0374 Decline in Habitual Sleep Duration Over 10 Years and Worsening Sleep Disparities: Data From NHIS (2006-2015)

Introduction

Despite claims in the media, evidence that habitual sleep has declined in recent years is scant. Few data sources exist that systematically document sleep duration in a nationally representative sample, in the same way, over several years.

Methods

Data from 10 years of the National Health Interview Survey were used (N=305,555). During all years, habitual sleep duration, age, sex, race/ethnicity, and height/weight were recorded in the same way. Weighted regression analyses examined sleep duration as the outcome, year as linear predictor, and sociodemographics as covariates. Then, interaction terms examined whether the linear change associated with years was differentially experienced by different sociodemographic groups.

Results

The linear trend of sleep duration over the past 10 years is a loss of 0.78 minutes per year (95%CI -0.91,-0.64; p<0.0001). After adjustment for age, sex, race/ethnicity and BMI, this remained relatively unchanged at 0.86 minutes (95%CI -0.99,-0.73; p<0.0001). A year-by-race/ethnicity interaction was observed (p<0.05). In stratified analyses, Non-Hispanic Whites showed a loss of 0.68 minutes per year (95%CI -0.84,-0.52, p<0.0001). This was 1.33 minutes/year in Blacks/African-Americans (95%CI -1.74,-0.92; p<0.0001), 1.57 minutes/year in Mexican-Americans (95%CI -1.98,-1.16; p<0.0001), 0.99 minutes/year in other Hispanics/Latinos (95%CI -1.51,-0.47; p<0.0001), 0.74 minutes/year in Asians (95%CI -1.24,-0.25; p=0.003), and 1.80 minutes/year in American Indians/Alaskan Natives (95%CI -3.57,-0.03, p=0.046).

Conclusion

On average, the US population has lost 47 seconds of nightly sleep per year over a 10-year period, equating to about 4.7 hours of sleep per year, but racial/ethnic groups were impacted differently. Compared to Non-Hispanic Whites, Blacks/African-Americans lost 96% more sleep, Mexicans lost 131% more sleep, other Hispanics/Latinos lost 46% more sleep, Asians lost 9% more sleep, and American Indians lost 165% more sleep. Thus, sleep disparities may be widening.

Support

Dr. Grandner is supported by R01MD011600

0243 Community-Level Daytime Sleepiness and Substance Use: Implications of Sleep Time and Mental Health

Introduction

Daytime sleepiness is associated with impaired functioning and well-being. Those with more sleepiness may turn to illicit substances to overcome these problems. The present study examined whether community-level daytime sleepiness is associated with the likelihood of drug use.

Methods

Data were pulled from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 community adults (age 22–60). Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Use of different substances was assessed with the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). The present analyses examined use within the past month of alcohol, tobacco, cannabis, cocaine, amphetamines, inhalants, sedatives, hallucinogens, and illicit opioids. A separate item assessed caffeine. Ordinal logistic regression analyzed ESS score as a predictor of frequency of substance use adjusted for age, sex, education, and race/ethnicity. Additional models included habitual sleep duration and score on the PHQ9 depression scale.

Results

In sociodemographically-adjusted analyses, ESS score was associated with an increased risk of using tobacco (OR=1.04, p=0.015), cannabis (OR=1.04, p=0.014), cocaine (OR=1.07, p=0.009), amphetamines (OR=1.06, p=0.025), inhalants (OR=1.13, p=0.002), sedatives (OR=1.07, p=0.003), hallucinogens (OR=1.12, p=0.001), and opioids (OR=1.12, p=0.0001). Controlling for sleep duration did not significantly affect these relationships, while controlling for depression made every association non-significant except hallucinogens (OR=1.09, p=0.040).

Conclusion

Daytime sleepiness was associated with increased use of nearly all drug categories, but not alcohol or caffeine. Public consumption of alcohol and caffeine might be sufficiently common that the presence of their use cannot be adequately associated with sleepiness. Moreover, the increased frequency of drug use with sleepiness is not linked to sleep deprivation but may reflect emotional distress.

Support

This work was supported by a grant from Jazz Pharmaceuticals

Dr. Grandner is supported by R01MD011600

0232 Impact of Mental Health on 10-Year Trends in Habitual Sleep Duration

Introduction

Public health efforts aimed at reducing the decline in habitual sleep duration have not been successful. It is possible that this decline is differentially experienced relative to individuals’ mental health status. This would further support the need to focus on mental health as a strategy for improving sleep in the general population.

Methods

We examined 10 years of the National Health Interview Survey data (N=305,555). During all years, habitual sleep duration, age, sex, race/ethnicity, and height and weight (used to compute body mass index) were recorded in the same way. In addition, depressed mood in the past 30 days was evaluated (coded as none, mild, moderate, or severe). Weighted regression analyses examined sleep duration as an outcome, year and depressed mood as predictors, and sociodemographics as covariates. A year-by-depressed mood interaction was computed, and analyses were stratified by group.

Results

There was a significant year-by-depression interaction on linear change in sleep duration over the 10 year period (p=0.0001). Analyses were then stratified by depressed mood. In adjusted analyses, individuals with no depressed mood lost an average of 0.68 minutes of sleep per year (95%CI -0.82,-0.55; p<0.0001). Among those with mild depression, this was 7% higher, at 0.73 minutes (95%CI -1.13,-0.33; p<0.0001). Among those with moderate depressed mood, this was 154% higher, at 1.73 minutes lost per year (95%CI -2.31,-1.16; p<0.0001). Among those with severe depressed mood, this was 351% higher, at 3.07 minutes per year (95%CI -4.22,-1.92; p<0.0001).

Conclusion

The 10-year linear decline in habitual sleep duration seems to depend on mental health status. Individuals with better mental health lose less sleep over time, relative to those with worse mental health. This highlights the importance of mental health as a possible avenue for improving sleep health in the population.

Support

Dr. Grandner is supported by R01MD011600

Voltage Imaging of Cardiac Cells and Tissue Using the Genetically Encoded Voltage Sensor Archon1

Precise measurement of action potentials (APs) is needed to observe electrical activity and cellular communication within cardiac tissue. Voltage-sensitive dyes (VSDs) are traditionally used to measure cardiac APs; however, they require acute chemical addition that prevents chronic imaging. Genetically encoded voltage indicators (GEVIs) enable long-term studies of APs without the need of chemical additions, but current GEVIs used in cardiac tissue exhibit poor kinetics and/or low signal to noise (SNR). Here, we demonstrate the use of Archon1, a recently developed GEVI, in hiPSC-derived cardiomyocytes (CMs). When expressed in CMs, Archon1 demonstrated fast kinetics comparable with patch-clamp electrophysiology and high SNR significantly greater than the VSD Di-8-ANEPPS. Additionally, Archon1 enabled monitoring of APs across multiple cells simultaneously in 3D cardiac tissues. These results highlight Archon1's capability to investigate the electrical activity of CMs in a variety of applications and its potential to probe functionally complex in vitro models, as well as in vivo systems.

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Genetic sensor Archon1 reports membrane voltage in hiPSC-derived cardiomyocytes

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Archon1 monitors action potentials in 2D and 3D cardiac tissue with high sensitivity

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Archon1 repeatedly monitored voltage in the same cells and over extended time periods

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Voltage dynamics of multiple cells were recorded simultaneously with Archon1

P1709 Left atrial strain: a novel prognostic marker in patients with chronic kidney disease

Funding Acknowledgements

Nil

Background and objectives

Patients with chronic kidney disease (CKD) are at increased risk of adverse cardiovascular events that are underestimated by traditional risk stratification algorithms. We sought to determine clinical and echocardiographic predictors of adverse outcomes in CKD patients.

Methods

Stage 3 and 4 CKD patients without previous cardiac disease underwent a comprehensive transthoracic and stress echocardiogram, with left ventricular (LV) and left atrial (LA) strain analysis. Participants were followed for the primary end point of a composite of all-cause death and major adverse cardiovascular events (MACE). The secondary end point was a composite of cardiovascular death and MACE.

Results

243 patients (male 63%; mean age 59.2 ± 14.4 years) were followed for a median of 3.9 ± 2.7 years. 69 patients met the primary endpoint and 58 the secondary end point. Age (p < 0.01), history of diabetes mellitus (p < 0.01), indexed LV mass(LVMI) (p < 0.01), LV global longitudinal strain(GLS) (p < 0.01), indexed LA volume(LAVI) (p < 0.01), E/e’ ratio (p < 0.01) and LA strain (LAs) (p < 0.01) were independent predictors of death and MACE. On Cox proportional hazards regression analysis, LAs (p < 0.01) was the only independent predictor for the primary end point in a model accounting for age, diabetes mellitus, LVMI, LVGLS, E/e’ and LAVI. LAs remained an independent predictor for the secondary end point.

Conclusions

LAs is an independent predictor of death and MACE in CKD patients, in whom the predominant cardiac abnormality is diastolic impairment. LAs is a prognostic biomarker, reflecting alterations in diastolic function in CKD.

Abstract P1709 Figure. Kaplan Meier curve of LAs

1679 E/e predicts exercise capacity and adverse cardiovascular outcomes in patients with chronic kidney disease

Funding Acknowledgements

Nil

OnBehalf

NA

Background

Patients with chronic kidney disease(CKD) have reduced physical fitness that contributes to the disproportionately elevated risk of cardiovascular disease in this population. Our aim was to assess the association between E/e’ and exercise capacity in CKD patients and the prognostic role of E/e’.

Methods

Patients with Stage 3/4 CKD, without previous cardiac disease were prospectively recruited. Recruited patients underwent transthoracic echocardiogram and exercise stress echocardiogram with assessment of exercise E/e’. Patients were compared, one to one, to age, gender and risk factor matched controls and were followed annually for 5 years for cardiovascular death and major adverse cardiovascular events (MACE). Exercise capacity was assessed as metabolic equivalents (METs) with reduced exercise capacity defined as METS of ≤7. Raised exercise E/e’ was defined as exercise E/average e’ of >13.

Results

156 CKD patients (62.8 ± 10.6 yrs, male 62%) were compared to 156 matched controls. CKD patients had higher rates of anemia (p < 0.01), larger left ventricular indexed mass (p < 0.01), larger LAVI (p < 0.01) and higher resting (p < 0.01) and exercise E/e’ (p < 0.01). Overall, CKD patients achieved lower METs (p < 0.01) with exercise and a greater proportion of CKD patients had METs ≤7 (p < 0.01). Receiver operating curves (Figure1) showed exercise E/e’ (AUC 0.89, CI 0.84-0.95, p < 0.01) to be the strongest predictor of reduced exercise capacity in CKD patients. Exercise E/e’ of >13 was also associated with higher rates of cardiovascular death and MACE amongst CKD patients.

Conclusion

Exercise E/e’ is a strong predictor of exercise capacity amongst CKD patients, who commonly have reduced exercise capacity presumably consequent to diastolic dysfunction. Raised exercise E/e’ in CKD patients is predictor of cardiovascular death and MACE.

Abstract 1679 Figure.

P246 Exercise E/e refines diagnosis of diastolic impairment in patients with chronic kidney disease

Background

Diastolic dysfunction is common in chronic kidney disease (CKD) patients and is associated with reduced exercise capacity. The ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/e’) correlates with invasively measured left ventricular pressures, both at rest and during exercise. The aim of our study was to evaluate the relationship of resting and exercise E/e’ to exercise capacity in patients with stage 3 and 4 CKD.

Methods

CKD patients were prospectively recruited. All patients were required to be in sinus rhythm, without previous cardiac disease and with normal LVEF. The E/e’ ratio was assessed at rest and patients were stratified into 3 groups based on conventional clinical classifications (normal: ≤8, indeterminate: 8-14, raised: >14). Patients then underwent a symptom limited exercise stress echocardiogram whereby patients with resting E/e’ <14 additionally had post exercise E/e’ measured. Exercise capacity was assessed as metabolic equivalents (METs). A raised exercise E/e’ was defined as exercise E/e’ of ≥13. Lastly, we grouped patients based on abnormalities of baseline and exercise E/e’ (resting E/e’<14 & exercise E/e’<13, resting E/e’<14 & exercise E/e ≥13, resting E/e’ ≥14) and the relationship to METs achieved was evaluated.

Results

The cohort consisted of 156 CKD patients (62.8 ± 10.6 yrs, male 62%). 85% had hypertension and 39% had diabetes mellitus. 52/156 (34%) CKD patients had resting E/e’ of ≥14. Of the 104/156 (66%) CKD patients with normal/indeterminate resting E/e’, 66/104(63%) had normal exercise E/e’ <13 whilst 38/104(37%) had exercise E/e’ ≥13. Patients with resting E/e" & raised exercise E/e’ ≥13 achieved significantly lower METs compared to CKD patients with resting E/e’ <14 & exercise E/e’ <13 (5.0 ± 2.2 vs 8.8 ± 2.5, p< 0.01). Of note, METs achieved in this group were comparable to that achieved by CKD patients with raised resting E/e’ ≥ 14 (5.0 ± 2.2 METs vs 5.3 ± 2.6 METs, p = 0.55).

Conclusion

In CKD patients with normal/indeterminate resting E/e’, there is utility in performing exercise E/e’, which was increased in 37% of patients. Raised exercise E/e’ is associated with poorer exercise capacity and is a potential tool to refine diastolic abnormalities in patients with CKD.

Group Total (n,%) METs Achieved Exercise Duration (minutes) Resting E/e’ ≤ 14 & Exercise E/e’ ≤ 13 66/156 (42%) 8.8 ± 2.5 8.4 ± 3.1 Resting E/e’ ≤ 14 & Exercise E/e’ ≥ 13 38/156 (24%) 5.0 ± 2.2 6.0 ± 3.1 Resting E/e’ ≥ 14 52/156 (34%) 5.3 ± 2.6 5.6 ± 2.2

P6422Physician vs machine: an innovative ST-elevation myocardial infarction pathway through artificial intelligence

Background

The diagnosis of ST-Elevation Myocardial Infarction (STEMI) has traditionally relied on a cardiologist's interpretation of an Electrocardiogram (EKG). This cumbersome process is costly, inefficient and out of date. Artificial Intelligence (AI) -guided algorithms can provide point-of-care, accurate STEMI diagnosis that will facilitate STEMI management.

Purpose

To demonstrate the feasibility of an automated AI-guided EKG analysis for STEMI diagnosis.

Methods

An observational, retrospective, case-control study. Sample: 8,511 EKG cardiologist-annotated records, including 4,255 STEMI cases. Records excluded patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (90,592 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

The model achieved an accuracy of 96.5%, with a sensitivity of 96.3%, and a specificity of 96.8%.

Conclusion(s)

1) AI-guided interpretation of the EKG can reliably diagnose STEMI; 2) AI algorithms can be incorporated into ambulance systems for pre-hospital diagnosis, single page activation, emergency department bypass, facilitating more efficient STEMI pathways.

P1464Adoption of feedback to validate a machine learning model for single lead STEMI detection

Background

We have explored the performance of a single lead EKG with Artificial Intelligence (AI) based algorithms in STEMI diagnosis, thus far lead V2 has yielded the best results. Anticipating the performance of the LUMENGT-AI model, we designed a feedback strategy with healthcare centers to expand the validation of our work.

Purpose

To create a pragmatic alternative to the existing gold standard, a 12-lead EKG, for STEMI diagnosis.

Methods

An observational, retrospective, case-control study. Sample: 2,543 exclusively STEMI (anterior, inferior and lateral wall) diagnosis, EKG records. Feedback: From healthcare centers, confirming STEMI diagnosis and location, was obtained (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmaco invasive therapy or coronary artery bypass surgery). Records excluded other patient and medical information. Sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using the wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “STEMI” and “Not-STEMI” classes were considered for each heartbeat per lead; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

V2 was the most precise lead with an Accuracy of 93.6%, a Sensitivity of 89%, and a Specificity of 94.7%.

Conclusions

The strategic adoption of feedback from healthcare centers provided strong validation of our model. The results of AI-augmented, single lead EKG are encouraging. We anticipate that this approach will become a promising methodology in STEMI detection.

P1524Women are from venus and face barriers for AMI treatment

Background

The Latin American Telemedicine Infarct Network (LATIN), a program for managing population-based AMI care, has utilized an innovative hub-spoke strategy and remote guidance to expand medical access in remote parts of Brazil, Colombia, Mexico, and Argentina. Based on the Global Lumen Organization for Women (GLOW) project, that has previously demonstrated gender inequalities and worse outcomes for women who undergo Primary PCI, our research explored the management of female patients in our network.

Purpose

To demonstrate gender disparities in a telemedicine-guided, population based AMI program.

Methods

Meta-analysis of >750,000 patients within LATIN. Telemedicine was employed as a screening tool to accurately diagnose AMI. According to the duration of chest pain and transfer time, AMI patients were triaged into guidelines-based pathways of thrombolysis, pharmaco-invasive management or Primary PCI. Resource allocation was identical for men and women.

Results

Data from 784,395 screened patients reveals broad gender disparities. The critical results yielded that female patients have a lower prevalence of STEMI diagnosis and treatment regardless of the technique – PCI, pharmaco-invasive or CABG (p<0.01). These findings assume more significance given the fact that more women than men, were screened through the Telemedicine pathway.

Conclusions

LATIN gender data signals the urgency to demand better AMI care for women.

P6421Can cardiologists rely on artificial intelligence to identify the culprit vessel in STEMI?

Background

The importance of culprit lesion identification is critical for risk stratification of a patient with an ST-Elevation Myocardial Infarction (STEMI). The aforementioned provide patients with a more elaborated strategy of management and treatment either they are treated with PCI or less invasive techniques such as thrombolysis. We report a novel approach that employs AI-guided electrocardiogram (EKG) algorithms for rapid and accurate identification of the culprit STEMI vessel.

Purpose

To create an innovative, machine learning tool for a more effective risk stratification of STEMI patients.

Methods

An observational, retrospective, case-control study. Sample: 2,542 exclusively STEMI diagnosis EKG records that included post discharge feedback from healthcare centers, confirming diagnosis and culprit vessel (Left Main Coronary Artery [LMCA]; Left Anterior Descending [LAD]; Right Coronary Artery [RCA]; Left Circumflex Artery [LCX]; Saphenous Vein Graft [SVG]). Records excluded other patient and medical information. The sample was derived from the private International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes using a wavelet system, segmentation of each EKG into individual heartbeats (27,125 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented; “LCMA”, “LAD”, “LCX”, “RCA”, “SVG”, and “No Information” classes were considered for each heartbeat; individual probabilities were aggregated to generate the final label for each record. Training & Testing: 90% and 10% of the sample was used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVidia GTX 1070 GPU, 8GB RAM.

Results

Global Accuracy: 79.4%; LAD: Sensitivity 86.2%; Specificity 84.8%. RCA: Sensitivity 85.7%; Specificity 83.7%. LCX: Sensitivity 43.5%; Specificity 96.9%.

Conclusions

Coupling an AI-augmented algorithm and 12-lead EKG provides encouraging results for STEMI culprit vessel localization. Overall, risk stratification is possible for individual lesions located in the LAD and RCA. However, our approach yielded uncertain results in the LCX territory. We plan to continue to exploring variables for improvement of our results.

P5241Balancing limited resources, infra-structure deficits & cultural differences in sustaining the growth of LATIN telemedicine program

Background

In resource-constrained nations, population-based AMI coverage is daunting. Telemedicine can transform with efficient, cost-effective and scalable programs. We present our data with screening >780,000 patients with innovative hub and spoke strategies.

Purpose

Scientifically pristine protocols, rigorous training, unflinching quality assurance, technology upgrades and education of broad stakeholders are essential attributes for creating population-based AMI programs.

Methods

Latin America Telemedicine Infarct Network (LATIN) required methodical groundwork during a 12-month pilot prior to its formal launch and sustenance for 5 years. It involved scrupulous site selection, technology, and telemedicine optimization and system-wide process metrics. Spokes are the LATIN nucleus and require constant (3-T) training: Triage, Telemedicine, and Transportation. Plus, a mandatory deconstruct of their role in LATIN, of urgent transfer and desist non-critical care. Telemedicine requires constant upgrading of platform, tele-equipment and cloud computing. Ambulance availability is a constant challenge as is the battle with payers. Data entry has required meticulous training and oversight. Strict QA processes have monitored critical metrics: Spokes (Door In Door Out, DIDO and Transport Times); Hubs (Door to Balloon Times, D2B); Telemedicine Platform (Time to Telemedicine Diagnosis, TTD).

Results

Linear growth is observed in the number of sites and telemedicine screenings with simultaneous and sustained improvements in D2B and TTD. 784,395 patients were screened at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). With expanded reach, 8,440 (1.08%) patients were diagnosed and 3,924 (46.5%) urgently reperfused, including 3,048 (77.7%) with PCI. Time to Telemedicine Diagnosis (TTD) was 3 min, tele-accuracy 98.9%, D2B 51 min and in-hospital morality 5.2%. Major reasons for non-treatment were insurance, lack of ICU beds and delayed presentation.

Conclusions

As other regions of the world develop large, population-based AMI management initiatives, LATIN can provide important lessons in the sustainability of these processes.