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.

Impact of home confinement during COVID-19 pandemic on sleep parameters in Parkinson's disease

Background

Literature shows that home confinement during coronavirus disease 2019 (COVID-19) pandemic has significantly affected sleep. However, such information regarding subjects having Parkinson's disease (PD) is unavailable.

Methods

This cross-sectional study was conducted using a questionnaire, developed and validated by experts. PD subjects from nine centers across India were included. Questionnaire assessed presence as well as change in sleep-related parameters and PD symptoms during home confinement. Restless legs syndrome (RLS) and REM sleep behavior disorder (REMBD) was diagnosed using validated questionnaire. Additionally, changes in physical activity, adoption of new hobbies during home confinement and perceived quality of life were assessed.

Results

Of 832 subjects, 35.4% reported sleep disturbances. New-onset/worsening of sleep disturbances (NOWS) was reported by 23.9% subjects. Among those with sleep disturbances (n = 295), insomnia symptoms worsened in half (51.5%) and nearly one-fourth reported worsening of RLS (24.7%) and REMBD (22.7%) each. NOWS was common in subjects lacking adequate family support during home confinement (P = 0.03); home confinement > 60 days (P = 0.05) and duration of PD > 7 years (P = 0.008). Contrarily, physical activity >1 h/day and engagement in new hobbies during home confinement were associated with better sleep. NOWS was associated with worsening of motor as well as non-motor symptoms of PD (P < 0.001) and poorer life quality (P < 0.001).

Conclusion

Home confinement during COVID-19 pandemic was significantly associated with NOWS among PD subjects. NOWS was associated with global worsening of PD symptoms and poorer life quality. Physical activity >1 h/day and adoption of new hobbies during home confinement were associated with better sleep.

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

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&lt;0.001). The index beat also had the lowest CV for GLS (26% versus 43% and 42%; p&lt;0.001) and E/e' (25% versus 41% and 41%; p&lt;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

Spectrum of Truncal Dystonia and Response to Treatment: A Retrospective Analysis

Background:

Presence of truncal dystonia usually points to a secondary cause of dystonia like exposure to dopamine receptor blockers or neurodegenerative illness. Rarely, it can occur as an idiopathic focal or segmental dystonia.

Methods:

Retrospective review of medical records and videos of patients of truncal dystonia presenting in the Botulinum Toxin Clinic of Department of Neurology at Post Graduate Institute of Medical Education and Research, Chandigarh between May 2016 and February 2019.

Results:

A total of 16 patients with predominant truncal dystonia were recruited. There were ten males and six females with mean age of 49.1 ± 15.1 years (range 22–70). Extensor truncal dystonia was the most common (12/16) followed by camptocormia (4/16). Various etiologies included Idiopathic Parkinson’s disease (4/16), Tardive dystonia (5/16), Neurodegeneration with brain iron accumulation (genetically confirmed) (2/16) and idiopathic (5/16). All patients were refractory to a combination of oral medications tried over a period of 1.82 ± 1.93 years. All patients received electromyographic-guided botulinum toxin in paraspinals or rectus abdominis muscles depending upon the type of dystonia. The mean dose of abobotulinum toxin used was 286.7 ± 108.6 units (range 200–500 units) for paraspinals and 297.5 ± 68.5 (range 200–350) for rectus abdominis muscles per session. Average subjective response after botulinum toxin injection session was 31.2 ± 21.5% (range 0–70). No adverse effects were reported.

Conclusion:

Botulinum toxin is an acceptable alternative to patients presenting with medically refractory truncal dystonia and may offer modest benefit.

Utility of Imaging of Nigrosome-1 on 3T MRI and Its Comparison with 18F-DOPA PET in the Diagnosis of Idiopathic Parkinson Disease and Atypical Parkinsonism

Background

Loss of nigrosome-1 on 3T and 7T magnetic resonance imaging (MRI) is a recently explored imaging biomarker in the diagnosis of neurodegenerative parkinsonism.

Objectives

This study was undertaken to evaluate the utility of imaging of nigrosome in the diagnosis of neurodegenerative parkinsonism on 3T MRI.

Methods

An institution-based prospective case-control study was conducted at a tertiary care center in North India. 3T venous blood oxygen level-dependent (VenoBOLD) and high-resolution susceptibility-weighted imaging (SWI) imaging sequences in MRI were performed in 100 patients with parkinsonism (56 with idiopathic Parkinson's disease [IPD], 30 with young onset Parkinson's disease [YOPD], 12 with progressive supranuclear palsy, and 2 patients with multiple system atrophy) and 15 controls. Grading of nigrosome was done in both the sequences. Each patient underwent 18F-DOPA positron emission tomography (PET), detailed neurological examination including Hoen and Yahr (H&Y) staging and Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scoring.

Results

The diagnostic sensitivity and specificity of the detection of loss of nigrosome-1 on VenoBOLD and SWI sequence at 3T MR imaging were 90% and 66.7% and 94% and 80%, respectively. A weak negative correlation was found between the grading of the nigrosome and clinical parameters (H&Y and UPDRS III). There was no correlation between the side of nigrosome loss and clinical asymmetry. However, nigrosome imaging was not able to differentiate between Parkinson's disease and atypical parkinsonism.

Conclusions

The loss of nigrosome-1 on 3T MRI on SWI and VenoBOLD sequences may serve as a potential imaging marker in the diagnosis of degenerative parkinsonian syndromes. However, it cannot differentiate between idiopathic Parkinson's disease and atypical parkinsonian syndromes.