Time saving, simple and reproducible method to quantify left ventricular function in patients with atrial fibrillation

Introduction

Echocardiography is essential for the management of patients with atrial fibrillation (AF), but current methods are time consuming and lack any evidence of reproducibility.

Purpose

To compare conventional averaging of consecutive beats with an index beat approach, where systolic and diastolic measurements are taken once after two prior beats with a similar RR interval (not more than 60 ms difference).

Methods

Transthoracic echocardiography was performed using a standardized and blinded protocol in patients enrolled into the RAte control Therapy Evaluation in permanent AF randomised controlled trial (RATE-AF; NCT02391337). AF was confirmed in all patients with a preceding 12-lead ECG. A minimum of 30-beat loops were recorded. Left ventricular function was determined using the recommended averaging of 5 and 10 beats and using the index beat method, with observers blinded to clinical details. Complete loops were used to calculate the within-beat coefficient of variation (CV) and intraclass correlation coefficient (ICC) for Simpson's biplane left ventricular ejection fraction (LVEF), global longitudinal strain (GLS) and filling pressure (E/e').

Results

160 patients (median age 75 years (IQR 69–82); 46% female) were included, with median heart rate 100 beats/min (IQR 86–112). For LVEF, the index beat had the lowest CV of 32% compared to 51% for 5 consecutive beats and 53% for 10 consecutive beats (p<0.001). The index beat also had the lowest CV for GLS (26% versus 43% and 42%; p<0.001) and E/e' (25% versus 41% and 41%; p<0.001; see Figure for ICC comparison). Intra-operator reproducibility, assessed by the same operator from two different recordings in 50 patients, was superior for the index beat with GLS bias −0.5 and narrow limits of agreement (−3.6 to 2.6), compared to −1.0 for 10 consecutive beats (−4.0 to 2.0). For inter-operator variability, assessed in 18 random patients, the index beat also showed the smallest bias with narrow confidence intervals (CI). Using a single index beat did not impact on the validity of LVEF, GLS or E/e' measurement when correlated with natriuretic peptides. Index beat analysis substantially shortened analysis time; 35 seconds (95% CI 35 to 39 seconds) for measuring E/e' with the index beat versus 98 seconds (95% CI 92 to 104 seconds) for 10 consecutive beats (see Figure).

Conclusion

Index beat determination of left ventricular function improves reproducibility, saves time and does not compromise validity compared to conventional quantification in patients with heart failure and AF. After independent validation, the index beat method should be adopted into routine clinical practice.

Comparison for measurement of E/e'

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institute of Health Research UK

Artificial intelligence methodology: multi-label classification of abnormal EKG records

Background

Our previous experience with Artificial Intelligence (AI)-conducted EKG characterization displayed outstanding results in fast and reliable identification of Normal EKGs within the International Telemedical System (ITMS)'s massive record repository. By expanding the array of recognizable cardiovascular entities, we upgraded our methodology to accurately discriminate an anomaly amongst a highly complex database of EKG records.

Purpose

To present a feasible AI-guided filter that can accurately discriminate and classify Normal and Abnormal EKG records within a multilabeled cardiologist-annotated EKG database.

Methods

ITMS developed and tested the “One Click”' process, a “Normal/Abnormal” EKG assessing AI algorithm, by incorporating it into their digital EKG reading platform where cardiologists continuously report their findings remotely in real time. To ameliorate the diagnostic range of the algorithm, a separate dataset of 121,641 12-lead EKG records was consolidated from the ITMS database from October 2011 to January 2019. Only de-identified data was used. Preprocessing: The first 2s of each short lead and 9s of the long lead were considered. Limb leads I, II and III; and precordial leads V1, V2, V3, and V5 were used. The mean was removed from each lead. AI models/Classification: Two models were created and tested independently based on the method of EKG acquisition (69,852 records transtelephonic [TTP]; 52,259 mobile transmission [MOB]). Each record is categorized into six disjoint classes based on the most common types of cardiac disorders (Low/null co-occurrence pathologies in these datasets were grouped into analogous groups). Training/Testing: Distribution of both sets per transmission type was performed through a greedy algorithm, which identified multiple diagnoses per EKG record and labeled it separately to the corresponding group, ensuring sufficient samples per class. Detailed class distribution is shown below. An inception convolutional neural network was implemented; “Normal” or “Abnormal” labels were assigned to each EKG record independently and were compared to cardiologists' reports; performance indicators were calculated for each model and group.

Results

MOB model accrued an average accuracy of 86.7%; sensitivity of 90.5%; and specificity of 83.9%. TTP model yielded an average accuracy of 77.2%; sensitivity of 91.1%; and specificity of 69.4% (Lower values were attributed to the “Ventricular Complexes” group, which challenged the algorithm by having a smaller ratio of abnormal exams). Detailed results of each training set are shown below.

Conclusion

Providing an effective and reliable multilabel-capable EKG triaging tool remains a challenging but attainable goal. Continuous systematic enhancement of our AI-driven methodology has led us to satisfactory, yet imperfect results which compel us to further study and improve our efforts to provide a trustworthy cardiologist-friendly triage device.

Funding Acknowledgement

Type of funding source: None

Novel wearable sensor device methodology for STEMI detection

Background

Novel STEMI detection tools using wearable Single Lead EKG methodologies demonstrate vast potential in many clinical scenarios. Recent research suggests that smartwatches and other wearable devices can be repositioned to acquire “new” chest leads that have similar, but not equal, waveforms when compared to traditional precordial leads. Throughout our previous research, only Lead I data had been used to train our Machine Learning (ML) models due to a lack of datasets from these “new” leads. We now propose an innovative methodology to tackle these limitations and compare it with our previous experience.

Purpose

To demonstrate that mathematical vector algebra can reliably transform EKG STEMI databases into different, ML-ready datasets useful to train models with entirely new leads, mainly to be used in the development and training of reliable STEMI detection tools.

Methods

Our previous research has demonstrated that the most accurate (91.2%) ML model was achieved through precordial lead 2 (V2). By definition, V2 corresponds to the difference in electric potential between the Wilson Central Terminal (Wt) and the Chest terminal 2 (C2). To obtain the Wt, at least three electrodes must be used (Right Arm [RA], Left Arm [LA], Left Leg [LL]). Due to practical reasons, we discarded this methodology and worked with Lead I instead, which needs only two body contacts (RA, LA), and provides waveforms that are compatible with the majority of wearable devices (smartwatches, rings, among others). New waveforms (Vn') were obtained by positioning a single lead-capable wearable device (Smartwatch) to chest positions Cn (C1, C2,...,C6) and touching a second electrode with a right-hand finger, which corresponds to the difference in electric potential between RA and the correspondent conventional Vn chest position, respectively. Using vector algebra, we observe that Vn' corresponds to the sum of −aVR + Vn. Vector mathematical analysis was performed for 5,783 STEMI (50%) and 5,784 Not-STEMI (50%) EKG dataset, obtaining their corresponding new precordial leads Vn'. Following this, the ML Heart Attack Detector model was trained with 10,410 EKG (90%) and tested utilizing 1,157 (10%) EKG. Performance metrics were calculated for each new Lead and compared with our Prior Data.

Results

A 1:1 correlation was seen between our previous and current experiments, with Lead V2' performing as the best overall lead with 91.2% Accuracy, 89.6% Sensibility, and 92.9% Specificity. Complete information on prior and new data are provided below.

Conclusions

With the use of this new methodology, we overcame the inherent limitations of using our best Lead (V2) in a single lead approach for STEMI screening. Further prospective data is needed to validate this approach, but it provides a promising blueprint for automated STEMI detection and management triage through the use of wearable devices.

Funding Acknowledgement

Type of funding source: None

Retrospective analysis of EMR database to assess the effectiveness of Saccharomyces boulardii CNCM I-745 in children with acute diarrhoea during routine clinical practice

This study aimed to assess the efficacy of Saccharomyces boulardii, a yeast probiotic, in the management of acute diarrhoeal disorders in the paediatric population in outpatient settings. It was a multicentre retrospective analysis of medical records of children who were treated for acute diarrhoea by routine treatment (oral rehydration solution and zinc) with or without S. boulardii. Overall, 160 children presenting with acute diarrhoea at seven different outpatient paediatric settings were included in the study. Children were divided into two categories based on their treatment with S. boulardii (SB group) or without S. boulardii (Non-SB group). Baseline demographic, anthropometric and clinical variables were compared between the two groups. The median duration of diarrhoea post-treatment was significantly shorter in the S. boulardii group (3 days) than in the non-SB group (4 days). A significant reduction in the frequency of stools was observed post-treatment in the S. boulardii group (1.7 versus 2.5 in the non-SB group). There was a significant weight gain in the S. boulardii group post-treatment (300 g) in comparison with the non-SB group (mean loss of 400 g). This study established the positive role of S. boulardii in the management of acute diarrhoeal diseases in children. Moreover, the S. boulardii probiotic was seen to be effective in diarrhoeal diseases in children with dehydration.

Cardiological Society of India: Document on acute MI care during COVID-19

The unprecedented and rapidly spreading Coronavirus Disease-19 (COVID-19) pandemic has challenged public health care systems globally. Based on worldwide experience, India has initiated a nationwide lockdown to prevent the exponential surge of cases. During COVID-19, management of cardiovascular emergencies like acute Myocardial Infarction (MI) may be compromised. Cardiological Society of India (CSI) has ventured in this moment of crisis to evolve a consensus document for care of acute MI. However, this care should be individualized, based on local expertise and governmental advisories.

Efficacy and safety comparison of combination of 0.04% tretinoin microspheres plus 1% clindamycin versus their monotherapy in patients with acne vulgaris: a phase 3, randomized, double-blind study

Background and objectives: There is an unmet need for topical treatments with good tolerability in management of acne vulgaris. The present study aimed to evaluate efficacy and safety of a novel tretinoin (microsphere, 0.04%) formulation in combination with clindamycin (1%) gel for treatment of acne vulgaris.Materials and methods: This phase 3 randomized, double-blind study included patients with moderate-to-severe acne. Patients were treated with tretinoin (microsphere, 0.04%) + clindamycin (1%) or one of the monotherapies (tretinoin, 0.025%; clindamycin, 1%). Key endpoints included percent change in lesion counts, and improvement in Investigator's Static Global Assessment (ISGA) score.Results: 750 patients were randomized (combination, n = 300; tretinoin and clindamycin, each n = 150). At week 12, reductions in inflammatory (77%), non-inflammatory (71%) and total lesions (73%) were significantly greater with combination treatment versus either monotherapy (p < .03). Proportion of patients rated 'clear' or 'almost clear' with ≥2-grade ISGA improvement was higher with combination (46%) versus monotherapies (p < .02). Adverse events occurred in 20 patients, most were mild-moderate; no deaths or serious adverse events were reported. The discontinuation rates due to adverse events with combination therapy were low (≤1%).Conclusion: The once-daily, microsphere-based formulation was generally tolerable with a positive impact on therapeutic outcomes and patients' compliance. ClinicalTrial Registration No.: CTRI/2014/08/004830.

Diversity of hepatitis C virus infection among HIV-infected people who inject drugs in India

The availability of generic direct acting antivirals (DAAs) for hepatitis C virus (HCV) treatment has prompted many low-and-middle-income countries to launch HCV elimination programs. Because the efficacy of some of these generic DAAs varies by HCV viral subtype, information on subtype distribution can contribute important information to these elimination programs. We conducted a cross-sectional serosurvey to characterize HCV subtype diversity among HIV positive people who inject drugs (PWID) across 14 cities in India. Of 801 HIV positive PWID sampled, 639 tested HCV antibody positive (78.9%). Among 105 samples sequenced, genotype 3 (58.1%) was the most commonly observed followed by genotype 1 (36.2%) and genotype 6 (5.7%). Of the genotype 3 infections, 65% were subtype 3a and 35% were subtype 3b. Of the genotype 1 infections, 94% were subtype 1a and 6% were subtype 1b. All genotype 6 samples were subtype 6n. There was some variability in genotype diversity depending on geographic region and PWID epidemic stage with greater diversity observed in older PWID epidemics. One sequence, HY018, did not cluster with any known reference sequences in phylogenetic analysis. Nearly 80% of HIV infected PWID across India are co-infected with HCV, and subtype prevalence and genetic diversity varied by region and PWID epidemic stage. HCV elimination programs in India will need to consider HCV subtype.

A multiple lines of evidence approach for identifying geologic heterogeneities in conceptual site models for performance assessments

Model-based decision making is commonly used in performance assessments to assure water resource protection for both human health and the environment for hundreds of years into the future. To make decisions regarding aquifer protection against potential contamination, a conceptual site model (CSM) describing the hydrodynamic behavior needs to account for subsurface heterogeneities in sufficient detail. When site-specific data are sparse, larger-scale geologic descriptions are adopted with the consequence of losing small-scale features (at the cm scale) that can control contaminant transport. In this study, a multiple lines of evidence approach is used to construct vadose zone CSMs based on an evaluation of several types of data, including geologic logs, borehole moisture content and concentration data, geophysical spectral gamma logging data, and groundwater concentration data for a tank farm at the Hanford Site in southeastern Washington State. The resulting CSMs of the unsaturated zone represent a synthesis of what is known about flow and transport processes at the site-scale and maintain consistency with knowledge that has been accumulated at the regional scale. Through a process of extensive data analyses, a systematic approach is described to create an evidence base that supports the evaluation and development of CSMs. Numerical models are then used to evaluate the impact that smaller-scale heterogeneities have on contaminant transport through the vadose zone for a performance assessment on waste tank closure. Together, the field data and the numerical experiments suggest that although small-scale features close to source releases can have an impact on horizontal spreading, overall there is a relatively minor impact on transport for the site under study as evaluated by differences in peak fluxes and arrival times for historical leak events, and for potential releases resulting from waste tank closure. Use of alternative CSMs, developed through careful examination of available characterization and monitoring data, provides confidence that geologic heterogeneities do not impact contaminant transport behavior significantly enough to alter the assessment of risk for closure at this site.

2387Recurrent cardiovascular events and mortality in relation to antiplatelet therapy in patients with myocardial infarction without obstructive coronary artery disease (MINOCA)

Background

Approximately 10% of patients presenting with myocardial infarction (MI) do not have obstructive coronary artery disease (MINOCA). The role of antiplatelet therapy and outcomes in this group remain unclear. We assessed prognosis and the effect of an intensified clopidogrel regimen in MINOCA patients.

Methods

We analyzed data from the CURRENT-OASIS 7 trial, which randomized 25,086 patients with acute coronary syndromes (ACS) referred for early intervention to receive either double-dose (600mg day 1; 150mg days 2–7; then 75mg daily) or standard-dose (300mg day 1; then 75mg daily) clopidogrel. We evaluated clinical outcomes at 30-days in patients with versus without obstructive CAD and in relation to standard versus double-dose clopidogrel.

Results

Overall, 23,783 MI patients were included, of which 1,599 (6.7%) had MINOCA. MINOCA patients were younger, more frequently presented with non-ST-segment elevation MI and had fewer comorbidities. Rates of all-cause mortality (0.7% versus 2.4%, p=0.0046), cardiovascular mortality (0.6 versus 2.2%, p=0.0056), repeat MI (0.5% versus 2.3%, p=0.0009) and major bleedings (0.7% versus 2.5%, p=0.0001) were significantly lower among patients with MINOCA versus those with obstructive CAD. Compared with the standard-dose clopidogrel regimen, the double-dose regimen appeared to increase the risk of cardiovascular death, MI or stroke in MINOCA patients (0.8% versus 2.1%, hazard ratio (HR) 2.74, P=0.033). There was no difference in those with obstructive CAD (4.7% versus 4.4%, HR 0.93, P=0.226; P-for-interaction=0.023) (see Figure 1A). Major bleeding did not occur more frequently in MINOCA patients with double- versus standard-dose clopidogrel regimen (0.7% versus 0.6%, (HR 1.16 (95% CI 0.35–3.80), p=0.805), but their rate was higher In MI patients with obstructive CAD (2.7% versus 2.2% (HR 1.26 (95% CI 1.06–1.49), p=0.008) (Figure 1B).

Figure 1A & B

Conclusions

Compared to MI patients with obstructive CAD, patients presenting with MINOCA represent a distinct cohort, which is generally younger, has a higher NSTEMI prevalence and fewer comorbidities. Their risk for adverse events, especially repeat MI, stroke, death, and bleeding, is low (<1%) at 30 days. Applying an intensified clopidogrel regimen in MINOCA patients appears to be related to a higher risk for CV death, MI and stroke. Accordingly, more potent antiplatelet regimens might be harmful among MINOCA patients and should not be administered routinely. Nevertheless, there is a need for more prospective studies evaluating the role of dual antiplatelet therapies in MINOCA patients.

Acknowledgement/Funding

The CURRENT-OASIS 7 trial was sponsored by Sanofi-Aventis and Bristol-Myers Squibb.

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.

A Study about Co-Infection of Fungal Pathogens in Active Tuberculosis Patients

The diagnosis of mycotic lung infection in pulmonary TB patients remains misdiagnosed because of its non-specific clinical manifestations which mimics the symptoms of TB. Physicians have to rely on the investigation but as radiology and pathology cannot probe the appropriate diagnosis, conventional microbiology or PCR testing continue as an essential mode for the diagnosis. In developing country like India PCR is not cost effective. Thus, Direct microscopy by KOH (10%), Gram's staining & Culture remains only option for identification. A three-year cross-sectional study was carried out in the Department of Microbiology, Maharishi Markandeshwar Institute of Medical Science & Research, Mullana, India from August 2015 to August 2018. On 300 LED positive sputum samples collected from previously treated cases of pulmonary TB. Early morning sputum was collected and subjected to KOH 10%, Gram's staining afterwards cultured on Sabouraud Dextrose Agar and species identification was done by LPCB preparation. In 300 LED smear positive samples, the dominant pathogens were C. albicans (43.3%), followed by C. non-albicans (26.7%), A. fumigatus (21.7%) etc. ATT administration for 5-8 months' duration of illness showed highest fungal infection (45%) and maximum growth of fungus was seen in the Autumn season (45%). The co-occurrence of fungi with tubercle bacteria adds fatal consequences thus routine screening is recommended for proper diagnosis and early treatment of mycotic infection in the patients of Pulmonary TB on ATT.

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.

P1928Global telemedicine initiatives for combating ami

Background

Major disparities exist between developed and developing countries in Acute Myocardial Infarction (AMI) outcomes. Telemedicine has emerged as a powerful, cost-efficient, and scalable tool for population-based AMI management. We propose efficient telemedicine protocols as frontline AMI strategies for resource-constrained developing countries.

Purpose

To create a global template of using telemedicine protocols for treating AMI.

Methods

A hub and spoke strategy was utilized for Latin America Telemedicine Infarct Network (LATIN) to expand access in Brazil, Colombia, Mexico, and Argentina. Small clinics and primary care health centers in remote areas were strategically connected with 24/7 primary PCI facilities. Experts at 4 remote sites provided urgent EKG diagnosis and tele-consultation that triggered ambulance dispatch and implementation of standardized AMI protocols.

Results

784,947 patients were screened for AMI at 350 LATIN centers (Brazil 143, Colombia 118, Mexico 82, Argentina 7). With this expanded geographic reach, 8,448 (1.08%) patients were diagnosed with STEMI and 3,911 (46.3%) urgently reperfused, including 3,049 (78%) with Primary PCI. Time to Telemedicine Diagnosis (TTD) was 3 min, tele-accuracy 98.9%, D2B 51 min, and in-hospital mortality 5.2%. Major reasons for non-treatment were insurance denials, lack of ICU beds and chest pain >12 hours.

Conclusions

LATIN demonstrates the feasibility of a population-based and telemedicine guided AMI strategy that can hugely expand access. Telemedicine has important public health implications as a global approach to AMI care in developing countries.