (123.3; 2.05, 2.00 to 2.10; 3.45), stroke/transient ischaemic attack (118.9; 8.07, 7.80 to 8.34; 4.27), myocardial infarction (69.6; 5.02, 4.82 to 5.22; 4.32), peripheral vascular disease (44.6; 6.62, 6.28 to 6.98; 4.28), valvular heart disease (37.8; 6.49, 6.14 to 6.85; 4.54), aortic stenosis (18.7; 9.98, 9.16 to 10.87; 4.41) and death from any cause (273.9; 10.45, 10.23 to 10.68; 4.75). The higher-risk group constituted 74% of deaths from cardiovascular or cerebrovascular causes (8582 of 11 676). CONCLUSIONS: Individuals identified for risk-guided AF screening are at risk of new diseases across the cardio-renal-metabolic spectrum and death, and may benefit from interventions beyond ECG monitoring.

Development of saliva-based cardiac troponin I point-of-care test using alpha-amylase depletion: a feasibility study.

INTRODUCTION: Cardiac troponin (cTn) is the biomarker of choice for detection of myocardial injury. There is a great need for simple point-of-care (POC) troponin testing among patients with chest pain, mainly in the prehospital setting. The purpose of this study was to evaluate the presence of cardiac troponin I (cTnI) in saliva of patients with myocardial injury using alpha-amylase depletion technique. METHODS: Saliva samples were collected from 40 patients with myocardial injury who were tested positive for conventional high-sensitivity cardiac troponin T (cTnT) blood tests, and from 66 healthy volunteers. Saliva samples were treated for the removal of salivary alpha-amylase. Treated and untreated samples were tested with blood cTnI Rapid Diagnostic Test. Salivary cTnI levels were compared to blood cTnT levels. RESULTS: Thirty-six of 40 patients with positive blood cTnT had positive salivary samples for cTnI following alpha-amylase depletion treatment (90.00% sensitivity). Moreover, three of the four negative saliva samples were obtained from patients with relatively low blood cTnT levels of 100 ng/L or less (96.88% sensitivity for 100 ng/L and above). The negative predictive value was 93.65% and rose up to 98.33% considering the 100 ng/L cutoff. Positive predictive values were 83.72% and 81.58%, respectively. Among 66 healthy volunteers and 7 samples yielded positive results (89.39% specificity). CONCLUSION: In this preliminary work, the presence of cTnI in saliva was demonstrated for the first time to be feasibly identified by a POC oriented assay. The specific salivary alpha-amylase depletion technique was shown to be crucial for the suggested assay.

Predictors of Adverse Outcome in High-Risk Percutaneous Coronary Interventions Patients.

BACKGROUND: Identifying high-risk percutaneous coronary intervention (PCI) patients is challenging. We aimed to evaluate which high-risk patients are prone to adverse events. METHODS: We performed a retrospective study including consecutive high-risk PCIs from 2005 to 2018 in a large tertiary medical centre. Patients with unprotected left main (LM) disease, last patent coronary vessel, or 3-vessel coronary artery disease with left ventricular ejection fraction < 35% were included. A predictive 30-day major adverse cardiac events (MACE) score consisting of any myocardial infarction, all-cause death, or target-vessel revascularisation was constructed. RESULTS: From 2005 to 2018, a total of 1890 patients who underwent PCI met the predefined high-risk PCI criteria. Mortality rate was 8.8% at 30 days and 20.7% at 1 year, and 30-day MACE rate was 14.2% and 33.5% at 1 year. Predictors of short-term MACE were New York Heart Association functional class (NYHA) 4 (hazard ratio [HR] 6.65; P < 0.001), systolic blood pressure (SBP) < 90 mm Hg (HR 4.93; P < 0.001), creatinine > 1.3 mg/dL (HR 3.57; P < 0.001), hemoglobin < 11.0 g/dL (HR 3.07; P < 0.001), pulmonary artery systolic pressure > 50 mm Hg (HR 2.06; P < 0.001), atrial fibrillation (HR 1.74; P < 0.001), and LM disease (HR 2.04; P < 0.001) or last patent vessel (HR 1.70; P = 0.002). A score constructed from these parameters reached a sensitivity of 90% and a specificity of 81% with areas under the receiver operating characteristic curve of 0.92 for MACE and 0.94 with 89% sensitivity and 87% specificity for all-cause mortality. CONCLUSIONS: Specific features such as LM lesion or last patent conduit, pulmonary hypertension, atrial fibrillation, anemia, and renal failure, along with low SBP and NYHA 4, aid risk stratification and consideration of further treatment measures.

Prediction of short-term atrial fibrillation risk using primary care electronic health records.

OBJECTIVE: Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). METHODS: We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS: Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS: FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.

Nonobstructive coronary atherosclerosis is associated with adverse prognosis among patients diagnosed with myocardial infarction without obstructive coronary arteries.

BACKGROUND AND AIMS: The prognostic impact of nonobstructive coronary artery disease (CAD), as opposed to normal coronary arteries, on long-term outcomes of patients with myocardial infarction with no obstructive coronary arteries (MINOCA) is unclear. We aimed to address the association between nonobstructive-CAD and major adverse events (MAE) following MINOCA. METHODS: We conducted a retrospective cohort study of consecutive MINOCA patients admitted to a large referral medical center between 2005 and 2018. Patients were classified according to coronary angiography as having either normal-coronaries or nonobstructive-CAD. The primary outcome was MAE, defined as the composite of all-cause mortality and recurrent acute coronary syndrome (ACS). RESULTS: Of the 1544 MINOCA patients, 651 (42%) had normal coronaries, and 893 (58%) had CAD. The mean age was 61.2 ± 12.6 years, and 710 (46%) were females. Nonobstructive-CAD patients were older and less likely to be females, with higher rates of diabetes, hypertension, dyslipidemia, atrial fibrillation, and chronic renal-failure (p < 0.05). At a median follow-up of 7 years, MAE occurred in 203 (23%) patients and 67 (10%) patients in the nonobstructive-CAD and normal-coronaries groups, respectively (p < 0.01). In multivariable models, nonobstructive -CAD was significantly associated with long-term MAE [adjusted-hazard-ratio (aHR):1.67, 95% confidence-interval (95%CI):1.25-2.23; p < 0.001]. Other factors associated with a higher MAE-risk were older-age (aHR:1.05,95%CI:1.03-1.06; p < 0.001) and left ventricular ejection-fraction<40% (aHR:3.04,95%CI:2.03-4.57; p < 0.001), while female-sex (aHR:0.72, 95%CI: 0.56-0.94; p=0.014) and sinus rhythm at presentation (aHR:0.66, 95%CI: 0.44-0.98; p=0.041) were associated with lower MAE-risk. CONCLUSIONS: In MINOCA, nonobstructive-CAD is independently associated with a higher MAE-risk than normal-coronaries. This finding may promote risk-stratification of patients with nonobstructive-CAD-MINOCA who require tighter medical follow-up and treatment optimization.

Ivabradine for Uncontrolled Sinus Tachycardia in Thyrotoxic Cardiomyopathy - Case Report.

BACKGROUND: Beta-blockers, mainly propranalol, are usually administered to control heart rate in patients with thyrotoxicosis, especially when congestive heart failure presents. However, when thyrotoxicosis is not controlled, heart rate may be difficult to control even with maximal doses of propranolol. This presentation alerts physicians to the possibility of using ivabradine, a selective inhibitor of the sinoatrial pacemaker, for the control of heart rate. CASE PRESENTATION: We present a 37-year-old woman with thyrotoxicosis and congestive heart failure whose heart rate was not controlled with a maximal dose of beta blockers during a thyroid storm. The addition of ivabradine, a selective inhibitor of the sinoatrial pacemaker, controlled her heart rate within 48 hours. CONCLUSION: Ivabradine should be considered in patients with thyrotoxicosis, including those with heart failure, in whom beta blockers are insufficient to control heart rate.

Peripheral Arterial Disease in Patients With Acute Coronary Syndrome: Results From a Large Real-World Registry.

BACKGROUND: Limited data exist regarding the significance of peripheral arterial disease (PAD) in patients with acute coronary syndrome (ACS). METHODS: We evaluated 16,922 consecutive ACS patients who were prospectively included in a national ACS registry. The co-primary endpoint included 30 days major adverse cardiovascular event (MACE) (re-infarction, stroke, and/or cardiovascular death) and 1-year mortality. RESULTS: PAD patients were older (70±11 vs 63±13; p<0.01), male predominance (80% vs 77%; p=0.01), and more likely to sustain prior cardiovascular events. PAD patients were less likely to undergo coronary angiography (69% vs 83%; p<0.001) and revascularisation (80% vs 86%; p<0.001). Patients with PAD were more likely to sustain 30-day MACE (22% vs 14%; p<0.001) and mortality (10% vs 4.4%; p<0.001), as well as re-hospitalisation (23% vs 19%; p=0.001). After adjusting for potential confounders, PAD remained an independent predictor of 30-day MACE (odds ratio [OR], 1.6 [95% confidence interval (CI), 1.24-2.06]). Patients with compared to those without PAD had 2.5 times higher 1-year mortality rate (22% vs 9%; p<0.001). Co-existence of PAD remained an independent predictor of 1-year mortality after adjustment for potential confounders by multivariable regression analysis (OR, 1.62; 95% CI, 1.4-1.9). PAD was associated with a significant higher 1-year mortality rate across numerous sub-groups of patients including type of myocardial infarction (ST-elevation myocardial infarction vs non-ST-elevation myocardial infarction), and whether the patient underwent revascularisation. CONCLUSIONS: Acute coronary syndrome with concomitant PAD represents a high-risk subgroup that warrants special attention and a more tailored approach.

Machine learning-based prediction of 1-year mortality for acute coronary syndrome✰.

BACKGROUND: Clinical risk assessment with quantitative formal risk scores may add to intuitive physician risk assessment and are advised by the international guidelines for the management of acute coronary syndrome (ACS) patients. Most previous studies have used the binary regression/classification approach (dead/alive) for long-term mortality post-ACS, without considering the time-to-event as in survival analysis. The use of machine learning (ML)-based survival models has yet to be validated. The primary objective was to compare survival prediction performance of 1-year mortality following ACS of two newly developed ML-based models [random survival forest (RSF) and deep learning (DeepSurv)] with the traditional Cox-proportional hazard (CPH) model. The secondary objective was external validation of the findings. METHODS: This was a retrospective, supervised learning data mining study based on the Acute Coronary Syndrome Israeli Survey (ACSIS) and the Myocardial Ischemia National Audit Project (MINAP). The ACSIS data were divided to train/test in a 70/30 fashion. Next, the models were externally validated on the MINAP data. Harrell's C-index, inverse probability of censoring weighting (IPCW), and the Brier-score were used for models' performance comparison. RESULTS: RSF performed best among the three models, with Harrell's C-index on training and testing sets reaching 0.953 and 0.924 respectively, followed by CPH multivariate selected model (0.805/0.849), CPH Univariate selected model (0.828/0.806), DeepSurv model (0.801/0.804), and the traditional CPH model (0.826/0.738). The RSF model also had the highest performance on the validation data set with 0.811 for Harrell's C-index, 0.844 for IPCW, and 0.093 for Brier score. The CPH model performance on the validation set had C-index range between 0.689 to 0.790, 0.713 to 0.826 for IPCW, and 0.094 to 0.103 Brier score. CONCLUSIONS: RSF survival predictions for long-term mortality post-ACS show improved model performance compared with the classic statistical method. This may benefit patients by allowing better risk stratification and tailored therapy, however further prospective evaluations are required.

Temporal Trends and Outcome of Patients with Acute Coronary Syndrome and Prior Myocardial Infarction.

Patients who have previously had a myocardial infarction (MI) are considered a high-risk group with increased risk for cardiovascular (CV) events. During the last decade, the outcome of acute coronary syndrome (ACS) patients has improved due to advances in medical therapy and interventional techniques. We aimed to examine temporal trends and outcomes of patients with prior MI admitted due to ACS from the Acute Coronary Syndrome Israeli Survey (ACSIS). Included were 16,934 ACS patients, of whom 31.4% had prior MI. For temporal trend analysis, the cohort was divided into an early period (2000-2008) and late period (2010-2018). For patients with prior MI, patients in the late period had a higher rate of CV risk factors and were treated more frequently with revascularization and guidelines-directed medical therapy. Recurrent MI (6.7% vs. 12%, p < 0.001), MACE (10.6% vs. 21%, p < 0.001) and 1-year mortality (10.7% vs. 14.6%, p < 0.001) were significantly lower in the late period. However, the mortality rate for patients with prior MI remained higher compared with patients without prior MI (10.7% vs. 6.8% p < 0.001) with an overall higher mortality rate in the STEMI group. Thus, despite significant improvement in outcome measures in the contemporary era, ACS patients with prior MI are still at increased risk for recurrent ischemic CV events and mortality.

WITHDRAWN: Ivabradine for Uncontrolled Sinus Tachycardia in Thyrotoxic Cardiomyopathy ­ Case Report

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Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets.

BACKGROUND: Various prognostic models for mortality prediction following ST-segment elevation myocardial infarction (STEMI) have been developed over the past two decades. Our group has previously demonstrated that machine learning (ML)-based models can outperform known risk scores for 30-day mortality post-STEMI. The study aimed to redevelop an ML-based random forest prediction model for 30-day mortality post-STEMI and externally validate it on a large cohort. METHODS: This was a retrospective, supervised learning, data mining study developed on the Acute Coronary Syndrome Israeli Survey (ACSIS) registry and the Myocardial Ischemia National Audit Project (MINAP) for external validation. Patients included received reperfusion therapy for STEMI between 2006 and 2016. Discrimination and calibration performances were assessed for two developed models and compared with the Global Registry of Acute Cardiac Events (GRACE) score. RESULTS: The ACSIS cohort (2,782 included /15,212 total) and MINAP cohort (22,693 included/735,000 total) were significantly different in most variables, yet similar in 30-day mortality rate (4.3-4.4%). Random forest models were developed on the ACSIS cohort with a full model including all 32 variables and a simple model including the 10 most important ones. Features' importance was calculated using the varImp function measuring how much each feature contributes to the data's homogeneity. Applying the optimized models on the MINAP validation cohort showed high discrimination of area under the curve (AUC) = 0.804 (0.786-0.822) for the full model, and AUC = 0.787 (0.748-0.780) using the simple model, compared with the GRACE risk score discrimination of AUC = 0.764 (0.748-0.780). All models were not well calibrated for the MINAP data. Following Platt scaling on 20% of the MINAP data, the random forest models calibration improved while the GRACE calibration did not change. CONCLUSIONS: The random forest predictive model for 30-day mortality post STEMI, developed on the ACSIS national registry, has been validated in the MINAP large external cohort and can be applied early at admission for risk stratification. The model performed better than the commonly used GRACE score. Furthermore, to the best of our knowledge, this is the first externally validated ML-based model for STEMI.

Acute myocardial infarction in the Covid-19 era: Incidence, clinical characteristics and in-hospital outcomes-A multicenter registry.

BACKGROUND: We aimed to describe the characteristics and in-hospital outcomes of ST-segment elevation myocardial infarction (STEMI) patients during the Covid-19 era. METHODS: We conducted a prospective, multicenter study involving 13 intensive cardiac care units, to evaluate consecutive STEMI patients admitted throughout an 8-week period during the Covid-19 outbreak. These patients were compared with consecutive STEMI patients admitted during the corresponding period in 2018 who had been prospectively documented in the Israeli bi-annual National Acute Coronary Syndrome Survey. The primary end-point was defined as a composite of malignant arrhythmia, congestive heart failure, and/or in-hospital mortality. Secondary outcomes included individual components of primary outcome, cardiogenic shock, mechanical complications, electrical complications, re-infarction, stroke, and pericarditis. RESULTS: The study cohort comprised 1466 consecutive acute MI patients, of whom 774 (53%) were hospitalized during the Covid-19 outbreak. Overall, 841 patients were diagnosed with STEMI: 424 (50.4%) during the Covid-19 era and 417 (49.6%) during the parallel period in 2018. Although STEMI patients admitted during the Covid-19 period had fewer co-morbidities, they presented with a higher Killip class (p value = .03). The median time from symptom onset to reperfusion was extended from 180 minutes (IQR 122-292) in 2018 to 290 minutes (IQR 161-1080, p < .001) in 2020. Hospitalization during the Covid-19 era was independently associated with an increased risk of the combined endpoint in the multivariable regression model (OR 1.65, 95% CI 1.03-2.68, p value = .04). Furthermore, the rate of mechanical complications was four times higher during the Covid-19 era (95% CI 1.42-14.8, p-value = .02). However, in-hospital mortality remained unchanged (OR 1.73, 95% CI 0.81-3.78, p-value = .16). CONCLUSIONS: STEMI patients admitted during the first wave of Covid-19 outbreak, experienced longer total ischemic time, which was translated into a more severe disease status upon hospital admission, and a higher rate of in-hospital adverse events, compared with parallel period.

Intensity of statin treatment after acute coronary syndrome, residual risk, and its modification by alirocumab: insights from the ODYSSEY OUTCOMES trial.

AIMS: Statins are pivotal to the secondary prevention of major adverse cardiovascular events, but some patients are statin-intolerant. We examined the effects of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor alirocumab on the risk of major adverse cardiovascular events according to the intensity of background statin treatment. METHODS AND RESULTS: The ODYSSEY OUTCOMES trial compared alirocumab with placebo in 18,924 patients with acute coronary syndrome and dyslipidaemia despite intensive or maximum-tolerated statin treatment (including no statin if intolerance was documented). The primary outcome (major adverse cardiovascular events) comprised coronary heart disease death, non-fatal myocardial infarction, ischaemic stroke, or unstable angina. Median follow-up was 2.8 years. Baseline statin treatment was high-intensity (88.8%), low/moderate-intensity (8.7%) or none (2.4%). Median baseline low-density lipoprotein cholesterol was 86, 89 and 139 mg/dL (P < 0.001) in these statin treatment categories, respectively. Alirocumab produced similar relative reductions in low-density lipoprotein cholesterol from baseline across statin treatment subgroups, but the mean absolute reductions differed (52.9, 56.7 and 86.1 mg/dL, respectively; P < 0.001). With placebo, the incidence of major adverse cardiovascular events was highest in the no statin subgroup (10.8%, 10.7% and 26.0% respectively). Alirocumab reduced major adverse cardiovascular events in each statin subgroup (hazard ratio 0.88, 95% confidence interval (CI) 0.80-0.96; 0.68, 0.49-0.94; and 0.65, 0.44-0.97, respectively; Pinteraction = 0.14) with a gradient of absolute risk reduction: 1.25%, 95% CI 0.34-2.16; 3.16%, 0.38-5.94; 7.97%, 0.42-15.51; Pinteraction = 0.106). CONCLUSIONS: PCSK9 inhibition with alirocumab reduces the relative risk of major adverse cardiovascular events after acute coronary syndrome irrespective of background statin treatment. However, patients on no statin are at high absolute risk for recurrent major adverse cardiovascular events; alirocumab substantially reduces that risk. PCSK9 inhibition may be an important therapeutic strategy for statin-intolerant patients with acute coronary syndrome.