Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Impact of treatment on elder-relevant physical function and quality of life outcomes in older adults with metastatic castration-resistant prostate cancer.

INTRODUCTION: Understanding physical function (PF) and quality of life (QoL) treatment effects are important in treatment decision-making for older adults with cancer. However, data are limited for older men with metastatic castration-resistant prostate cancer (mCRPC). We evaluated the effects of treatment on PF and QoL in older men with mCRPC. MATERIALS AND METHODS: Men aged 65+ with mCRPC were enrolled in this multicenter prospective observational study. PF measures included instrumental activities of daily living, grip strength, chair stands, and gait speed. QoL measures included fatigue, pain, mood, and Functional Assessment of Cancer Therapy (FACT)-General total and sub-scale scores. Outcomes were collected at baseline, three, and six months. Linear mixed effects regression models were used to examine PF and QoL differences over time across various treatment cohorts. RESULTS: We enrolled 198 men starting chemotherapy (n = 71), abiraterone (n = 37), enzalutamide (n = 67), or radium-223 (n = 23). At baseline, men starting chemotherapy had worse measures of PF, QoL, pain, and mood than the other groups. Over time, all PF measures remained stable, pain improved, but functional wellbeing (FWB) and mood worsened significantly for all cohorts. However, change over time in all outcomes was not appreciably different between treatment cohorts. Worst-case sensitivity analyses identified attrition (ranging from 22 to 42% by six months) as a major limitation of our study, particularly for the radium-223 cohort. DISCUSSION: FWB and mood were most prone to deterioration over time, whereas pain improved with treatment. Although patients initiating chemotherapy had worse baseline PF and QoL, chemotherapy was not associated with significantly greater worsening over time compared to other common therapies for mCRPC. These findings may assist in treatment discussions with patients. However, given the modest sample size, attrition, and timeframe of follow-up, the impact of treatment on PF and QoL outcomes in this setting requires further study, particularly for radium-223.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

The role of frailty in modifying physical function and quality of life over time in older men with metastatic castration-resistant prostate cancer.

INTRODUCTION: As treatment options for metastatic castration-resistant prostate cancer (mCRPC) expand and its patient population ages, consideration of frailty is increasingly relevant. Using a novel frailty index (FI) and two common frailty screening tools, we examined quality of life (QoL) and physical function (PF) in frail versus non-frail men receiving treatment for mCRPC. MATERIALS AND METHODS: Men aged 65+ starting docetaxel chemotherapy, abiraterone, or enzalutamide for mCRPC were enrolled in a multicenter prospective cohort study. QoL, fatigue, pain, and mood were measured with the Functional Assessment of Cancer Therapy-General scale, the Edmonton Symptom Assessment System tiredness and pain subscales, and the Patient Health Questionnaire-9. PF was evaluated with grip strength, four-meter gait speed, five times Sit-to-Stand Test, and instrumental activities of daily living. Frailty was determined using the Vulnerable Elders Survey (VES-13), the Geriatric 8 (G8), and an FI constructed from 36 variables spanning laboratory abnormalities, geriatric syndromes, functional status, social support, as well as emotional, cognitive, and physical deficits. We categorized patients as non-frail (FI ≤ 0.2, VES < 3, G8 > 14), pre-frail (FI > 0.20, ≤0.35), or frail (FI > 0.35, VES ≥ 3, G8 ≤ 14); assessed correlation between the three tools; and performed linear mixed-effects regression analyses to examine longitudinal differences in outcomes (0, 3, 6 months) by frailty status. A sensitivity analysis with worst-case imputation was conducted to explore attrition. RESULTS: We enrolled 175 men (mean age 74.9 years) starting docetaxel (n = 71), abiraterone (n = 37), or enzalutamide (n = 67). Our FI demonstrated moderate correlation with the VES-13 (r = 0.607, p < 0.001) and the G8 (r = -0.520, p < 0.001). Baseline FI score was associated with worse QoL (p < 0.001), fatigue (p < 0.001), pain (p < 0.001), mood (p < 0.001), PF (p < 0.001), and higher attrition (p < 0.01). Over time, most outcomes remained stable, although pain improved, on average, regardless of frailty status (p = 0.007), while fatigue (p = 0.045) and mood (p = 0.015) improved in frail patients alone. DISCUSSION: Among older men receiving care for mCRPC, frailty may be associated with worse baseline QoL and PF, but over time, frail patients may experience largely similar trends in QoL and PF as their non-frail counterparts. Further study with larger sample size and longer follow-up may help elucidate how best to incorporate frailty into treatment decision-making for mCRPC.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.

STUDY OBJECTIVE: Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis. METHODS: This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis. RESULTS: Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation. CONCLUSION: In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

Improving reliability for measuring ST deviation in suspected acute coronary syndrome.

BACKGROUND: Standard 12­lead ECG is used for diagnosis and risk stratification in suspected acute coronary syndrome (ACS) patients. Artifacts have significant impact on the measuring quality, which consequently affect the diagnostic decision. We used a signal quality indicator (SQI) to identify the ECG segments with lower artifact levels which we hypothesized would improve ST measurements. METHODS: The Staff III 12­lead ECG database was used with the ECG segments before balloon inflation (n = 185). SQI scores per second were calculated and a 10-s ECG segment with least noise and artifacts (Clean10) was identified for each minute of recording. The first 10 s of ECG recordings (First10) for each minute were selected as a reference. The Philips DXL™ algorithm was used to measure the ST levels at J-point, +20 ms, +40 ms, +60 ms, and + 80 ms after the J-point. Standard deviations (SDs) for the ST measurements for each of the 185 ECG records were calculated for the Clean10 and for the First10 across records. The resulting SDs for the Clean10 were compared with the SDs for the First10 using the Wilcoxon signed rank test. RESULTS: The results indicated that 1) The SDs for the Clean10 are lower than that of the First10; 2) The SDs for J+20 ms and J+40 ms are lowest among the 5 different measuring points although similar improvement for the Clean10 over the First10 is observed for J+60 ms and J+80 ms as well; 3) The improvement at the J-point was not as high as other ST measurements. CONCLUSIONS: The SQI is demonstrated as an efficient tool to identify the ECG segments with lower artifacts that produce more consistent and reliable ST measurement. The measurements at J+20 ms demonstrated the highest consistency among the five studied measuring points.

Effective Data Augmentation, Filters, and Automation Techniques for Automatic 12-Lead ECG Classification Using Deep Residual Neural Networks.

Automatic electrocardiogram (ECG) analysis plays a critical role in early detection and diagnosis of cardiac abnormalities and diseases. Data augmentation and automation strategies have been proposed to enhance the robustness of the machine and deep learning model for the classification of cardiac abnormalities. Here we propose 15 data augmentation and 6 filters, and an automation method using an end-to-end deep residual neural network (ResNet) model for automatic cardiac abnormalities detection from 12-lead ECG recordings. We evaluate the effectiveness of data augmentation/filtering and automation techniques using the proposed ResNet-based model on the China Physiological Signal Challenge (CPSC) dataset consisting of 9 diagnostic classes. The average F1 scores across 9 classes on the CPSC dataset trained with three data augmentation (baseline wander addition, dropout, and scaling) and a filter (sigmoid compression) were significantly higher than that without using augmentation/filters (baseline). The highest average F1 score with sigmoid compression method was significantly higher (relative improvement of 2.04 %) than the baseline while horizontal and vertical flipping augmentations were detrimental to the classification performance. Additionally, the results show that the random combination of four selected data augmentation and filter using the modified RandAugment technique provided a significantly higher average F1 score (relative improvement of 2.54 %) compared to the baseline. The proposed data augmentation, filters, and automation techniques provide an effective solution to improve the classification performance of the end-to-end deep learning model from ECG recordings without changing the model hyperparameters and structure.

Big data reveals insights for lead importance in ECG interpretation.

BACKGROUND: Not every lead contributes equally in the interpretation of an ECG. There are some abnormalities in which the lead importance is not clear either from cardiac electrophysiology or experience. Therefore, it is beneficial to develop an algorithm to quantify the lead importance in the reading of ECGs, namely to determine how much to weigh the evidence from each individual lead when interpreting ECG. METHODS: One representative beat per ECG lead was constructed for each ECG in a database. An algorithm was developed to find the top K (K = 1, 5, 10, 20, 50, 100) ECGs in the database that had the most similar morphology to the query ECG, independently for each lead. For each lead, the query ECG was interpreted based on the weighted average voting on the most similar ECGs by applying a variety of thresholds. For each category of abnormality, we found the threshold that maximized the median F1 score of sensitivity and positive predictive value among all ECG leads. Finally, the F1 score of each lead at this chosen threshold was defined as the importance value for that lead. RESULTS: Eighteen morphology-based categories of abnormality were investigated for two databases. For most, the lead importance confirmed what expert ECG readers already know. However, it also revealed new insights. For example, lead aVR appeared in the top 6 most important leads in 11 and 12 categories of abnormality in two databases respectively, and ranked first among 12 leads if summarizing all categories. CONCLUSIONS: Lead importance information may be useful in selecting only the most important leads to screen for a specific abnormality, for example using wearable patches.

Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary syndrome.

BACKGROUND: Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation. The objective of this study was to test the performance of a standard automated algorithm against an additional high specificity setting developed for reducing the false positive STEMI calls. METHODS: We included consecutive patients with an available digital prehospital ECG triaged directly to Cath-lab for acute coronary angiography between 2009 and 2012. An adjudicated discharge diagnosis of STEMI or no myocardial infarction (no-MI) was assigned for each patient. The new automatic algorithm contains a feature to reduce false positive STEMI interpretation. The STEMI performance with the standard setting (STD) and the high specificity setting (HiSpec) was tested against the adjudicated discharge diagnosis in a retrospective manner. RESULTS: In total, 2256 patients with an available digital prehospital ECG (mean age 63 ± 13 years, male gender 71%) were included in the analysis. The discharge diagnosis of STEMI was assigned in 1885 (84%) patients. The STD identified 165 true negative and 1457 true positive (206 false positive and 428 false negative) cases (77.3%, 44.5%, 87.6% and 17.3% for sensitivity, specificity, PPV and NPV, respectively). The HiSpec identified 191 true negative and 1316 true positive (180 false positive and 569 false negative) cases (69.8%, 51.5%, 88.0% and 25.1% for sensitivity, specificity, PPV and NPV, respectively). From STD to HiSpec, false positive cases were reduced by 26 (12,6%), but false negative results were increased by 33%. CONCLUSIONS: Implementing an automated ECG algorithm with a high specificity setting was able to reduce the number of false positive STEMI cases. However, the predictive values for both positive and negative STEMI identification were moderate in this highly selected STEMI population. Finally, due the reduced sensitivity/increased false negatives, a negative AMI statement should not be solely based on the automated ECG statement.

Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome.

BACKGROUND: The 12­lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of "Acute MI" or "Acute Ischemia" crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS). METHODS: An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<<. Two independent reviewers retrospectively adjudicated the study outcomes and disagreements were resolved by a third reviewer. RESULTS: Our study included 2400 patients (age 59 ± 16, 47% females, 41% Black), with 190 (8%) patients with documented automated diagnostic statements of acute MI or acute ischemia. The sensitivity/specificity of the automated algorithm for detecting confirmed STEMI (n = 143, 6%); presence of actionable coronary culprit lesions (n = 258, 11%), myocardial necrosis (n = 291, 12%), or any ACS (n = 378, 16%) were 62.9%/95.6%; 37.2%/95.6%; 38.5%/96.4%; and 30.7%/96.3%, respectively. CONCLUSION: Although being very specific, automated interpretation statements of acute MI/acute ischemia on prehospital ECGs are not satisfactorily sensitive to exclude symptomatic coronary disease. Patients without these automated interpretation statements should be considered further for significant underlying coronary disease based on the clinical context. TRIAL REGISTRATION: ClinicalTrials.gov # NCT04237688.

ECG reading differences demonstrated on two databases.

Many studies that rely on manual ECG interpretation as a reference use multiple ECG expert interpreters and a method to resolve differences between interpreters, reflecting the fact that experts sometimes use different criteria. The aim of this study was to show the effect of manual ECG interpretation style on training automated ECG interpretation. METHODS: The effect of ECG interpretation style or differing ECG criteria on algorithm training was shown in this study by careful analysis of the changes in algorithm performance when the algorithm was trained on one database and tested on a different database. Morphology related ECG interpretation was summarized in eleven abnormalities such as left bundle branch block (LBBB) and old anterior myocardial infarction (MI). Each of the two databases used in the study had a reference interpretation mapped to those eleven abnormalities. F1 algorithm performance scores across abnormalities were compared for four cases. First, the algorithm was trained and tested on randomly split database A and then trained on the training set of database A and tested on randomly chosen test set of database B. The previous two test cases were repeated for opposite databases, train and test on database B and then train on database B and test on the test set of database A. RESULTS: F1 scores across abnormalities were generally higher when training and testing on the same database. F1 scores were high for bundle branch blocks (BBB) no matter the training and testing database combination. Old anterior MI F1 score dropped for one cross-database comparison and not the other suggesting a difference in manual interpretation. CONCLUSION: For some abnormalities, human experts appear to have used different criteria for ECG interpretation, as evident by the difference between cross-database and within-database performance. Bundle branch blocks appear to be interpreted in a consistent manner.

Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.

BACKGROUND: Novel temporal-spatial features of the 12­lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion. METHODS: This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12­lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm). RESULTS: Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance. CONCLUSIONS: Novel computational features of the 12­lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.

Association of Chemotherapy, Enzalutamide, Abiraterone, and Radium 223 With Cognitive Function in Older Men With Metastatic Castration-Resistant Prostate Cancer.

Importance: Older adults are at greater risk of cognitive decline with various oncologic therapies. Some commonly used therapies for advanced prostate cancer, such as enzalutamide, have been linked to cognitive impairment, but published data are scarce, come from single-group studies, or focus on self-reported cognition. Objective: To longitudinally examine the association between cognitive function and docetaxel (chemotherapy), abiraterone, enzalutamide, and radium Ra 223 dichloride (radium 223) in older men with metastatic castration-resistant prostate cancer. Design, Setting, and Participants: A multicenter, prospective, observational cohort study was conducted across 4 academic cancer centers in Ontario, Canada. A consecutive sample of 155 men age 65 years or older with metastatic castration-resistant prostate cancer starting any treatment with docetaxel, abiraterone acetate, enzalutamide, or radium Ra 223 dichloride (radium 223) were enrolled between July 1, 2015, and December 31, 2019. Exposures: First-line chemotherapy (docetaxel), abiraterone, enzalutamide, or radium 223. Main Outcomes and Measures: Cognitive function was measured at baseline and end of treatment using the Montreal Cognitive Assessment, the Trail Making Test part A, and the Trail Making Test part B to assess global cognition, attention, and executive function, respectively. Absolute changes in scores over time were analyzed using univariate and multivariable linear regression, and the percentages of individuals with a decline of 1.5 SDs in each domain were calculated. Results: A total of 155 men starting treatment with docetaxel (n = 51) (mean [SD] age, 73.5 [6.2] years; 34 [66.7%] with some postsecondary education), abiraterone (n = 29) (mean [SD] age, 76.2 [7.2] years; 18 [62.1%] with some postsecondary education), enzalutamide (n = 54) (mean [SD] age, 75.7 [7.4] years; 33 [61.1%] with some postsecondary education), and radium 223 (n = 21) (mean [SD] age, 76.4 [7.2] years; 17 [81.0%] with some postsecondary education) were included. Most patients had stable cognition or slight improvements during treatment. A cognitive decline of 1.5 SDs or more was observed in 0% to 6.5% of patients on each measure of cognitive function (eg, 3 of 46 patients [6.5%; 95% CI, 2.2%-17.5%] in the group receiving chemotherapy [docetaxel] had a decline of 1.5 SDs for Trails A and Trails B). Although patients taking enzalutamide had numerically larger declines than those taking abiraterone, differences were small and clinically unimportant. Conclusions and Relevance: These findings suggest that most older men do not experience significant cognitive decline in attention, executive function, and global cognition while undergoing treatment for advanced prostate cancer regardless of the treatment used.

Examining the ability of the Cancer and Aging Research Group tool to predict toxicity in older men receiving chemotherapy or androgen-receptor-targeted therapy for metastatic castration-resistant prostate cancer.

BACKGROUND: Because multiple treatments are available for metastatic castrate-resistant prostate cancer (mCRPC) and most patients are elderly, the prediction of toxicity risk is important. The Cancer and Aging Research Group (CARG) tool predicts chemotherapy toxicity in older adults with mixed solid tumors, but has not been validated in mCRPC. In this study, its ability to predict toxicity risk with docetaxel chemotherapy (CHEMO) was validated, and its utility was examined in predicting toxicity risk with abiraterone or enzalutamide (A/E) among older adults with mCRPC. METHODS: Men aged 65+ years were enrolled in a prospective observational study at 4 Canadian academic cancer centers. All clinically relevant grade 2 to 5 toxicities over the course of treatment were documented via structured interviews and chart review. Logistic regression was used to identify predictors of toxicity. RESULTS: Seventy-one men starting CHEMO (mean age, 73 years) and 104 men starting A/E (mean age, 76 years) were included. Clinically relevant grade 3+ toxicities occurred in 56% and 37% of CHEMO and A/E patients, respectively. The CARG tool was predictive of grade 3+ toxicities with CHEMO, which occurred in 36%, 67%, and 91% of low, moderate, and high-risk groups (P = .003). Similarly, grade 3+ toxicities occurred among A/E users in 23%, 48%, and 86% with low, moderate, and high CARG risk (P < .001). However, it was not predictive of grade 2 toxicities with either treatment. CONCLUSIONS: There is external validation of the CARG tool in predicting grade 3+ toxicity in older men with mCRPC undergoing CHEMO and demonstrated utility during A/E therapy. This may aid with treatment decision-making.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.

Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome.

BACKGROUND: Non-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. METHODS: We performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. RESULTS: Our final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%-65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R2 = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation time in left ventricular apex were the most important determinants of reduced LVEF, while global QRS duration was of less importance. CONCLUSIONS: Poor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

Detection of strict left bundle branch block by neural network and a method to test detection consistency.

OBJECTIVE: To develop an automatic algorithm to detect strict left bundle branch block (LBBB) on electrocardiograms (ECG) and propose a procedure to test the consistency of neural network detections. APPROACH: The database for the classification of strict LBBB was provided by Telemetric and Holter ECG Warehouse. It contained 10 s ECGs taken from the MADIT-CRT clinical trial. The database was divided into a training dataset (N = 300, strict LBBB = 174, non-strict LBBB = 126) and a test dataset (N = 302, strict LBBB = 156, non-strict LBBB = 146). LBBB-related features were extracted by Philips DXL™ algorithm, selected by a random forest classifier, and fed into a 5-layer neural network (NN) for the classification of strict LBBB on the training dataset. The performance of NN on the test dataset was compared to two random forest classifiers, an algorithm applying strict LBBB criteria, a wavelet-based approach, and a support-vector-machine approach. The consistency of NN's detection was tested on 549 2 min recordings of the PTB diagnostic ECG database. LBBB annotations are not required to measure consistency. MAIN RESULTS: The performance of NN on the test dataset were sensitivity = 91. 7%, specificity = 85.6% and accuracy = 88.7% (PPV = 87.2%, NPV = 90.6%). The consistency score of strict-LBBB and non-strict-LBBB detection was 0.9341 and 0.9973 respectively. CONCLUSION: NN achieved the highest specificity, accuracy, and PPV. Using random forest for feature selection and NN for classification increased interpretability and reduced computational cost. The consistency test showed that NN achieved high consistency scores in the detection of strict LBBB. SIGNIFICANCE: This work proposed an approach for selecting features and training NN for the detection of strict LBBB as well as a consistency test for black-box algorithms.

Outcomes of Multi-disciplinary Management of Metastatic Renal Cell Carcinoma.

Metastatic renal cell carcinoma (mRCC) is associated with a poor prognosis. It is traditionally a treatment-resistant disease necessitating multi-modal treatment and close follow-up. We herein report a case of mRCC in a patient who was managed closely by a multi-disciplinary team and still retained a very good performance status and treatment response three years after diagnosis. We highlight the importance of close monitoring, switching systemic therapies at progression, early palliative radiotherapy, and patient education in controlling disease burden and maintaining quality of life in patients with mRCC.