Detection Of ST-Elevation Myocardial Infarction Research Articles

Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients.

We compared four methods to screen emergency department (ED) patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI) in a 5-year retrospective cohort through observed practice, objective application of screening protocol criteria, a predictive model, and a model augmenting human practice. We measured screening performance by sensitivity, missed acute coronary syndrome (ACS) and STEMI, and the number of ECGs required. Our cohort of 279,132 ED visits included 1397 patients who had a diagnosis of ACS. We found that screening by observed practice augmented with the model delivered the highest sensitivity for detecting ACS (92.9%, 95%CI: 91.4-94.2%) and showed little variation across sex, race, ethnicity, language, and age, demonstrating equity. Although it missed a few cases of ACS (7.6%) and STEMI (4.4%), it did require ECGs on an additional 11.1% of patients compared to current practice. Screening by protocol performed the worst, underdiagnosing young, Black, Native American, Alaskan or Hawaiian/Pacific Islander, and Hispanic patients. Thus, adding a predictive model to augment human practice improved the detection of ACS and STEMI and did so most equitably across the groups. Hence, combining human and model screening--rather than relying on either alone--may maximize ACS screening performance and equity.

Performance metrics of AI-enhanced single lead EKG maintained after entry of organised clustered data

Abstract Background Our experience in creating innovative Artificial Intelligence-guided single lead EKG methodologies for ST-Elevation Myocardial Infarction (STEMI) detection within complex EKG records has been previously validated. Purpose By expanding the intricate variables of our previously tested algorithm input, we seek to further improve our STEMI detecting tool. Methods 11,567 12-lead EKG records (10-s length, 500 Hz sample frequency) derived from the Latin America Telemedicine Infarct Network database from April 2014 to December 2019. From these records, we included the following balanced classes: angiographically confirmed and unconfirmed STEMI (divided by wall affected), branch blocks, non-specific ST-T changes, normal, and abnormal (Remaining 200+ CPT codes). Cardiologist annotations ensured precision (Ground truth). Determined classes were “STEMI” and “Not-STEMI”. A 1-D Convolutional Neural Network model was trained and tested for each lead with dataset proportions of 90/10, respectively. The last dense layer outputs a probability for each record being STEMI/Not-STEMI. The analysis also included performance metrics and false-negative reports. Results Overall, the most promising Single lead for STEMI detection was V2 (91.2% Accuracy, 89.6% Sensitivity, and 92.9% Specificity). 55% of false negatives were inferior wall STEMI (Table 1). Conclusion Appreciable progress of our new methodology compared to our previous experiences in AI-guided Single Lead for STEMI detection, especially for lead V2. By performing a thorough analysis of false-negative reports, we aspire to identify potential areas of STEMI detection weakness which will become the focus of future ventures. Funding Acknowledgement Type of funding sources: None.

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P < 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI.

Performance metrics of AI-enhanced single lead EKG maintained after entry of organised clustered data

Abstract Background Our experience in creating innovative Artificial Intelligence-guided single lead EKG methodologies for ST-Elevation Myocardial Infarction (STEMI) detection within complex EKG records has been previously validated. Purpose By expanding the intricate variables of our previously tested algorithm input, we seek to further improve our STEMI detecting tool. Methods 11,567 12-lead EKG records (10-s length, 500 Hz sample frequency) derived from the Latin America Telemedicine Infarct Network database from April 2014 to December 2019. From these records, we included the following balanced classes: angiographically confirmed and unconfirmed STEMI (divided by wall affected), branch blocks, non-specific ST-T changes, normal, and abnormal (Remaining 200+ CPT codes). Cardiologist annotations ensured precision (Ground truth). Determined classes were “STEMI” and “Not-STEMI”. A 1-D Convolutional Neural Network model was trained and tested for each lead with dataset proportions of 90/10, respectively. The last dense layer outputs a probability for each record being STEMI/Not-STEMI. The analysis also included performance metrics and false-negative reports. Results Overall, the most promising Single lead for STEMI detection was V2 (91.2% Accuracy, 89.6% Sensitivity, and 92.9% Specificity). 55% of false negatives were inferior wall STEMI (Table 1). Conclusion Appreciable progress of our new methodology compared to our previous experiences in AI-guided Single Lead for STEMI detection, especially for lead V2. By performing a thorough analysis of false-negative reports, we aspire to identify potential areas of STEMI detection weakness which will become the focus of future ventures. Funding Acknowledgement Type of funding sources: None.

Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study.

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2–90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.

Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram

We aimed to measure the diagnostic accuracy of the deep learning model (DLM) for ST-elevation myocardial infarction (STEMI) on a 12-lead electrocardiogram (ECG) according to culprit artery sorts. From January 2017 to December 2019, we recruited patients with STEMI who received more than one stent insertion for culprit artery occlusion. The DLM was trained with STEMI and normal sinus rhythm ECG for external validation. The primary outcome was the diagnostic accuracy of DLM for STEMI according to the three different culprit arteries. The outcomes were measured using the area under the receiver operating characteristic curve (AUROC), sensitivity (SEN), and specificity (SPE) using the Youden index. A total of 60,157 ECGs were obtained. These included 117 STEMI-ECGs and 60,040 normal sinus rhythm ECGs. When using DLM, the AUROC for overall STEMI was 0.998 (0.996–0.999) with SEN 97.4% (95.7–100) and SPE 99.2% (98.1–99.4). There were no significant differences in diagnostic accuracy within the three culprit arteries. The baseline wanders in false positive cases (83.7%, 345/412) significantly interfered with the accurate interpretation of ST elevation on an ECG. DLM showed high diagnostic accuracy for STEMI detection, regardless of the type of culprit artery. The baseline wanders of the ECGs could affect the misinterpretation of DLM.

A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department.

BackgroundRapid revascularization is the key to better patient outcomes in ST-elevation myocardial infarction (STEMI). Direct activation of cardiac catheterization laboratory (CCL) using artificial intelligence (AI) interpretation of initial electrocardiography (ECG) might help reduce door-to-balloon (D2B) time. To prove that this approach is feasible and beneficial, we assessed the non-inferiority of such a process over conventional evaluation and estimated its clinical benefits, including a reduction in D2B time, medical cost, and 1-year mortality.MethodsThis is a single-center retrospective study of emergency department (ED) patients suspected of having STEMI from January 2021 to June 2021. Quantitative ECG (QCG™), a comprehensive cardiovascular evaluation system, was used for screening. The non-inferiority of the AI-driven CCL activation over joint clinical evaluation by emergency physicians and cardiologists was tested using a 5% non-inferiority margin.ResultsEighty patients (STEMI, 54 patients [67.5%]) were analyzed. The area under the curve of QCG score was 0.947. Binned at 50 (binary QCG), the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 98.1% (95% confidence interval [CI], 94.6%, 100.0%), 76.9% (95% CI, 60.7%, 93.1%), 89.8% (95% CI, 82.1%, 97.5%) and 95.2% (95% CI, 86.1%, 100.0%), respectively. The difference in sensitivity and specificity between binary QCG and the joint clinical decision was 3.7% (95% CI, −3.5%, 10.9%) and 19.2% (95% CI, −4.7%, 43.1%), respectively, confirming the non-inferiority. The estimated median reduction in D2B time, evaluation cost, and the relative risk of 1-year mortality were 11.0 minutes (interquartile range [IQR], 7.3–20.0 minutes), 26,902.2 KRW (22.78 USD) per STEMI patient, and 12.39% (IQR, 7.51–22.54%), respectively.ConclusionAI-assisted CCL activation using initial ECG is feasible. If such a policy is implemented, it would be reasonable to expect some reduction in D2B time, medical cost, and 1-year mortality.

Evolution of single-lead ECG for STEMI detection using a deep learning approach

BackgroundWhile ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 90 min, symptom to door times remain long at 2.5-h, due at least in part to a delay in diagnosis. ObjectivesTo develop and validate a machine learning-guided algorithm which uses a single‑lead electrocardiogram (ECG) for STEMI detection to speed diagnosis. MethodsData was extracted from the Latin America Telemedicine Infarct Network (LATIN), a population-based Acute Myocardial Infarction (AMI) program that provides care to patients in Brazil, Colombia, Mexico, and Argentina through telemedicine. Sample: the first dataset was comprised of 8511 ECGs that were used for various machine learning experiments to test our Deep Learning approach for STEMI diagnosis. The second dataset of 2542 confirmed STEMI diagnosis EKG records, including specific ischemic heart wall information (anterior, inferior, and lateral), was derived from the previous dataset to test the STEMI localization model. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats with fixed window of 0.4 s to the left and 0.9 s to the right of main. Training & Testing: 90% and 10% of the total dataset, respectively, were used for both models. Classification: two 1-D convolutional neural networks were implemented, two classes were considered for first models (STEMI/Not-STEMI) and three classes for the second model (Anterior/Inferior/Lateral) each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each model. ResultsThe single‑lead ECG strategy was able to provide an accuracy of 90.5% for STEMI detection with Lead V2, which also yielded the best results overall among individual leads. STEMI Localization model provided promising results for anterior and inferior wall STEMIs but remained suboptimal for Lateral STEMI. ConclusionsAn Artificial Intelligence-enhanced single‑lead ECG is a promising screening tool. This technology provides an autonomous and accurate STEMI diagnostic alternative that can be incorporated into wearable devices, potentially providing patients reliable means to seek treatment early and offers the potential to thereby improve STEMI outcomes in the long run.

A modified cardiac triage strategy reduces door to ECG time in patients with ST elevation myocardial infarction

Timely performing electrocardiography (ECG) is crucial for early detection of ST-elevation myocardial infarction (STEMI). For shortening door-to-ECG time, a chief complaint-based “cardiac triage” protocol comprising (1) raising alert among medical staff with bedside triage tags, and (2) immediate bedside ECG after focused history-taking was implemented at the emergency department (ED) in a single tertiary referral center. All patients diagnosed with STEMI visiting the ED between November 2017 and January 2020 were retrospectively reviewed to investigate the effectiveness of strategy before and after implantation. Analysis of a total of 117 ED patients with STEMI (pre-intervention group, n = 57; post-intervention group, n = 60) showed significant overall improvements in median door-to-ECG time from 5 to 4 min (p = 0.02), achievement rate of door-to-ECG time < 10 min from 45 to 57% (p = 0.01), median door-to-balloon time from 81 to 70 min (p < 0.01). Significant trends of increase in achievement rates for door-to-ECG and door-to-balloon times (p = 0.032 and p = 0.002, respectively) was noted after strategy implementation. The incidences of door-to-ECG time > 10 min for those with initially underestimated disease severity (from 90 to 10%, p < 0.01) and walk-in (from 29.2 to 8.8%, p = 0.04) were both reduced. In conclusion, a chief complaint-based “cardiac triage” strategy successfully improved the quality of emergency care for STEMI patients through reducing delays in diagnosis and treatment.

Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram.

To develop an artificial intelligence-based approach with multi-labelling capability to identify both ST-elevation myocardial infarction (STEMI) and 12 heart rhythms based on 12-lead electrocardiograms (ECGs). We trained, validated, and tested a long short-term memory (LSTM) model for the multi-label diagnosis of 13 ECG patterns (STEMI + 12 rhythm classes) using 60537 clinical ECGs from 35981 patients recorded between 15 January 2009 and 31 December 2018. In addition to the internal test above, we conducted a real-world external test, comparing the LSTM model with board-certified physicians of different specialties using a separate dataset of 308 ECGs covering all 13 ECG diagnoses. In the internal test, the area under the curves (AUCs) of the LSTM model in classifying the 13 ECG patterns ranged between 0.939 and 0.999. For the external test, the LSTM model for multi-labelling of the 13 ECG patterns evaluated by AUC was 0.987 ± 0.021, which was superior to those of cardiologists (0.898 ± 0.113, P < 0.001), emergency physicians (0.820 ± 0.134, P < 0.001), internists (0.765 ± 0.155, P < 0.001), and a commercial algorithm (0.845 ± 0.121, P < 0.001). Of note, the LSTM model achieved an accuracy of 0.987, AUC of 0.997, and precision, recall, and F 1 score of 0.952, 0.870, and 0.909, respectively, in detecting STEMI. We demonstrated the usefulness of an LSTM model in the multi-labelling detection of both rhythm classes and STEMI in competitive testing against board-certified physicians. This AI tool exceeding the cardiologist-level performance in detecting STEMI and rhythm classes on 12-lead ECG may be useful in prioritizing chest pain triage and expediting clinical decision-making in healthcare.

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

Abstract 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 &amp; 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 &amp; 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

A Statewide Assessment of Prehospital Electrocardiography Approaches of Acquisition and Interpretation for ST-Elevation Myocardial Infarction Based on Emergency Medical Services Characteristics

Background: The American Heart Association recommends acquiring and interpreting prehospital electrocardiograms (ECG) for patients transported by Emergency Medical Services (EMS) to the emergency department with symptoms highly suspicious of acute coronary syndrome. If interpreted correctly, prehospital ECGs have the potential to improve early detection of ST-elevation myocardial infarction (STEMI) and inform prehospital activation of the cardiac catheterization laboratory, thus reducing total ischemic time and improving patient outcomes. Standardized protocols for prehospital ECG interpretation methods are lacking due to variations in EMS system design, training, and procedures. Objectives: We aimed to describe approaches for prehospital ECG interpretation in EMS systems across North Carolina (NC), and examine potential differences among systems. Methods: A 35-item internet survey was sent to all NC EMS systems (n = 99). Questions pertaining to prehospital ECG interpretation methods included: paramedic, computerized algorithm (i.e., software interpretation), combined approaches, and/or transmission for physician interpretation, transmission capability, cardiac catheterization laboratory activation, and EMS system characteristics (e.g. rural versus urban). Data were summarized and compared. Results: A total of 96 EMS systems across NC responded to the survey (97% response rate); of these, 69% were rural. EMS medical directors (53%) or EMS administrative directors (42%) completed the majority of surveys. While 91% of EMS systems had a prehospital ECG interpretation protocol in place, only 61% had a written cardiac catheterization laboratory activation policy. More than half (55%) of systems reported paramedic interpretation of prehospital ECGs, followed by a combined paramedic and software interpretation approach (39%), physician interpretation (4%), or software interpretation only approach (2%). Nearly 80% of EMS systems transmitted prehospital ECGs to receiving hospitals (always or sometimes), regardless of interpretation method. All EMS systems had some paid versus non-paid EMS personnel and the majority (86%) had both basic and advanced life support capabilities. Conclusions: Most NC EMS systems had a paramedic only ECG interpretation or paramedic in combination with a computerized algorithm approach. Very few used a physician read approach following transmission, even in rural service areas.

3035Synergy of artificial intelligence and single lead EKG to detect and localize STEMI

Abstract Background The cumbersome, standard 12-lead electrocardiogram (EKG) challenges an efficient detection of ST-Elevation Myocardial Infarction (STEMI) in pre-hospital (ambulances) and hospital (portable devices) settings. We believe that our machine-learning algorithm embedded into a single lead EKG will be successful in acute care settings. Purpose To incorporate Artificial Intelligence-guided, single lead EKG interpretation, to facilitate easy and accurate STEMI detection in urgent situations. Methods This is an observational, retrospective, case-control study. A subset sample was generated from the International Telemedical Systems (ITMS) database that contains cardiologist annotated EKG records. Subset: A total of 2,542 exclusively confirmed STEMI diagnosis EKG records from enrolled healthcare centers in Mexico, Colombia, and Brazil; including specific ischemic heart wall (anterior, inferior, and lateral). Following discharge of treated patients, confirmation of STEMI diagnosis was obtained as feedback from healthcare centers. Records were anonymized EKG that excluded all medical information. Sample: A Standard 12 lead, 10-seconds length, 500Hz sampling frequency EKG was fed to the LUMENGT-AI STEMI detecting algorithm. Preprocessing: Detection of QRS complexes by wavelet system, segmentation of each EKG record into individual heartbeats (total dataset 27,152 beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, three classes were considered for individual heartbeats: “Anterior”, “Inferior” and “Lateral”, each corresponding to the heart wall affected. These individual probabilities were aggregated to generate the final label for each of the 12 leads. Training &amp; Testing: 90% and 10% of the dataset was used respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with a NVidia GTX 1070 GPU, 8GB RAM. Results Accuracy – Lead V2 (91.7%); Sensitivity Anterior wall – Lead V2 (97.4%); Sensitivity for Lateral wall – Lead I (10.0%); and Sensitivity for Inferior wall – Lead V2 (93.6%). Conclusions AI algorithms merged with a Single lead approach detect and localize STEMI within any setting. The V2 lead yields superior results for mapping of ischemic areas of the heart among the anterior and inferior walls. In contrast, diagnosis remains suboptimal for identifying the lateral wall. The usage of synergistic technologies facilitates easy, fast and early STEMI triage and management.

P2426Validating the diagnostic value of a machine learning algorithm for STEMI detection

Abstract Background We have previously reported the use of Artificial Intelligence (AI) guided EKG analysis for detection of ST-Elevation Myocardial Infarction (STEMI). To demonstrate the diagnostic value of our algorithm, we compared AI predictions with reports that were confirmed as STEMI. Purpose To demonstrate the absolute proficiency of AI for detecting STEMI in a standard12-lead EKG. Methods An observational, retrospective, case-control study. Sample: 5,087 EKG records, including 2,543 confirmed STEMI cases obtained via feedback from health centers following appropriate patient management (thrombolysis, primary Percutaneous Coronary Intervention (PCI), pharmacoinvasive therapy or coronary artery bypass surgery). Records excluded patient and medical information. The sample was derived from the International Telemedical Systems (ITMS) database. LUMENGT-AI Algorithm was employed. Preprocessing: detection of QRS complexes by wavelet system, segmentation of each EKG into individual heartbeats (53,667 total beats) with fixed window of 0.4s to the left and 0.9s to the right of main QRS; Classification: A 1-D convolutional neural network was implemented, “STEMI” and “Not-STEMI” classes were considered for each heartbeat, individual probabilities were aggregated to generate the final label for each record. Training &amp; Testing: 90% and 10% of the sample were used, respectively. Experiments: Intel PC i7 8750H processor at 2.21GHz, 16GB RAM, Windows 10 OS with NVIDIA GTX 1070 GPU, 8GB RAM. Results The model yielded an accuracy of 97.2%, a sensitivity of 95.8%, and a specificity of 98.5%. Conclusion(s) Our AI-based algorithm can reliably diagnose STEMI and will preclude the role of a cardiologist for screening and diagnosis, especially in the pre-hospital setting.

Confidence Level and Knowledge of Pre-hospital Thrombolysis Therapy of Paramedic in Prehospital Care: A Survey

Data on knowledge, attitude and ST Elevation Myocardial Infarction (STEMI) electrocardiography (ECG) detection among pre-hospital paramedics in Malaysia is still scarce. It is imperative to gain this information as a pre-requisite in initiating pre-hospital thrombolysis. This was a descriptive study conducted from May 2016 until November 2017 on paramedics from Universiti Kebangsaan Malaysia Medical Center (UKMMC) and Kuala Lumpur Hospital (HKL). It was to assess knowledge on pre-hospital thrombolytic therapy and ECG detection of STEMI. One hundred and six questionnaires were answered and analyzed. More than 60% of the candidates were unable to detect STEMI ECG changes and lacked of confidence in handling and administering thrombolytic treatment at the level of pre-hospital phase. Only 20.8% candidates managed to detect more than 8/10 of the correct ECG pattern in STEMI. Less than 40% of the candidates achieved more than 80% marks in answering questions regarding STEMI and treatment. It is concluded that the overall performance and knowledge of paramedic regarding pre-hospital thrombolytic therapy is generally poor. Further training need to be done prior to implementing pre-hospital STEMI thrombolytic therapy in Malaysia.

Chest-lead ST-J amplitudes using arm electrodes as reference instead of the Wilson central terminal in smartphone ECG applications: Influence on ST-elevation myocardial infarction criteria fulfillment.

Background“Smartphone 12‐lead ECG” for the assessment of acute myocardial ischemia has recently been introduced. In the smartphone 12‐lead ECG either the right or the left arm can be used as reference for the chest electrodes instead of the Wilson central terminal. These leads are labeled “CR leads” or “CL leads.” We aimed to compare chest‐lead ST‐J amplitudes, using either CR or CL leads, to those present in the conventional 12‐lead ECG, and to determine sensitivity and specificity for the diagnosis of STEMI for CR and CL leads.MethodsFive hundred patients (74 patients with ST elevation myocardial infarction (STEMI), 66 patients with nonischemic ST deviation and 360 controls) were included. Smartphone 12‐lead ECG chest‐lead ST‐J amplitudes were calculated for both CR and CL leads.ResultsST‐J amplitudes were 9.1 ± 29 μV larger for CR leads and 7.7 ± 42 μV larger for CL leads than for conventional chest leads (V leads). Sensitivity and specificity were 94% and 95% for CR leads and 81% and 97% for CL leads when fulfillment of STEMI criteria in V leads was used as reference. In ischemic patients who met STEMI criteria in V leads, but not in limb leads, STEMI criteria were met with CR or CL leads in 91%.ConclusionBy the use of CR or CL leads, smartphone 12‐lead ECG results in slightly lower sensitivity in STEMI detection. Therefore, the adjustment of STEMI criteria may be needed before application in clinical practice.

The utility of the triage electrocardiogram for the detection of ST-segment elevation myocardial infarction

IntroductionCurrent AHA/ACC guidelines on the management of ST-elevation myocardial infarction (STEMI) suggest that an ECG is indicated within 10minutes of arrival for patients arriving to the Emergency Department (ED) with symptoms concerning for STEMI. In response, there has been a creep towards performing ECGs more frequently in triage. The objectives of this study were to quantify the number of triage ECGs performed at our institution, assess the proportion of ECGs performed within current hospital guidelines, and evaluate the rate of STEMI detection in triage ECGs. MethodsA retrospective chart review of all emergency department patients presenting over a period of 8days who had a triage ECG performed. Cases of bradycardia or tachycardia were excluded. Data collection included patient demographics, presenting complaint, cardiac risk factors, troponin values, and final diagnosis. Summary statistics are reported in a descriptive manner. ResultsDuring the study period, 538 patients had a triage ECG for possible STEMI with no STEMI identified and 16 NSTEMI diagnoses (confirmed as positive troponins following ED assessment). Sixty-three (11.7%) patients did not meet internal criteria for a triage ECG. A NSTEMI ED diagnosis was identified in 3% of patients who met internal triage ECG criteria and 1.6% who did not meet criteria (p=0.29). A cost analysis was performed using an average of 50 STEMI cases diagnosed in our ED per given year. Current institutional ECG billing rates for ECGs performed and interpreted is $125 per ECG, providing an estimated triage ECG charge to detect one STEMI at $54,295. DiscussionThis retrospective study of 538 triage ECG's performed over an 8day period identified no STEMIs and 16 NSTEMIs. A very large number of ECGs were done at triage overall and included patients who do not meet our own hospital criteria. Given the extremely low yield and high associated charges, current guidelines for triage ECG for identifying a possible STEMI should be reviewed.

Telemetry-assisted early detection of STEMI in patients with atypical symptoms by paramedic-performed 12-lead ECG with subsequent cardiological analysis.

ECG is an essential diagnostic tool in patients with acute coronary syndrome. We aimed to determine how many patients presenting with atypical symptoms for an acute myocardial infarction show ST-segment elevations on prehospital ECG. We also aimed to study the feasibility of telemetric-assisted prehospital ECG analysis. Between April 2010 and February 2011, consecutive emergency patients presenting with atypical symptoms such as nausea, vomiting, atypical chest pain, palpitations, hypertension, syncope, or dizziness were included in the study. After basic measures were completed, a 12-lead ECG was written and telemetrically transmitted to the cardiac center, where it was analyzed by attending physicians. Any identification of an ST-elevation myocardial infarction resulted in patient admission at the closest coronary angiography facility. A total of 313 emergency patients presented with the following symptoms: dyspnea, nausea, vomiting, dizziness/collapse, or acute hypertension. Thirty-four (11%) patients of this cohort were found to show ST-segment elevations on the 12-lead ECG. These patients were directly admitted to the closest coronary catheterization facility rather than the closest hospital. The time required for transmission and analysis of the ECG was 3.6±1.2 min. Telemetry-assisted 12-lead ECG analysis in a prehospital setting may lead to earlier detection of ST-elevation myocardial infarction in patients with atypical symptoms. Thus, a 12-lead ECG should be considered in all prehospital patients both with typical and atypical symptoms.

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Introduction: The Medical Emergency Team (MET) was developed in response to a need for emergent advanced assessment skills outside of the ICU. The MET nurse provides immediate bedside expertise to manage patients experiencing sudden clinical changes. Timely intervention and crisis prevention are key principles. Methods: Budgetary constraints led us to build our MET from an elite team of experienced nurses within the Medical Intensive Care Unit. All MET nurses have Advanced Cardiac Life Support (ACLS) certification and are trained in the Fundamentals of Critical Care Support (FCCS). Around the clock coverage is provided seven days a week. Results: These changes drove a 55% increase in the volume of MET calls. Preliminary data with the implementation of a dedicated MET nurse has indicated a 26.9% decrease in codes outside of the ICU and an 18% decrease in transfers back to the ICU post-MET calls. In addition, our hospital had a considerably longer door to balloon time (D2B) for inpatient cases of acute ST-elevation myocardial infarction (STEMI) than those presenting to the Emergency Department (ED). The Centers for Medicare and Medicaid Services set a goal D2B time of 90 minutes or less for patients presenting through the ED. An increase in early inpatient STEMI detection and rapid transfer to the cardiac catheterization lab has reduced symptom to balloon time by 31%, from 88 minutes to 61 minutes. Secondary benefits of the dedicated MET nurse have been improved communication and collegiality among care providers, and more robust staff education. Conclusions: These changes drove a 55% increase in the volume of MET calls. Preliminary data with the implementation of a dedicated MET nurse has indicated a 26.9% decrease in codes outside of the ICU and an 18% decrease in transfers back to the ICU post-MET calls.

An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction

BackgroundPre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI.MethodsFive hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care were prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not) and the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty register. The CCU physician's real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not) were registered for comparison.ResultsThe ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and 99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN's receiver operating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute PCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were theoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by 64% without missing any case with STEMI or a need of immediate PCI.ConclusionsOur ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs, and has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.