Daniel Lindholm

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we present a trained and validated deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model has a C-statistic of ≥0.95 for OMI and ≥0.87 for non-OMI infarctions and can localize culprit lesions in the three main coronary branches, which can guide the angiographer. Performance is similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for the identification and localization of OMI has the potential to shorten the time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria misses many acute occlusion myocardial infarctions (OMI) and triggers unnecessary acute angiographies. Here, we trained and validated a deep learning model using 540,372 emergency ECGs paired with definitive catheterization outcomes. The model achieved a C-statistic of >0.95 for OMI and >0.87 for non-OMI infarctions and could localize culprit lesions in the left main/LAD, LCX, and RCA coronary branches, which can guide the angiographer. Performance was similar across age, sex, and ECG hardware subgroups. Obviating dependence on ST-elevations and troponins, this model for identification and localization of OMI has the potential to shorten time to reperfusion of an acute coronary occlusion and save resources. Because human oversight of OMI detection on the ECG is limited, randomized clinical trials with patient-relevant outcomes are warranted.

Aims: In patients with heart failure, treatment patterns in the last years of life have not been assessed at large scale. We aimed to assess whether heart failure treatment patterns up to 5 years prior to death changed over time. Methods and results: In a cohort study covering the whole Swedish population, we assessed all heart failure patients who died between 1 July 2007 and 31 December 2020 for evidence-based treatments. The proportion on the respective treatment at the time of death was examined by year of death using binomial regression. Looking back in time, treatment discontinuation rates were estimated using Poisson regression on time-split data. Combining these models, the proportion on each medication was estimated up to 5 years prior to death. A total of 364 480 patients died with heart failure during the study period. Half were women, and the median (interquartile range) age at death was 86 (79–90). The use of all heart failure treatments decreased gradually closer to death, but the discontinuation rate of beta blockers decreased over time, resulting in an increasing proportion of patients on treatment at the time of death. Conclusion: In patients with heart failure, a changing pattern of medical treatment during the last years of life was observed, most notably with an increasing use of beta blockers. This may in part be due to a changing pattern of comorbidities over time, with an increase in e.g. hypertension and atrial fibrillation, but a decline in ischaemic heart disease.

With modern treatments for heart failure with reduced ejection fraction (EF), indicative of impaired cardiac systolic function, patients may exhibit an increase in EF. Limited data are available regarding the clinical management of this growing population, categorized as heart failure with improved EF (HFimpEF), which has a high event rate and has been excluded from virtually all prior heart failure outcomes trials. In a prespecified analysis of the DELIVER trial (NCT03619213), of a total of 6,263 participants with symptomatic heart failure and a left ventricular EF >40%, 1,151 (18%) had HFimpEF, defined as patients whose EF improved from ≤40% to >40%. Participants were randomized to 10 mg dapagliflozin or placebo daily and the primary outcome of the trial was a composite of cardiovascular death or worsening heart failure (heart failure hospitalization or an urgent heart failure visit). Participants with HFimpEF had similar event rates to those with an EF consistently >40%. In participants with HFimpEF, dapagliflozin reduced the primary composite outcome (hazard ratio (HR) = 0.74, 95% confidence interval (CI) = 0.56–0.97), first worsening heart failure events (HR = 0.84, 95% CI = 0.61–1.14), cardiovascular death (HR = 0.62, 95% CI = 0.41–0.96) and total worsening heart failure events (rate ratio = 0.68, 95% CI = 0.50–0.94) to a similar extent as for individuals with an EF consistently >40%. These data suggest that patients with HFimpEF who are symptomatic may benefit from the addition of a sodium/glucose cotransporter 2 inhibitor to previously instituted guideline-directed medical therapy to further reduce morbidity and mortality.

Background: Hierarchical composite endpoints are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g. most severe) event of a patient. Hierarchical composite endpoint can be analysed with the win odds, an adaptation of win ratio to include ties. One of the difficulties in interpreting hierarchical composite endpoint is the lack of proper tools for visualizing the treatment effect captured by hierarchical composite endpoint, given the complex nature of the endpoint which combines events of different types. Methods: Hierarchical composite endpoints usually combine time-to-event outcomes and continuous outcomes into a composite; hence, it is important to capture not only the shift from more severe categories to less severe categories in the active group in comparison to the control group (as in any ordinal endpoint), but also changes occurring within each category. We introduce the novel maraca plot which combines violin plots (with nested box plots) to visualize the density of the distribution of the continuous outcome and Kaplan–Meier plots for time-to-event outcomes into a comprehensive visualization. Conclusion: The novel maraca plot is suggested for visualization of hierarchical composite endpoints consisting of several time-to-event outcomes and a continuous outcome. It has a very simple structure and therefore easily communicates both the overall treatment effect and the effect on individual components.