COVID-19 infection in patients with intestinal failure: UK experience

BACKGROUND

The direct effect of the coronavirus disease 2019 (COVID-19) pandemic on patients with intestinal failure (IF) has not been described.

METHODS

We conducted a nationwide study of UK IF centers to evaluate the infection rates, presentations, and outcomes in patients with types 2 and 3 IF.

RESULTS

A total of 45 patients with IF contracted COVID-19 between March and August 2020; this included 26 of 2191 (1.2%) home parenteral nutrition (HPN)-dependent adults and 19 of 298 (6.4%) adults hospitalized with type 2 IF. The proportion of patients receiving nursing care for HPN administration was higher in those with community-acquired COVID-19 (66.7%) than the proportion in the entire HPN cohort (26.1%; P < .01). Two HPN-dependent and 1 hospitalized patient with type 2 IF died as a direct consequence of the virus (6.7% of 45 patients with types 2 or 3 infected).

CONCLUSION

This is the first study to describe the outcomes of COVID-19 in a large cohort of patients requiring long-term PN. Methods to reduce hospital and community nosocomial spread would likely be beneficial.

The Lipiodol Uterine Bathing Effect to Improve Fertility May Include Uterine Natural Killer Cell Up-regulation in the Endometrium

BACKGROUND: Lipiodol has a dramatic short term fertility enhancing effect for women with endometriosis. Microarray studies have shown transcriptomic regulation of molecular markers of endometrial inflammation, most notably a consistent down-regulation of endometrial osteopontin. We further explored the endometrial bathing effect of lipiodol on leukocyte expression in endometrium.

METHODS: A cohort of four women, nested within a randomised trial of twelve women assessing the lipiodol uterine bathing effect, was studied as an ‘own control’ group, with their mid-luteal endometrium assessed before and after endometrial lipiodol exposure. Pipelle endometrial sampling allowed endometrial assessment by immunochemistry. Endometrial tissue samples were assessed by immunochemistry for total CD45+ leukocytes, CD68+ macrophages, CD3+ T-cells and CD56+ uterine natural killer cells.

RESULTS: There was a statistically significant increase in the mean density of uterine natural killer cells in the endometrium of women post-lipiodol. No other significant differences were found in the mean densities of all leukocytes, macrophages or T cells in the endometrium of women post-lipiodol.

CONCLUSIONS: These preliminary data further support the concept of a uterine bathing effect of lipiodol. Whether the increase in the mean density of uterine natural killer cells in the endometrium might contribute to an improvement in endometrial receptivity to embryo implantation merits further investigation.

Functional role of iNOS-Rac2 interaction in neutrophil extracellular traps (NETs) induced cytotoxicity in sepsis

BACKGROUND

Recent reports from this lab have demonstrated a higher incidence of NETs, nitrosative, as well as oxidative stress, and have a direct correlation with the severity of sepsis and organ damage. However, the mechanistic perspective of NETs induced organ damage has not been understood at the cellular and molecular level. Interaction of inducible nitric oxide synthase (iNOS) with Rac2 in regulating reactive oxygen species (ROS) and reactive nitrogen species (RNS) generation and its implications in microbial killing has been reported. This study was, therefore, undertaken in neutrophils of sepsis patients to investigate the functional importance of iNOS-Rac2 interaction in ROS/ RNS, peroxynitrite generation, NETs generation, and NETs mediated cell death.

METHODS

The study was conducted on 100 patients with sepsis and 50 healthy volunteers. Interaction between iNOS and Rac2 was performed using co-immunoprecipitation and co-immunolabeling assay. Free radicals involving ROS and RNS were evaluated using cytochrome c reduction assay. NETs formation was evaluated by fluorescence microscopy. The cytotoxic effect of NETs was assessed on lung carcinoma cell line (A549) using colorimetric Alamar blue assay.

RESULTS

Enhanced interaction between iNOS and Rac2 was found in sepsis neutrophils in comparison with control. This was accompanied by an increased level of superoxide (O2.-), nitric oxide (NO), and peroxynitrite (ONOO-) which were decreased in the presence of NAC, DPI, and 1400 W, signifying the role of iNOS-Rac2 interaction. Enhanced NETs release from activated sepsis neutrophils were abrogated in the presence of DPI. NETs from sepsis neutrophils exert a cytotoxic effect on lung epithelial cells (A549) in a concentration-dependent manner.

CONCLUSION

Our findings exhibit the functional role of iNOS-Rac2 interaction in ROS/RNS, peroxynitrite generation, NETs generation, and NETs mediated cell death.

Individual-oocyte transcriptomic analysis shows that genotoxic chemotherapy depletes human primordial follicle reserve in vivo by triggering proapoptotic pathways without growth activation

Gonadotoxic chemotherapeutics, such as cyclophosphamide, can cause early menopause and infertility in women. Earlier histological studies showed ovarian reserve depletion via severe DNA damage and apoptosis, but others suggested activation of PI3K/PTEN/Akt pathway and follicle ‘burn-out’ as a cause. Using a human ovarian xenograft model, we performed single-cell RNA-sequencing on laser-captured individual primordial follicle oocytes 12 h after a single cyclophosphamide injection to determine the mechanisms of acute follicle loss after gonadotoxic chemotherapy. RNA-sequencing showed 190 differentially expressed genes between the cyclophosphamide- and vehicle-exposed oocytes. Ingenuity Pathway Analysis predicted a significant decrease in the expression of anti-apoptotic pro-Akt PECAM1 (p = 2.13E-09), IKBKE (p = 0.0001), and ANGPT1 (p = 0.003), and reduced activation of PI3K/PTEN/Akt after cyclophosphamide. The qRT-PCR and immunostaining confirmed that in primordial follicle oocytes, cyclophosphamide did not change the expressions of Akt (p = 0.9), rpS6 (p = 0.3), Foxo3a (p = 0.12) and anti-apoptotic Bcl2 (p = 0.17), nor affect their phosphorylation status. There was significantly increased DNA damage by γH2AX (p = 0.0002) and apoptosis by active-caspase-3 (p = 0.0001) staining in the primordial follicles and no change in the growing follicles 12 h after chemotherapy. These data support that the mechanism of acute follicle loss by cyclophosphamide is via apoptosis, rather than growth activation of primordial follicle oocytes in the human ovary.

Non-communicable diseases among adolescents: current status, determinants, interventions and policies

BACKGROUND

Addressing non-communicable disease (NCDs) is a global priority in the Sustainable Development Goals, especially for adolescents. However, existing literature on NCD burden, risk factors and determinants, and effective interventions and policies for targeting these diseases in adolescents, is limited. This study develops an evidence-based conceptual framework, and highlights pathways between risk factors and interventions to NCD development during adolescence (ages 10-19 years) and continuing into adulthood. Additionally, the epidemiologic profile of key NCD risk factors and outcomes among adolescents and preventative NCD policies/laws/legislations are examined, and a multivariable analysis is conducted to explore the determinants of NCDs among adolescents and adults.

METHODS

We reviewed literature to develop an adolescent-specific conceptual framework for NCDs. Global data repositories were searched from Jan-July 2018 for data on NCD-related risk factors, outcomes, and policy data for 194 countries from 1990 to 2016. Disability-Adjusted Life Years were used to assess disease burden. A hierarchical modeling approach and ordinary least squares regression was used to explore the basic and underlying causes of NCD burden.

RESULTS

Mental health disorders are the most common NCDs found in adolescents. Adverse behaviours and lifestyle factors, specifically smoking, alcohol and drug use, poor diet and metabolic syndrome, are key risk factors for NCD development in adolescence. Across countries, laws and policies for preventing NCD-related risk factors exist, however those targeting contraceptive use, drug harm reduction, mental health and nutrition are generally limited. Many effective interventions for NCD prevention exist but must be implemented at scale through multisectoral action utilizing diverse delivery mechanisms. Multivariable analyses showed that structural/macro, community and household factors have significant associations with NCD burden among adolescents and adults.

CONCLUSIONS

Multi-sectoral efforts are needed to target NCD risk factors among adolescents to mitigate disease burden and adverse outcomes in adulthood. Findings could guide policy and programming to reduce NCD burden in the sustainable development era.

Abnormal Levels of Metal Micronutrients and Autism Spectrum Disorder: A Perspective Review

The aim of the present review is to summarize the prevalence of abnormal levels of various metal micronutrients including copper (Cu), iron (Fe), magnesium (Mg), zinc (Zn), and selenium (Se) in Autism Spectrum Disorder (ASD) using hair, nail and serum samples. A correlation of selected abnormal metal ions with known neurodevelopmental processes using Gene Ontology (GO) term was also conducted. Data included in this review are derived from ASD clinical studies performed globally. Metal ion disparity data is also analyzed and discussed based on gender (Male/Female) to establish any gender dependent correlation. Finally, a rational perspective and possible path to better understand the role of metal micronutrients in ASD is suggested.

Prospective correlation of the radiological, surgical and pathological findings in patients undergoing cytoreductive surgery for colorectal peritoneal metastases: implications for the preoperative estimation of the peritoneal cancer index

AIM

The peritoneal cancer index (PCI) is one of the strongest prognostic factors in patients undergoing cytoreductive surgery (CRS) for colorectal peritoneal metastases. Using pathological evaluation, however, the disease extent differs in a large proportion of patients. Our aim was to study the correlation between the radiological (rPCI), surgical (sPCI) and pathological (pPCI) PCI in order to determine factors affecting the discordance between these indices and their potential therapeutic implications.

METHOD

From July 2018 to December 2019, 128 patients were included in this study. The radiological, pathological and surgical findings were compared. A protocol for pathological evaluation was followed at all centres.

RESULTS

All patients underwent a CT scan and 102 (79.6%) had a peritoneal MRI. The rPCI was the same as the sPCI in 81 (63.2%) patients and the pPCI in 93 (72.6%). Concordance was significantly lower for moderate-volume (sPCI 13-20) and high-volume (sPCI > 20) disease than for low-volume disease (sPCI 0-12) (P < 0.001 for sPCI; P = 0.001 for pPCI). The accuracy of imaging in predicting presence/absence of disease upon pathological evaluation ranged from 63% to 97% in the different regions of the PCI. The pPCI concurred with the sPCI in 86 (68.8%) patients. Of the nine patients with sPCI > 20, the pPCI was less than 20 in six.

CONCLUSION

The rPCI and sPCI both concurred with pPCI in approximately two thirds of patients. Preoperative evaluation should focus on the range in which the sPCI lies and not its absolute value. Radiological evaluation did not overestimate sPCI in any patient with high/moderate-volume disease. The benefit of CRS in patients with a high r/sPCI (> 20) who respond to systemic therapies should be prospectively evaluated.

Engaged with the heart – the EKG ring STEMI detector

Background

Our previous experience in telemedicine-centered STEMI management networks has shown delayed presentations as one of the most relevant factors in the reduction of Symptom-to-Baloon times. In order to further improve outcomes, we delved into the applications of mathematical vector algebra and engineering to incorporate an innovative Artificial Intelligence-guided Single Lead EKG methodology into a wearable ring to provide a self-administered alternative to reliable and expedite STEMI screening.

Purpose

To provide preliminary results of the application of ultra-wearable technology in a ring for accurate STEMI detection.

Methods

Our present work was done in two steps – 1) Applying mathematical vector algebra to construct an accurate and practical AI-guided Single Lead EKG algorithm for STEMI detection compatible with wearable devices, and 2) To engineer this algorithm into a wearable ring for quick and reliable STEMI detection. Throughout our first step, we provided a group of new lead waveforms (Vn') by positioning a single lead-capable wearable device into the chest positions Cn (C1, C2,..., C6) while touching the second electrode with a right-hand finger in the same device, all of which corresponded to the difference in electric potential between Right Arm (RA) and the correspondent conventional precordial Vn chest position. By using vector algebra, we recognized Vn' as the sum of (-aVR + Vn). Vector mathematical analysis was performed for 5,783 STEMI (50%) and 5,784 Not-STEMI (50%) EKG from a proprietary dataset, obtaining their corresponding new Vn' precordial leads. Finally, the AI-guided STEMI detector model was trained with 10,410 EKG records (90%) and tested with 1,157 EKG records (10%). Performance metrics were calculated to determine best new Lead for STEMI detection. In the second step, we engineered this methodology into a wearable ring device. When a patient presents chest discomfort or oppression, the most common reaction is to move the hands towards the chest. By mimicking this behavior and having our EKG-capable ring technology on the right hand, we replicate our methodology by positioning said ring to chest positions Cn to register an EKG trace of new Vn' precordial leads and calculated performance metrics to evaluate the correlation with previous experiment.

Results

Test results shows Lead V2' as the best overall lead in detecting STEMI with 91.2% Accuracy, 89.6% Sensibility, and 92.9% Specificity. These results were reproduced with both methodologies.

Conclusions

Preliminary outcomes of the implementation of our innovative Single Lead EKG methodology into an ultra-portable ring yielded promising results. Prospective studies will be needed to further validate this neoteric methodology for STEMI detection, nevertheless, we envision the potential future applications of this technology in the clinical setting, particularly with swift screening and activation of remote STEMI management networks.

Funding Acknowledgement

Type of funding source: None

Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?

Background

Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label.

Purpose

To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed.

Results

On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG).

Conclusions

The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue.

Funding Acknowledgement

Type of funding source: None

Enhancing AI-guided STEMI detection algorithms by incorporating higher quality fiduciary EKG elements

Background

As EKG interpretation paradigms to a physician-free milieu, accumulating massive quantities of distilled pre-processed data becomes a must for machine learning techniques. In our pursuit of reducing ischemic times in STEMI management, we have improved our Artificial Intelligence (AI)-guided diagnostic tool by following a three-step approach: 1) Increase accuracy by adding larger clusters of data. 2) Increase the breadth of EKG classifications to provide more precise feedback and further refine the inputs which ultimately reflects in better and more accurate outputs. 3) Improving the algorithms' ability to discern between cardiovascular entities reflected in the EKG records.

Purpose

To bolster our algorithm's accuracy and reliability for electrocardiographic STEMI recognition.

Methods

Dataset: A total of 7,286 12-lead EKG records of 10-seconds length with a sampling frequency of 500 Hz obtained from Latin America Telemedicine Infarct Network from April 2014 to December 2019. This included the following balanced classes: angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Labels of each record were manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. Order 5 digital low pass filters with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: Determined classes were “STEMI” and “Not-STEMI” (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10, respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original complete dataset of unconfirmed STEMI. Performance indicators (accuracy, sensitivity, and specificity) were calculated for each model and results were compared with our previous findings from past experiments.

Results

Complete STEMI data: Accuracy: 95.9% Sensitivity: 95.7% Specificity: 96.5%; Confirmed STEMI: Accuracy: 98.1% Sensitivity: 98.1% Specificity: 98.1%; Prior Data obtained in our previous experiments are shown below for comparison.

Conclusion(s)

After the addition of clustered pre-processed data, all performance indicators for STEMI detection increased considerably between both Confirmed STEMI datasets. On the other hand, the Complete STEMI dataset kept a strong and steady set of performance metrics when compared with past results. These findings not only validate the consistency and reliability of our algorithm but also connotes the importance of creating a pristine dataset for this and any other AI-derived medical tools.

Funding Acknowledgement

Type of funding source: None

Cost-effectiveness of ticagrelor in patients with type 2 diabetes and coronary artery disease with a history of PCI: an economic evaluation of THEMIS-PCI using a Swedish healthcare perpective

Background

The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS) evaluated ticagrelor compared to placebo for the prevention of myocardial infarction (MI), stroke and cardiovascular (CV) death in 19 220 patients with type 2 diabetes (T2DM) and stable coronary artery disease (CAD) with no prior myocardial infarction (MI) or stroke. THEMIS-PCI was a pre-specified subgroup of 11 154 patients who had a history of percutaneous coronary intervention (PCI) when entering the study. In THEMIS, ticagrelor reduced CV death, MI or stroke, although with an increase in major bleeding compared to aspirin alone, and there was a significant interaction between a prior history of PCI and the net benefit of ticagrelor. In the THEMIS-PCI population, ticagrelor plus aspirin provided a favourable net clinical benefit with a significant 15% reduction in all-cause death, MI, stroke, fatal bleed, or intracranial haemorrhage.

Objective

The objective of this analysis was to estimate the cost-effectiveness of ticagrelor for the prevention of CV events based on the results of the THEMIS-PCI population using a lifetime horizon from a Swedish healthcare perspective.

Methods

A lifetime Markov state transition model was developed with health states aligned to the THEMIS trial endpoints. Health state transitions were informed by parametric survival equations fitted to patient level data from THEMIS-PCI population. Treatment discontinuation rates were informed by the THEMIS-PCI population, with all patients assumed to discontinue treatment with ticagrelor after four years. The incidence of bleeding and dyspnoea were modelled as adverse events. Costs (2019 Euros) and utility data were derived from the published literature and the THEMIS-PCI population, respectively, and discounted at 3.0% annually. Probabilistic (PSA) and deterministic sensitivity analysis (DSA) were conducted to quantify uncertainty of key input parameters.

Results

Treatment with ticagrelor plus aspirin over four years resulted in estimated Quality Adjusted Life Year (QALY) gains of 0.09 at an incremental cost of €1,891 compared to aspirin alone. The estimated incremental cost-effectiveness ratio (ICER) was €19,959/QALY. PSA indicated that ticagrelor was cost-effective in 93% of simulations using a willingness-to-pay threshold of €47,000/QALY and DSA showed that cost-effectiveness was robust to changes in key input parameters (ICER range: €16,504 to €25,012/QALY).

Conclusion

Based on the results of the THEMIS trial, dual antiplatelet therapy with ticagrelor plus aspirin is likely to be a cost-effective treatment compared with aspirin alone for the prevention of CV events in patients with T2DM and CAD with a history of PCI.

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): AstraZeneca

Waddling beyond door to balloon times and impinging true ischemic times with artificial intelligence-guided single lead EKG for STEMI detection

Background

The present process of STEMI detection is cumbersome as it utilizes outdated equipment and requires a trained technician and an expert cardiologist. We have developed a patient-administered, Artificial Intelligence (AI) guided, Single Lead EKG for early STEMI detection.

Purpose

To answer the question “Is early STEMI detection possible with a Single Lead EKG?”

Methods

We experimented with an AI-guided algorithm for a single-lead EKG for STEMI detection with the following step-wise developments: 1) An AI algorithm that predictably interprets STEMI using a 12-lead EKG; 2) An AI algorithm for STEMI detection using a single-lead EKG; 3) A methodology for identifying the best single lead to detect STEMI; 4) Advanced AI algorithms for STEMI localization with a single-lead EKG. The AI methodology was as follows: Sample: The mammoth Latin American Telemedicine Infarct Network telemedicine database that provides an umbrella of AMI management to 100 million patients in Brazil, Colombia, Mexico, Chile, and Argentina was queried for cardiologist annotated EKG. A total of 8,511 EKG and 90,592 classified heartbeats were selected for the experiments. Preprocessing: segmentation of each ECG into individual heartbeats. Training & Testing: 90% and 10%, respectively, of the total dataset. Classification: 1-D Convolutional Neural Network; classes were construed for each heartbeat. Performance indicators were calculated per lead.

Results

The algorithm was able to provide an accuracy of 91.9%. Lead V2 yielded the best results among individual leads for STEMI detection.

Conclusions

Early experiments provide a framework for augmenting STEMI detection with the use of AI-guided, single lead techniques. Such approaches seem rational as we target the reduction of true STEMI ischemic times.

Funding Acknowledgement

Type of funding source: None

Countdown to physician-free EKG interpretation

Background

With the introduction of electronic medical records and other digital platforms, the classification and coding of different medical entities have become a complex, cumbersome task that is prone to diagnostic inconsistencies and errors. By incorporating Artificial Intelligence (AI) to a massive database of EKG records, we have developed an innovative methodology to accurately discriminate an EKG as “normal” or “abnormal”. We firmly believe that this algorithm sets up medicine on a path of complete computer-aided EKG interpretation.

Purpose

To present a viable AI-guided filter that can accurately discriminate between normal and abnormal EKG within a cardiologist-annotated EKG database.

Methods

An observational, retrospective, case-control study. Samples: A total of 140,000 randomly sampled 12-lead ECG of 10-seconds length with a sampling frequency of 500 [Hz] from Brazil (BR) and Colombia (CO) (divided as 70,000 normal and 70,000 abnormal EKG records per country dataset) were derived from the private International Telemedical System (ITMS) database from September 2018 to July 2019. Only de-identified records were used, records with artifacts were excluded. Preprocessing: Only the first 2s of each short lead and 9s of the long lead were considered. This data includes mobile (MOB) and transtelephonic (TTP) EKGs (50/50 ratio). Limb leads I, II and III and precordial leads V1, V2, V3 and V5 were used. The mean was removed from each lead. Training Sets: Four models were trained as depicted in the figure below. Each training dataset has 25,000 Normal and 25,000 Abnormal records, where 10% of the total records were used as a validation set. The test sets included 10,000 normal, and 10,000 abnormal records each. Testing and Class Assigning: An inception convolutional neural network was implemented; Each model was tested with 5,000 normal and 5,000 abnormal records of the corresponding country and transmission type with which they were trained. “Normal” or “Abnormal” labels were assigned to each EKG record and were compared to the cardiologists' reports; performance indicators (accuracy, sensitivity, and specificity) were calculated for each model.

Results

An overall accuracy of 82.4%; sensitivity of 88.7%; and specificity of 76.2% was achieved amongst the 4 testing models (Separate results of each training set are shown below).

Conclusion(s)

AI enables the interpretation of digital EKG records to be exercised in an organized, accurate, and straightforward manner, taking into consideration the multiple potential entities that can be diagnosed through this historical triage tool. By quickly identifying the normal records, the cardiologist is able to invest efforts in treating patients in a timely manner.

Funding Acknowledgement

Type of funding source: None

Maximum artificial intelligence and complete reconstruct of population-based AMI care

Background

After creating a behemoth hub and spoke AMI network that encompasses more than 100 million patients in 5 countries, we have begun to incorporate Artificial Intelligence (AI) algorithms into our telemedicine strategy with the goal of creating comprehensive, very early AMI diagnosis and physician-free triage. In doing so, we have replaced door-to-balloon times (d2b) with symptom-to-balloon times (s2b) as an immutable objective.

Purpose

To incorporate AI attributes for very early AMI detection, triage, and management.

Methods

We expanded our effective telemedicine strategy (100 million population; 877,178 telemedicine encounters; 55% overall mortality reduction; $291 million cost savings) with a logistic reset to impact s2b. To do this, we incorporated our Single Lead 1.0 (lead I) and Single Lead 2.0 (lead V2) technology for self-administered AMI detection with our physician-free STEMI diagnosis and triage AI algorithms. Single Lead algorithms and physician-free protocols were generated by utilizing Machine Learning from our mammoth annotated EKG repository.

Results

In addition to three logistic markers of efficiency Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT); we are monitoring s2b. A gradual release of the algorithms and single lead is occurring at the telemedicine spokes. Detailed results will be available at the time of presentation.

Conclusions

Impacting s2b, the Achilles Heel of Primary PCI, may be achieved with the use of patient-administered AMI detection tools. Incorporation of these technologies into AI algorithms will add to telemedicine efficiencies for population-based AMI care.

Funding Acknowledgement

Type of funding source: None

AI and telemedicine: total remote guidance of AMI management

Background

For a decade, Latin American Telemedicine Infarct Network (LATIN) Telemedicine has transformed AMI management in Brazil, Colombia, Mexico, Chile, and Argentina. With a hub and spoke strategy, AMI coverage was expanded to 100 million population and 877,177 telemedicine encounters were performed. Cost savings from avoiding unnecessary transfer of patients was $291 million. We are now rapidly escalating on a path to making the telemedicine process “physician-free” by utilizing Artificial Intelligence (AI) protocols.

Purpose

To demonstrate that AI can replace a cardiologist for remote AMI telemedicine guidance.

Methods

The process of total AI guidance focused on both aspects of our telemedicine strategy – accurate AMI diagnosis and tele-guidance of the entire STEMI process. We developed our innovative approach by initially creating AI algorithms for computer-aided diagnosis. Next, we incorporated logistic variables (duration of chest pain, transfer times to LATIN hub, etc) to the algorithm for physician-free triage into thrombolysis, primary PCI and pharmaco-invasive management. The intent of creating AI algorithms was early STEMI detection and triage. After the patient was efficiently transferred to the hub, a final treatment decision was made by the hub cardiologists.

Results

Three crucial areas of telemedicine efficiency are being monitored – Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT). All are showing improvements. Detailed results will be available at the time of presentation.

Conclusions

We are encouraged with the possibility of making the entire telemedicine guidance of AMI management “physician-free”. Next-Gen improvements are being contemplated by including a Single Lead EKG for AMI detection that will impact symptom-to-balloon times.

Funding Acknowledgement

Type of funding source: None

Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms

Background

Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario.

Purpose

To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner.

Methods

The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2,019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later submitting them to the AI algorithm implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued.

Results

Overall, cardiologists confirmed 23,213 out of 25,013 AI outputs for “Normal” EKGs, demonstrating a concordance of 92.8% for Normal diagnosis.

Conclusion

Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice.

Funding Acknowledgement

Type of funding source: None

Enriching artificial intelligence ST-elevation myocardial infarction (STEMI) detection algorithms with differential diagnoses

Background

STEMI outcomes, although improved with systems of care, are hamstrung by delayed presentation and prevaricates of a 12-lead ECG. We report an artificial intelligence (AI) guided, single lead EKG algorithm for a self-administered tool to reliably detect STEMI and trigger ambulance dispatch.

Purpose

To provide a reliable and improved AI-guided Single Lead EKG methodology.

Methods

From our cardiologist-annotated repository, we assigned a dataset of 11,118 classified ECG. Ontology organized 5 groups apportioned for an interclass balance among commoner STEMI differential diagnoses. This anonymous, pre-classified data included 5,549 STEMI, 1,391 normal, 1,393 Bundle Branch Block, 1,393 non-specific ST-T changes and 1,392 miscellaneous. Each ECG was fragmented into individual 1-lead strips. Algorithm: 1-D Convolutional Neural Networks. Gender and age were included before the last dense layer. Training and Testing: Preset 90% dataset (10,008 ECG) train, 10% test (1,110 ECG). Statistical Analysis and ROC curves: Digitized dataset, 500 samples/second, 10s duration, total 5,000 samples per lead. Statistical mean for each lead was calculated and subtracted from the original lead. Statistical values and ROC curves were assessed.

Results

Most Accurate: Lead V2 – 91%; Most Sensitive: Lead I – 92% Most Specific: Lead III – 96%. Best AUC: Lead V2 – 91%.

Conclusions

Incorporating subtypes of STEMI differential diagnosis enriches the single lead AI algorithm. Validating the derived algorithm with our entire database of 18 million ECG will further strengthen the results.

Funding Acknowledgement

Type of funding source: None

Efficacy of betablockers in patients with acute coronary syndrome: a systematic review and meta analysis of randomized trials

Background

Since the inception of clinical guidelines on the management of patients with acute coronary syndrome (ACS), betablocker therapy has been included as a class I recommendation. However, most studies evaluating betablockers in ACS were conducted in the pre-reperfusion era. Currently, the great majority of patients undergo reperfusion and secondary prevention therapy has evolved; the impact of treatment with a betablocker in these patients may be different.

Purpose

We conducted a systematic review and meta-analysis to evaluate the impact of betablockers on mortality in patients after an ACS in the reperfusion era.

Methods

We searched MEDLINE, EMBASE, and Cochrane Central Registry of Controlled Trials for RCTs from inception to September 2019. We included randomized controlled trials comparing betablockers to no betablockers in adult patients presenting with an ACS. Independently and in duplicate, we screened titles and abstracts, reviewed the full-text report of potentially eligible studies and extracted data. Two reviewers also evaluated the risk of bias in duplicate. Disagreements were addressed by consensus. We considered trials to be conducted in the reperfusion era if reperfusion was attempted in more than 50% of patients, either with thrombolytics or primary angioplasty. Our primary outcome of interest was all-cause mortality. Secondary outcomes included hospitalization for heart failure, nonfatal myocardial infarction, stroke and cardiogenic shock. We pooled trial outcomes using a fixed effects model. The study protocol is registered with PROSPERO (CRD42019143158).

Results

After the initial screening of 10,969 references and full-text review of 176 articles, nine RCTs comprising a total of 49,639 patients with ACS were eligible for the final analysis. Predominantly, these patients presented with ST elevation myocardial infarction. Treatment with a betablocker did not improve all-cause mortality at 30 days (risk ratio (RR) 0.98 [95% CI 0.92–1.04], I2=44%), or at longest follow up (up to three years) with RR 0.97 ([95% CI 0.91–1.03], I2=0%). Betablocker therapy was associated with an increased risk of HF hospitalization (RR 1.10 [95% CI 1.05–1.15], I2=52%) and cardiogenic shock during index hospitalization (RR 1.29, [95% CI 1.18–1.40], I2=0%). However, betablocker therapy reduced the risk of nonfatal myocardial infarction (RR 0.72 [95% CI 0.63–0.83], I2=0%); it did not impact the risk of stroke (RR 1.13 [95% CI 0.95–1.35], I2=0%).

Conclusion

In the reperfusion era, betablocker therapy after an ACS does not appear to improve short or long-term survival. Although betablocker therapy was associated with a reduction in nonfatal myocardial infarction, it increased the risk of heart failure hospitalization and cardiogenic shock. In light of these findings, clinical guidelines should reconsider the strength of their recommendation for betablocker use in the ACS population until further contemporary evidence is available.

Funding Acknowledgement

Type of funding source: None

Baby steps in the path of modifying the role of cardiologists for interpreting EKG for AMI

Background

EKG interpretation is slowly transitioning to a physician-free, Artificial Intelligence (AI)-driven endeavor. Our continued efforts to innovate follow a carefully laid stepwise approach, as follows: 1) Create an AI algorithm that accurately identifies STEMI against non-STEMI using a 12-lead EKG; 2) Challenging said algorithm by including different EKG diagnosis to the previous experiment, and now 3) To further validate the accuracy and reliability of our algorithm while also improving performance in a prehospital and hospital settings.

Purpose

To provide an accurate, reliable, and cost-effective tool for STEMI detection with the potential to redirect human resources into other clinically relevant tasks and save the need for human resources.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10-seconds length with sampling frequency of 500 [Hz], including the following balanced classes: unconfirmed and angiographically confirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding the ones included in other classes). The label of each record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: The first and last 250 samples were discarded as they may contain a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI (STEMI in different locations of the myocardium – anterior, inferior and lateral); Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10; respectively. The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. Additional testing was performed with a subset of the original dataset of angiographically confirmed STEMI.

Results

See Figure Attached – Preliminary STEMI Dataset Accuracy: 96.4%; Sensitivity: 95.3%; Specificity: 97.4% – Confirmed STEMI Dataset: Accuracy: 97.6%; Sensitivity: 98.1%; Specificity: 97.2%.

Conclusions

Our results remain consistent with our previous experience. By further increasing the amount and complexity of the data, the performance of the model improves. Future implementations of this technology in clinical settings look promising, not only in performing swift screening and diagnostic steps but also partaking in complex STEMI management triage.

Funding Acknowledgement

Type of funding source: None

Antiplatelet therapy prescription patterns for acute coronary syndrome: a decade analysed

Background/Introduction

Guidelines recommend dual antiplatelet therapy (DAPT) with acetylsalicylic acid (ASA) and ticagrelor following acute coronary syndrome (ACS) regardless of management strategy. Despite this, prescription practices lag and appropriate DAPT is not utilized.

Purpose

We aimed to trend differences in P2Y12 inhibitor prescriptions between ACS patients managed with percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). As well, we wanted to analyze the impact practice-changing trial publications, national guideline updates, and publicly funded drug coverage plans may have on prescription patterns.

Methods

From national databases, we obtained data for ACS patients in the province of Ontario, Canada between 2008 and 2018. Using an interrupted-time series with data aggregated monthly, we evaluated types of P2Y12 inhibitor prescribed at hospital discharge and changes to antiplatelet prescription patterns following publication of Ticagrelor versus Clopidogrel in Patients with Acute Coronary Syndrome (PLATO), Canadian Cardiovascular Society (CCS) antiplatelet therapy guidelines, and ticagrelor coverage by a publicly funded medication plan.

Results

We included 114,142 ACS patients; 49% underwent PCI and 8% required CABG. Between October 2008 and March 2018, the proportion of patients discharged on P2Y12 inhibitors increased from 73.4% to 87% (p&lt;0.0001) for PCI patients and 11.4% to 31.4% (p&lt;0.0001) for CABG patients. PLATO publication was associated with a 1.3% (p=0.002) monthly decline in clopidogrel prescriptions amongst PCI patients. The 2010 CCS antiplatelet therapy guidelines were associated with a 0.7% (p&lt;0.0001) monthly decline in clopidogrel prescriptions amongst PCI patients. The approval of ticagrelor by publicly funded medication plan was associated with an increase in ticagrelor prescriptions within the first month (24.5%; p&lt;0.0001) and a continued monthly increase (0.4%; p&lt;0.0001) in PCI patients. The approval was also associated with an increase in monthly ticagrelor prescriptions (0.2%; p&lt;0.0001) amongst CABG patients. The 2012 CCS antiplatelet therapy guidelines were associated with a decline in clopidogrel prescriptions within the first month (6.1%; p=0.003) and a monthly increase in ticagrelor prescriptions (0.3%; p=0.05) amongst PCI patients.

Conclusion

Drug coverage by a publicly funded medication plan and guideline updates had significant impact on P2Y12 inhibitor prescription practices. Despite improvements, P2Y12 inhibitor prescriptions for CABG patients are far behind PCI patients. Further research is necessary to address barriers to appropriate antiplatelet therapy in the ACS population.

Antiplatelet Prescription Patterns

Funding Acknowledgement

Type of funding source: Public hospital(s). Main funding source(s): New Investigator Fund - Hamilton Health Sciences Foundation, Hamilton, Canada

Innovative techniques to construct powerful artificial intelligence algorithms for st-elevation myocardial infarction

Background

With the sudden advent of Artificial Intelligence (AI), incorporation of these technologies into key aspects of our working environment has become an ever so delicate task, especially so when dealing with time-sensitive and potentially lethal scenarios such as ST-Elevation Myocardial Infarction (STEMI) management. By further expanding into our successful experiences with AI-guided algorithms for STEMI detection, we implemented an innovative ensemble method into our methodology as we seek to improve the algorithm's predictive capabilities.

Purpose

Through the ensemble method, we combined two ML techniques to boost our previous experiments' accuracy and reliability.

Methods

Database: EKG records obtained from Latin America Telemedicine Infarct Network (Mexico, Colombia, Argentina, and Brazil) from April 2014 to December 2019. Dataset: Two separate datasets were used to train and test two sets of AI algorithms. The first comprised of 11,567 records and the second 7,286 records, each composed of 12-lead EKG records of 10-second length with sampling frequency of 500 Hz, including the following balanced classes: unconfirmed & angiographically confirmed STEMI (first model); angiographically confirmed STEMI only (second model); and, for both models, we included branch blocks, non-specific ST-T abnormalities, normal, and abnormal (200+ CPT codes, excluding the ones included in other classes). Label per record was manually checked by cardiologists to ensure precision (Ground truth). Pre-processing: First and last 250 samples were discarded to avoid a standardization pulse. An order 5 digital low pass filter with a 35 Hz cut-off was applied. For each record, the mean was subtracted to each individual lead. Classification: The determined classes were STEMI and Not-STEMI (A combination of randomly sampled normal, branch blocks, non-specific ST-T abnormalities and abnormal records – 25% of each subclass). Training & Testing: The last dense layer outputs a probability for each record of being STEMI or Not-STEMI. These probabilities were calculated for each model (Model 1 trained with Complete STEMI dataset and Model 2 trained with confirmed STEMI only dataset) and aggregated using the mean aggregation to generate the final label for each record. A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90%/10%; respectively. Results are reported for both testing datasets (Complete and confirmed STEMI only records).

Results

Complete STEMI Dataset: Accuracy: 96.5% Sensitivity: 96.2% Specificity: 96.9% – Confirmed STEMI only Dataset: Accuracy: 98.5% Sensitivity: 98.3% Specificity: 98.6%'

Conclusion(s)

While Model 1 and Model 2 achieved similar performances with promising results on their own, applying a combination of both through the ensemble model exhibits a clear improvement in performance when applied to both datasets. This provides a blueprint for advanced automated STEMI detection through wearable devices.

Funding Acknowledgement

Type of funding source: None