Zachary T. Yoneda, MD, MSCI1; Katherine C. Anderson, MS, CSG1; Joseph A. Quintana, MD1; Matthew J. O’Neill, BS2; Richard A. Sims, MD1; Andrew M. Glazer, PhD3; Christian M. Shaffer, BS3; Diane M. Crawford, RN1; Thomas Stricker, MD, PhD4; Fei Ye, PhD5; Quinn Wells, MD, MSCI1; Lynne W. Stevenson, MD1; Gregory F. Michaud, MD1; Dawood Darbar, MBChB, MD6; Steven A. Lubitz, MD, MPH7,8; Patrick T. Ellinor, MD, PhD7,8; Dan M. Roden, MD1,9,10,11; M. Benjamin Shoemaker, MD, MSCI1
1Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
Importance Early-onset atrial fibrillation (AF) can be the initial manifestation of a more serious underlying inherited cardiomyopathy or arrhythmia syndrome.
Objective To examine the results of genetic testing for early-onset AF.
Design, Setting, and Participants This prospective, observational cohort study enrolled participants from an academic medical center who had AF diagnosed before 66 years of age and underwent whole genome sequencing through the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine program. Participants were enrolled from November 23, 1999, to June 2, 2015. Data analysis was performed from October 24, 2020, to March 11, 2021.
Exposures Rare variants identified in a panel of 145 genes that are included on cardiomyopathy and arrhythmia panels used by commercial clinical genetic testing laboratories.
Main Outcomes and Measures Sequencing data were analyzed using an automated process followed by manual review by a panel of independent, blinded reviewers. The primary outcome was classification of rare variants using American College of Medical Genetics and Genomics criteria: benign, likely benign, variant of undetermined significance, likely pathogenic, or pathogenic. Disease-associated variants were defined as pathogenic/likely pathogenic variants in genes associated with autosomal dominant or X-linked dominant disorders.
Results Among 1293 participants (934 [72.2%] male; median [interquartile range] age at enrollment, 56 [48-61] years; median [interquartile range] age at AF diagnosis, 50 [41-56] years), genetic testing identified 131 participants (10.1%) with a disease-associated variant, 812 (62.8%) with a variant of undetermined significance, 92 (7.1%) as heterozygous carriers for an autosomal recessive disorder, and 258 (20.0%) with no suspicious variant. The likelihood of a disease-associated variant was highest in participants with AF diagnosed before the age of 30 years (20 of 119 [16.8%; 95% CI, 10.0%-23.6%]) and lowest after the age of 60 years (8 of 112 [7.1%; 95% CI, 2.4%-11.9%]). Disease-associated variants were more often associated with inherited cardiomyopathy syndromes compared with inherited arrhythmias. The most common genes were TTN (n = 38), MYH7 (n = 18), MYH6 (n = 10), LMNA (n = 9), and KCNQ1 (n = 8).
Conclusions and Relevance In this cohort study, genetic testing identified a disease-associated variant in 10% of patients with early-onset AF (the percentage was higher if diagnosed before the age of 30 years and lower if diagnosed after the age of 60 years). Most pathogenic/likely pathogenic variants are in genes associated with cardiomyopathy. These results support the use of genetic testing in early-onset AF.
Background: Cardiac arrhythmias are a leading cause of death. The mainstay method for diagnosing arrhythmias (eg, atrial fibrillation) and cardiac conduction disorders (eg, prolonged corrected QT interval [QTc]) is by using 12-lead electrocardiography (ECG). Handheld 12-lead ECG devices are emerging in the market. In tandem with emerging technology options, evaluations of device usability should go beyond validation of the device in a controlled laboratory setting and assess user perceptions and experiences, which are crucial for successful implementation in clinical practice.
Objective: This study aimed to evaluate clinician and patient perceptions and experiences, regarding the usability of a handheld 12-lead ECG device compared to a conventional 12-lead ECG machine, and generalizability of this user-centered approach.
Methods: International Organization for Standardization Guidelines on Usability and the Technology Acceptance Model were integrated to form the framework for this study, which was conducted in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient underwent 2 ECGs (1 by each device) in 2 postures (supine and standing) acquired in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analyzed using linear regression. Electrocardiographic parameters (QT interval, QTc interval, heart rate, PR interval, QRS interval) and participant satisfaction surveys were collected. Device reliability was assessed by evaluating the mean difference of QTc measurements within ±15 ms, intraclass correlation coefficient, and level of agreement of the devices in detecting atrial fibrillation and prolonged QTc. Clinicians’ perceptions and feedback were assessed with semistructured interviews based on the Technology Acceptance Model.
Results:A total of 100 patients (age: mean 57.9 years, SD 15.2; sex: male: n=64, female n=36) and 11 clinicians (experience acquiring ECGs daily or weekly 10/11, 91%) participated, and 783 ECGs were acquired. Mean differences in QTc measurements of both handheld and conventional devices were within ±15 ms with high intraclass correlation coefficients (range 0.90-0.96), and the devices had a good level of agreement in diagnosing atrial fibrillation and prolonged QTc (κ=0.68-0.93). Regardless of device, QTc measurements when patients were standing were longer duration than QTc measurements when patients were supine. Clinicians’ ECG acquisition times improved with usage (P<.001). Clinicians reported that device characteristics (small size, light weight, portability, and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Regardless of device, patients reported that they felt comfortable when they were connected to the ECG devices.
Conclusions: Reliability and usability of the handheld 12-lead ECG device were comparable to those of a conventional ECG machine. The user-centered evaluation approach helped us identify remediable action to improve the efficiency in using the device and identified highly desirable device features that could potentially help mass screening and remote assessment of patients. The approach could be applied to evaluate and better understand the acceptability and usability of new medical devices.
This study aimed to investigate the potential of a novel 3-dimensional (3D) mechanical wave velocity mapping technique, based on the natural mechanical waves produced by the heart itself, to approach a noninvasive 3D stiffness mapping of the left ventricle.
Myocardial fibrosis is recognized as a pathophysiological substrate of major cardiovascular disorders such as cardiomyopathies and valvular heart disease. As fibrosis leads to increased myocardial stiffness, ultrasound elastography measurements could provide important clinical information.
A 3D high frame rate imaging sequence was implemented on a high-end clinical ultrasound scanner to achieve 820 volumes/s when gating over 4 consecutive cardiac cycles. Five healthy volunteers and 10 patients with various degrees of aortic stenosis were included to evaluate feasibility and reproducibility. Mechanical waves were detected using the novel Clutter Filter Wave Imaging approach, shown to be highly sensitive to the weak tissue displacements caused by natural mechanical waves.
3D spatiotemporal maps of mechanical wave velocities were produced for all subjects. Only the specific mechanical wave at atrial contraction provided a full 3D coverage of the left ventricle (LV). The average atrial kick propagation velocity was 1.6 ± 0.2 m/s in healthy volunteers and 2.8 ± 0.8 m/s in patients (p = 0.0016). A high correlation was found between mechanical wave velocity and age (R2 = 0.88, healthy group), septal wall thickness (R2 = 0.73, entire group), and peak jet velocity across the aortic valve (R2 = 0.70). For 3 of the patients, the higher mechanical wave velocity coexisted with the presence of late gadolinium enhancement on cardiac magnetic resonance.
In this study, 3D LV mechanical wave velocities were visualized and measured in healthy volunteers and patients with aortic stenosis. The proposed imaging sequence and measurement technique allowed, for the first time, the measurement of full spatiotemporal 3D elasticity maps of the LV using ultrasound. (Ultrasonic markers for myocardial fibrosis and prognosis in aortic stenosis; NCT03422770)
Left atrial reservoir strain provides incremental value to left atrial volume index for evaluation of left ventricular filling pressure
Left atrial analysis is employed in diastolic assessment with left atrial volume index (LAVI) incorporated in the 2016 ASE/EACVI diastology guideline algorithm. LAVI has sub-optimal correlation with invasive left ventricular filling pressure (LVFP) and incorporation of left atrial reservoir strain (LASr) may improve diastolic assessment.
A cross-sectional prospective study of 139 patients was undertaken with all patients undergoing transthoracic echocardiography immediately prior to cardiac catheterization with invasive evaluation of LVFP. LASr by speckle tracking echocardiography and conventional echocardiographic parameters were assessed in relation to invasive LVFP. Modification of the 2016 guideline algorithm was performed with incorporation of LASr in place of LAVI (LASr ≤23% indicating elevated LVFP). Accuracy of the modified and conventional algorithm were assessed for predicting invasive LVFP.
The mean age was 63±12 years with 27% female. LASr demonstrated superior correlation and receiver operator characteristic for predicting LVFP than LAVI (LASr: r -.46 (p < 0.01), AUC: .82 vs LAVI: r .19 (p 0.02), AUC: .66). LASr of ≤23% was the optimal cut-off for discriminating elevated LVFP (sensitivity 80%, specificity 77%). Modification of the 2016 algorithm with incorporation of LASr in place of LAVI reclassified 12% of the patient cohort and improved concordance of echocardiographic and invasive LVFP assessment (modified algorithm κ .47 vs 2016 algorithm κ: .33). No patients were incorrectly reclassified by modified algorithm assessment.
LASr better predicts invasive LVFP than LAVI. Modification of the 2016 guideline algorithm with incorporation of LASr in place of LAVI improves accuracy of echocardiographic assessment of LVFP.
Importance Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.
Objective To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.
Design, Setting, and Participants This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.
Interventions Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3–trained echocardiographers independently and blindly evaluated each acquisition.
Main Outcomes and Measures Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.
Results A total of 240 patients (mean [SD] age, 61  years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.
Conclusions and Relevance This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.
Echocardiography-Guided Risk Stratification for Long QT Syndrome. J Am Coll Cardiol 2020;76:2834-2843.
Is it possible to identify a subset of patients at the highest risk for long QT syndrome (LQTS)–associated life-threatening cardiac events by examining the electromechanical window (EMW) negativity, as derived from continuous-wave Doppler echocardiography?
There were 651 patients with LQTS (mean age, 26 years; 60% females; 158 symptomatic; 51% LQTS type 1; 33% LQTS type 2; 11% LQTS type 3) and 50 healthy controls. EMW was calculated as the difference between the interval from QRS onset to aortic valve closure midline, as derived for continuous-wave Doppler, and the electrocardiogram-derived QT interval for the same beat.
A negative EMW was found among nearly all patients with LQTS compared to controls, with more profound EMW negativity in patients with symptomatic LQTS compared to those with asymptomatic LQTS (-52 ± 38 ms vs. -18 ± 29 ms; p < 0.0001). Logistic regression identified EMW, corrected QT interval, female sex, and LQTS genotype as univariate predictors of symptomatic status. After multivariate analysis, EMW remained an independent predictor of symptomatic status (odds ratio for each 10-ms decrease in EMW, 1.37; 95% confidence interval, 1.27-1.48; p < 0.0001). EMW outperformed corrected QT interval in predicting symptomatic patients. EMW correlation across sonographers showed excellent reliability.
In this validation study, patients with a history of LQTS-associated life-threatening cardiac events had a more profoundly negative EMW. EMW outperformed heart rate–corrected QT interval as a predictor of symptomatic status. EMW is now a clinically validated risk factor.
So far, efforts at risk prediction in LQTS have mainly focused on the electrical manifestations of this condition such as corrected QT interval and T-wave morphology. This fascinating manuscript ties together the abnormalities in electrical conduction with mechanical function abnormalities observable on echo with high interobserver reliability. In normal adults, the end of electrical systole occurs slightly before the end of mechanical systole, resulting in a positive EMW. A negative EMW occurs when there is a mismatch between the end of electrical and mechanical systole as a result of prolongation of electrical systole. The exact mechanism is speculative, and it is possible that EMW is a surrogate marker that highlights impaired relaxation and inhomogeneity across the ventricle as the substrate for arrhythmogenesis. EMW should be evaluated prospectively in the QTS population and potentially many other arrhythmogenic conditions.
Sara Machado, PhD1; Andrew Sumarsono, MD2,3; Muthiah Vaduganathan, MD, MPH4
1 Department of Health Policy, London School of Economics, London, United Kingdom 2 Department of Medicine, University of Texas Southwestern Medical Center, Dallas 3 Division of Hospital Medicine, Parkland Memorial Hospital, Dallas, Texas 4 Brigham and Women’s Hospital Heart & Vascular Center and Harvard Medical School, Boston, Massachusetts
Importance The association of socioeconomic status and cardiovascular outcomes has been well described, but little is known about whether longitudinal changes in wealth are associated with cardiovascular health status.
Objective To evaluate the association between midlife wealth mobility and risk of cardiovascular events.
Design, Setting, and Participants This longitudinal, retrospective cohort study included US adults 50 years or older who participated in the Health and Retirement Study. Participants in the primary analysis had no history of cardiovascular disease and had observations in at least two of three 5-year age intervals (50-54, 55-59, and 60-64 years) and follow-up after 65 years of age. Data were collected from January 1, 1992, to December 31, 2016, and analyzed from November 10, 2020, to April 26, 2021.
Exposures Quintiles of wealth (reflecting total nonhousing assets) were defined within each of 4 birth cohorts (1931-1935, 1936-1940, 1941-1945, and 1946-1950). Wealth mobility was defined as an increase or a decrease of 1 or more wealth quintiles and was compared with wealth stability (same quintile over time) using covariate-adjusted Cox proportional hazards regression models.
Main Outcomes and Measures Composite outcome of nonfatal cardiovascular event (myocardial infarction, heart failure, cardiac arrhythmia, or stroke) or cardiovascular death.
Results A total of 5579 participants were included in the primary analysis (mean [SD] age, 54.2 [2.6] years; 3078 women [55.2%]). During a mean (SD) follow-up of 16.9 (5.8) years, 1336 participants (24.0%) experienced a primary end point of nonfatal cardiovascular event or cardiovascular death (14.4 [95% CI, 13.6-15.2] per 1000 patient-years). Higher initial wealth (per quintile) was associated with lower cardiovascular risk (adjusted hazard ratio [aHR] per quintile, 0.89 [95% CI, 0.84-0.95]; P = .001). When compared with stable wealth, participants who experienced upward wealth mobility (by at least 1 quintile) had independently lower hazards of a subsequent nonfatal cardiovascular event or cardiovascular death (aHR, 0.84 [95% CI, 0.73-0.97]; P = .02), and participants who experienced downward wealth mobility had higher risks (aHR, 1.15 [95% CI, 1.00-1.32]; P = .046).
Conclusions and Relevance These findings suggest that upward wealth mobility relative to peers in late middle age is associated with lower risks of cardiovascular events or death after 65 years of age.
OBJECTIVES To identify whether machine learning from processing of continuous wave transforms (CWTs) to provide an “energy waveform” electrocardiogram (ewECG) could be integrated with echocardiographic assessment of subclinical systolic and diastolic left ventricular dysfunction (LVD).
BACKGROUND Asymptomatic LVD has management implications, but routine echocardiography is not undertaken in subjects at risk of heart failure. Signal processing of the surface ECG with the use of CWT can identify abnormal myocardial relaxation.
METHODS EwECG and echocardiography were undertaken in 398 participants at risk of heart failure (HF). Reduced global longitudinal strain (GLS #16%)), diastolic abnormalities (E/e0 >15, left atrial enlargement with E/e0 >10 or impaired relaxation) or LV hypertrophy deﬁned LVD. EwECG feature selection and supervised machine-learning by random forest (RF) classiﬁer was undertaken with 643 CWT-derived features and the Atherosclerosis Risk in Communities (ARIC) heart failure risk score.
RESULTS The ARIC score and 18 CWT features were selected to build a RF predictive model for LVD in a training dataset (n ¼ 287; 60% female, median age 71 [interquartile range: 68 to 74] years). Model performance was tested in an in-dependent group (n ¼ 111; 49% female, median age 61 years [59 to 66 years]), demonstrating 85% sensitivity and 72%speciﬁcity (area under the receiver-operating characteristic curve [AUC]: 0.83; 95% conﬁdence interval [CI]: 0.74 to 0.92). With ARIC score removed, sensitivity was 88% and speciﬁcity, 70% (AUC: 0.78; 95% CI: 0.70 to 0.86). RF models for reduced GLS and diastolic abnormalities including similar features had sensitivities that were unsuitable for screening. Conventional candidates for LVD screening (ARIC score, N-terminal pro–B-type natriuretic peptide, and standard auto-mated ECG analysis) had inferior discriminative ability. Integration of ewECG in screening of people at risk of HF would reduce need for echocardiography by 45% while missing 12% of LVD cases.
CONCLUSIONS Machine learning applied to ewECG is a sensitive screening test for LVD, and its integration into screening of patients at risk for HF would reduce the number of echocardiograms by almost one-half.
From the Chair of Rural & Remote Mental Health – Genevieve Fraser
Visitors to Canberra during budget week were able to immerse themselves in more than the budget thanks to the National Gallery’s well timed “Masterpieces from the National Gallery, London”, featuring Vincent Van Gogh’s famous Sunflowers. $5 million dollars’ worth of refurbishments were undertaken last year to eliminate all dark corners of the Canberra exhibition space to shed equal light on each masterpiece. Despite these efforts the darkness of Van Gogh’s tragic story remains. In 1880, after the paint had dried on the petals of his celebrated flowers, Van Gogh walked into a nearby field in Auvers, France and shot himself in the stomach with a revolver.
Politicians who were able to take in Sunflowers over the past few months might spare a thought for those who equally lose hope in fields and homes across rural Australia 2021. Rural Australians suicide at two to three times the national average – a tragedy the 2020 Mental Health Productivity Commission asked our leaders to focus on in future funding decisions.
The report explained unambiguously who was dying, where, and why. The central recommendation for rural Australia was that half of those who suicide in Australia are NOT engaged with the medical system so future mental health spending needed to focus on community-based prevention.
Leaders were also told our suicide prevention model needs to change because suicide rates escalate the further someone lives from a capital city. And this pattern is consistent across every state and Territory.
Many of those who suicide in the country have no mental health history but are dealing with a trauma. It could be the loss of a child, a marriage, or a job, and they simply don’t have the tools to cope. They live a long way from mental health specialists, are not talking to their GP’s about their problems, and most of their friends and family don’t recognise the signs.
The families of Dolly Everett and Dr Andrew Bryant have both generously shared their experiences in recent times and both called for more community awareness because it is friends and families that need to know the signs to look for.
Rural Australians considering suicide are most likely to be saved by someone who already knows them or their community. Friends, family and work mates are the main line of defence.
Rural mental health charities like Rural & Remote Mental Health talk face to face with rural people and train locals in suicide prevention because it is the only way to save those not using our mental health services.
We don’t run million-dollar advertising campaigns on council buses and highways because the people we need to reach don’t have council buses or highways. Our work runs on cups of tea, biscuits, sandwiches, town halls and thousands of kilometres on country roads. Our trained presenters live and work across all states and territories. Most have other community-based roles – rural adversity nurses, indigenous elders, mine employees.
If people in these communities put their rural postcode into an online mental health directory it would often deliver the message “there are no services in your area”. Clinics in neighbouring rural centres look promising until the fine print reveals it is visited once a month by a specialist from somewhere else.
The best way for us to save people is to visit more communities and more often. Without dedicated federal funding we can’t go where the risk is highest, only where there are funds ‘left over’ in local health network budget’s for prevention work. Communities support what we do and want us there. Since our funding stopped in June 2020 one community used the proceeds of a local pig race to fund us to come back. As grateful as we are, why are at-risk rural communities paying for a service that is funded via large charities inour more affluent cities? Rural Australians pay taxes too but are not receiving equitable investment in mental health.
That is why in 2020, charities like ours asked to be funded properly as part of the new mental health strategy. Without the benefit of lobbyists or sophisticated marketing we provided honest hopeful submissions and genuinely expected that at least 5% of the billions being spent would be focused on the 29% of Australians who live in rural communities.
Our modest request for $2 million a year to reach at risk people across Australia was ignored and letters from politicians reminded us of the millions being spent on hospitals and resources in our cities and regional centres. Our charity is not alone. Many other rural mental health organisations lost some or all their funding and some folded or stopped going where they needed to go.
The responses we received from federal MP’s and ministers included: “Your funding will not be renewed as the drought is over.” “Apply to the rural Primary Health Network service – they might have funds left over” “Doesn’t Beyond Blue/Lifeline/R U OK look after that?”
Many rural charities watched in despair during budget week. We had already received our “no’s” and we could only watch as hundreds of millions were allocated to the city based ‘national’ charities for core funding, project funding and special project funding – hundreds of millions of dollars allocated to a small number of very large organisations. Some of these organisations report over $50 million in cash reserves in their 2020 annual reports. The work they do is important and effective in the communities where they concentrate their efforts. But they don’t go where we go. We know it takes some digging to find those of us that work on the ground but we are there at the bottom of the pile of submissions on desks throughout Canberra.
Rather than give up, we are asking our leaders to allocate a fair percentage of our mental health prevention budget to charities serving the 29% of Australians who live in rural communities.
We need to reach places far from Canberra like the Kimberly where the suicide rate is four times the national average. Places like the Tablelands of Queensland where 55 people lost hope in 2020. Or Wangaratta (VIC) , Maryborough (QLD), West Tamar (TAS), Outback North and the Outback Southern regions (QLD).
Places where the light of this mental health budget is not yet shining any light.
We need our politicians to see beyond the blue coastlines and cities and into high-risk red areas of our country. People need the tools to save themselves and others because some Australians will always be isolated from medical mental health facilities. Van Gogh’s brother Theo held him in his arms as he succumbed to the injuries inflicted in that rural paddock and lamented that he hadn’t recognised the signs of depression in time. We will keep sending our letters and visiting those paddocks and towns because we know this is our best line of defence. But we need our leaders to recognise the signs and invest beyond the blue.
Objective: Since the outbreak of the COVID-19 pandemic, healthcare professionals reported declining numbers of patients admitted with ST-segment myocardial infarction (STEMI) associated with increased in-hospital morbidity and mortality. However, the effect of lockdown on outcomes of STEMI patients admitted during the COVID-19 crisis has not been prospectively evaluated.
Methods: A prospective, observational study on STEMI patients admitted to our tertiary care center during the COVID-19 pandemic was conducted. Outcomes of patients admitted during lockdown were compared to those patients admitted before and after pandemic-related lockdown.
Results: A total of 147 patients were enrolled in our study, including 57 patients in the pre-lockdown group (November 1, 2019 to March 20, 2020), 16 patients in the lockdown group (March 21 to April 19, 2020), and 74 patients in the post-lockdown group (April 20 to September 30, 2020). Patients admitted during lockdown had significantly longer time to first medical contact, longer door-to-needle-time, higher serum troponin T levels, worse left ventricular end-diastolic pressure, and higher need for circulatory support. After a median follow-up of 142 days, survival was significantly worse in STEMI patients of the lockdown group (log-rank: p = 0.0035).
Conclusions: This is the first prospective study on outcomes of STEMI patients admitted during public lockdown amid the COVID-19 pandemic. Our results suggest that lockdown might deteriorate outcomes of STEMI patients. Public health strategies to constrain spread of COVID-19, such as lockdown, have to be accompanied by distinct public instructions to ensure timely medical care in acute diseases such as STEMI.