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.

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.

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.

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.

12-Lead ECG interpretation by database comparison.

BACKGROUND: Automated ECG interpretation is most often a rule-based expert system, though experts may disagree on the exact ECG criteria. One method to automate ECG analysis while indirectly using varied sets of expert rules is to base the automated interpretation on similar ECGs that already have a physician interpretation. The aim of this study is to develop and test an ECG interpretation algorithm based on such similar ECGs. METHODS: The study database consists of approximately 146,000 sequential 12-lead 10 s ECGs taken over the course of three years from a single hospital. All patient ECGs were included. Computer interpretation was corrected by physicians as part of standard care. The ECG algorithm developed here consisted of an ECG similarity search along with a method for estimating the interpretation from a small set of similar ECGs. A second level of differential diagnosis differentiated ECG categories with substantial similarity, such as LVH and LBBB. Interpretation performance was tested by ROC analysis including sensitivity (SE), specificity (SP), positive predictive value (PPV) and area under the ROC curve (AUC). RESULTS: LBBB was the category with the best ECG interpretation performance with an AUC of 0.981 while RBBB, LAFB and ventricular paced rhythm also had an AUC at 0.95 or above. AUC was 0.9 and above for the ischemic repolarization abnormality, LVH, old anterior MI, and early repolarization categories. All other morphology categories had an AUC over 0.8. CONCLUSION: ECG interpretation by analysis of ECG similarity provides adequate ECG interpretation performance on an unselected database using only strategies to weight the interpretation from those similar ECGs. Although this algorithm may not be ready to replace rule-based computer ECG analysis, it may be a useful adjunct recommender.

The algorithmic performance of J-Tpeak for drug safety clinical trial.

INTRODUCTION: The interval from J-point to T-wave peak (JTp) in ECG is a new biomarker able to identify drugs that prolong the QT interval but have different ion channel effects. If JTp is not prolonged, the prolonged QT may be associated with multi ion channel block that may have low torsade de pointes risk. From the automatic ECG measurement perspective, accurate and repeatable measurement of JTp involves different challenges than QT. We evaluated algorithm performance and JTp challenges using the Philips DXL diagnostic 12/16/18-lead algorithm. Measurement of JTp represents a different use model. Standard use of corrected QT interval is clinical risk assessment on patients with cardiac disease or suspicion of heart disease. Drug safety trials involve a very different population - young healthy subjects - who commonly have J-waves, notches and slurs. Drug effects include difficult and unusual morphology such as flat T-waves, gentle notches, and multiple T-wave peaks. METHODS: The JTp initiative study provided ECGs collected from 22 young subjects (11 males and females) in randomized testing of dofetilide, quinidine, ranolazine, verapamil and placebo. We compare the JTp intervals between DXL algorithm and the FDA published measurements. The lead wise, vector-magnitude (VM), root-mean-square (RMS) and principal-component-analysis (PCA) representative beats were used to measure JTp and QT intervals. We also implemented four different methods for T peak detection for comparison. RESULTS: We found that JTp measurements were closer to the reference for combined leads RMS and PCA than individual leads. Differences in J-point location led to part of the JTp measurement difference because of the high prevalence of J-waves, notches and slurs. Larger differences were noted for drug effect causing multiple distinct T-wave peaks (Tp). The automated algorithm chooses the later peak while the reference was the earlier peak. Choosing among different algorithmic strategies in T peak measurement results in the tradeoff between stability and the accurate detection of calcium or sodium channel block. CONCLUSION: Measurement of JTp has different challenges than QT measurement. JTp measurement accuracy improved with combined leads RMS and PCA over lead II or V5.

Intelligent use of advanced capabilities of diagnostic ECG algorithms in a monitoring environment.

A large number of ST-elevation notifications are generated by cardiac monitoring systems, but only a fraction of them is related to the critical condition known as ST-segment elevation myocardial infarction (STEMI) in which the blockage of coronary artery causes ST-segment elevation. Confounders such as acute pericarditis and benign early repolarization create electrocardiographic patterns mimicking STEMI but usually do not benefit from a real-time notification. A STEMI screening algorithm able to recognize those confounders utilizing capabilities of diagnostic ECG algorithms in variation analysis of ST segments helps to avoid triggering a non-actionable ST-elevation notification. However, diagnostic algorithms are generally designed to analyze short ECG snapshots collected in low-noise resting position and hence are susceptible to high levels of noise common in a monitoring environment. We developed a STEMI screening algorithm which performs a real-time signal quality evaluation on the ECG waveform to select the segments with quality high enough for subsequent analysis by a diagnostic ECG algorithm. The STEMI notifications generated by this multi-stage STEMI screening algorithm are significantly fewer than ST-elevation notifications generated by a continuous ST monitoring strategy.

Brain penetrant liver X receptor (LXR) modulators based on a 2,4,5,6-tetrahydropyrrolo[3,4-c]pyrazole core.

Liver X receptor (LXR) agonists have been reported to lower brain amyloid beta (Aß) and thus to have potential for the treatment of Alzheimer's disease. Structure and property based design led to the discovery of a series of orally bioavailable, brain penetrant LXR agonists. Oral administration of compound 18 to rats resulted in significant upregulation of the expression of the LXR target gene ABCA1 in brain tissue, but no significant effect on Aß levels was detected.

Identification of exercise-induced ischemia using QRS slopes.

In this work we studied a computer-aided approach using QRS slopes as unconventional ECG features to identify the exercise-induced ischemia during exercise stress testing and demonstrated that the performance is comparable to the experts' manual analysis using standard criteria involving ST-segment depression. We evaluated the performance of our algorithm using a database including 927 patients undergoing exercise stress tests and simultaneously collecting the ECG recordings and SPECT results. High resolution 12-lead ECG recordings were collected continuously throughout the rest, exercise, and recovery phases. Patients in the database were classified into three categories of moderate/severe ischemia, mild ischemia, and normal according to the differences in sum of the individual segment scores for the rest and stress SPECT images. Philips DXL 16-lead diagnostic algorithm was run on all 10-s segments of 12-lead ECG recordings for each patient to acquire the representative beats, ECG fiducial points from the representative beats, and other ECG parameters. The QRS slopes were extracted for each lead from the averaged representative beats and the leads with highest classification power were selected. We employed linear discriminant analysis and measured the performance using 10-fold cross-validation. Comparable performance of this method to the conventional ST-segment analysis exhibits the classification power of QRS slopes as unconventional ECG parameters contributing to improved identification of exercise-induced ischemia.

Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

BACKGROUND: With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence. METHODS: Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n=772) consisted of VPE ECGs (n=37), normal ECGs (n=492) and a high concentration of conduction defects, RBBB (n=232) and LBBB (n=11). The testing database was a random sample (n=763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database. RESULTS: The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%. CONCLUSION: This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population.

Agreement between cardiologists and fellows in interpretation of ischemic electrocardiographic changes in acute myocardial infarction.

BACKGROUND: Time from symptom onset may not be the best indicator for choosing reperfusion therapy for patients presenting with acute ST-elevation myocardial infarction (STEMI); consequently ECG-based methods have been developed. METHODS: This study evaluated the inter-observer agreement between experienced cardiologists and junior doctors in identifying the ECG findings of the pre-infarction syndrome (PIS) and evolving myocardial infarction (EMI). The ECGs of 353 STEMI patients were independently analyzed by two cardiologists, one fellow in cardiology, one fellow in internal medicine and a medical student. The last two were given a half-hour introduction of the PIS/EMI-algorithm. RESULTS: The inter-observer reliability between all the investigators was found to be good according to kappa statistics (κ 0.632-0.790) for the whole study population. When divided into different subgroups, the inter-observer agreements were from good to very good between the cardiologists and the fellow in cardiology (κ 0.652 -0.813) and from moderate to good (κ 0.464-0.784) between the fellow in internal medicine, medical student and the others. CONCLUSIONS: The PIS and EMI ECG patterns are reliably identified by experienced cardiologists and can be easily adopted by junior doctors.

Detection of culprit coronary lesion location in pre-hospital 12-lead ECG.

BACKGROUND: Pre-hospital 12-lead ECG interpretation is important because pre-hospital activation of the coronary catheterization laboratory reduces ST-segment elevation myocardial infarction (STEMI) discovery-to-treatment time. In addition, some ECG features indicate higher risk in STEMI such as proximal left anterior descending (LAD) culprit lesion location. The challenging nature of the pre-hospital environment can lead to noisier ECGs which make automated STEMI detection difficult. We describe an automated system to classify lesion location as proximal LAD, LAD, right coronary artery (RCA) and left circumflex (LCx) and test the performance on pre-hospital 12-lead ECG. METHODS: The overall classifier was designed from three linked classifiers to separate LAD from non-LAD (RCA or LCx) in the first step, RCA from LCx in a second classifier and proximal from non-proximal LAD in the third classifier. The proximal LAD classifier was designed for high specificity because the output may be used in the decision to modify treatment. The LCx classifier was designed for high specificity because RCA is dominant in most people. The system was trained on a set of emergency department ECGs (n=181) and tested on a set of pre-hospital ECGs (n=80). Both sets were based on a sequential sample starting with symptoms suggesting acute coronary syndromes. Culprit lesion location was determined from coronary catheterization laboratory reports. Inclusion criteria included STEMI interpretation by computer and culprit lesion with 70% or more narrowing. Algorithm accuracy was measured on the test set by sensitivity (SE), specificity (SP), and positive predictive value (PPV). RESULTS: SE, SP and PPV were 50, 100 and 100% respectively for proximal LAD lesion location; 90, 100 and 100% for all LAD; 98, 72 and 78% for RCA; and 50, 98 and 90% for LCx. Specificity and PPV were high for proximal LAD, LAD and LCx. Specificity and PPV are not as high for RCA by design since the RCA-LCx tradeoff favors high specificity in LCx. CONCLUSION: Although our test database is not large, algorithm performance suggests culprit lesion location can be reliably determined from pre-hospital ECG. Further research is needed however to evaluate the impact of automated culprit lesion location on patient treatment and outcomes.

Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations.

BACKGROUND: ECG cable interchange can generate erroneous diagnoses. For algorithms detecting ECG cable interchange, high specificity is required to maintain a low total false positive rate because the prevalence of interchange is low. In this study, we propose and evaluate an improved algorithm for automatic detection and classification of ECG cable interchange. METHOD: The algorithm was developed by using both ECG morphology information and redundancy information. ECG morphology features included QRS-T and P-wave amplitude, frontal axis and clockwise vector loop rotation. The redundancy features were derived based on the EASI™ lead system transformation. The classification was implemented using linear support vector machine. The development database came from multiple sources including both normal subjects and cardiac patients. An independent database was used to test the algorithm performance. Common cable interchanges were simulated by swapping either limb cables or precordial cables. RESULTS: For the whole validation database, the overall sensitivity and specificity for detecting precordial cable interchange were 56.5% and 99.9%, and the sensitivity and specificity for detecting limb cable interchange (excluding left arm-left leg interchange) were 93.8% and 99.9%. Defining precordial cable interchange or limb cable interchange as a single positive event, the total false positive rate was 0.7%. When the algorithm was designed for higher sensitivity, the sensitivity for detecting precordial cable interchange increased to 74.6% and the total false positive rate increased to 2.7%, while the sensitivity for detecting limb cable interchange was maintained at 93.8%. The low total false positive rate was maintained at 0.6% for the more abnormal subset of the validation database including only hypertrophy and infarction patients. CONCLUSION: The proposed algorithm can detect and classify ECG cable interchanges with high specificity and low total false positive rate, at the cost of decreased sensitivity for certain precordial cable interchanges. The algorithm could also be configured for higher sensitivity for different applications where a lower specificity can be tolerated.

Race- and sex-associated differences in rate-adjusted QT, QTpeak, ST elevation and other regional measures of repolarization: the Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: Data are limited about race-and sex-associated differences in prognostically important ECG measures of regional repolarization. METHODS AND RESULTS: The normal reference group from the Atherosclerosis Risk in Communities (ARIC) study included 8,676 white and African-American men and women aged 40-65 years. Exclusion criteria included cardiovascular disease, hypertension, diabetes and major ECG abnormalities. Notable sex differences (p<0.001) were observed in the upper 98% limits for rate-adjusted QTend (QTea) which was 435 ms in white and African-American men and 445 ms in white and African-American women, and for left ventricular epicardial repolarization time (RTepi) which was 345 ms in white and African-American men and 465 ms in white and African-American women. These sex differences reflect earlier onset and end of repolarization in men than in women. Upper normal limits for STJ amplitude in V2-V3 were 100 µV in white and African-American women, 150 µV in white men and 200 µV in African-American men (p<0.001 for sex differences), and for other chest leads, aVL and aVF 50 µV in white women, 100 µV in African-American women, 100 µV in white men and 150 µV in African-American men (p<0.001 for sex and race differences). CONCLUSIONS: Shorter QTea and RTepi in men than in women reflect earlier onset and end of repolarization in men. STJ amplitudes in African-American men were higher than in other subgroups by race and sex. These sex and race differences need to be considered in clinical and epidemiological applications of normal standards.

New ST-segment elevation myocardial infarction criteria for left bundle branch block based on QRS area.

BACKGROUND: ECG detection of ST-segment elevation myocardial infarction (STEMI) in the presence of left bundle-branch block (LBBB) is challenging due to ST deviation from the altered conduction. The purpose of this study was to introduce a new algorithm for STEMI detection in LBBB and compare the performance to three existing algorithms. METHODS: Source data of the study group (143 with acute MI and 239 controls) comes from multiple sources. ECGs were selected by computer interpretation of LBBB. Acute MI reference was hospital discharge diagnosis. Automated measurements came from the Philips DXL algorithm. Three existing algorithms were compared, (1) Sgarbossa criteria, (2) Selvester 10% RS criteria and (3) Smith 25% S-wave criteria. The new algorithm uses an ST threshold based on QRS area. All algorithms share the concordant ST elevation and anterior ST depression criteria from the Sgarbossa score. The difference is in the threshold for discordant ST elevation. The Sgarbossa, Selvester, Smith and Philips discordant ST elevation criteria are (1) ST elevation ≥ 500 µV, (2) ST elevation ≥ 10% of |S|-|R| plus STEMI limits, (3) ST elevation ≥ 25% of the S-wave amplitude and (4) ST elevation ≥ 100 µV + 1050 µV/Ash * QRS area. The Smith S-wave and Philips QRS area criteria were tested using both a single and 2 lead requirement. Algorithm performance was measured by sensitivity, specificity, and positive likelihood ratio (LR+). RESULTS: Algorithm performance can be organized in bands of similar sensitivity and specificity ranging from Sgarbossa score ≥ 3 with the lowest sensitivity and highest specificity, 13.3% and 97.9%, to the Selvester 10% rule with the highest sensitivity and lower specificity of 30.1% and 93.2%. The Smith S-wave and Philips QRS area algorithms were in the middle band with sensitivity and specificity of (20.3%, 94.9%) and (23.8%, 95.8%) respectively. CONCLUSION: As can be seen from the difference between Sgarbossa score ≥ 3 and other algorithms for STEMI in LBBB, a discordant ST elevation criterion improves the sensitivity for detection but also results in a drop in specificity. For applications of automated STEMI detection that require higher sensitivity, the Selvester algorithm is better. For applications that require a low false positive rate such as relying on the algorithm for pre-hospital activation of cardiac catheterization laboratory for urgent PCI, it may be better to use the 2 lead Philips QRS area or Smith 25% S-wave algorithm.

Normal standards for computer-ECG programs for prognostically and diagnostically important ECG variables derived from a large ethnically diverse female cohort: the Women's Health Initiative (WHI).

BACKGROUND: Substantial new information has emerged recently about the prognostic value for a variety of new ECG variables. The objective of the present study was to establish reference standards for these novel risk predictors in a large, ethnically diverse cohort of healthy women from the Women's Health Initiative (WHI) study. METHODS AND RESULTS: The study population consisted of 36,299 healthy women. Racial differences in rate-adjusted QT end (QT(ea)) and QT peak (QT(pa)) intervals as linear functions of RR were small, leading to the conclusion that 450 and 390 ms are applicable as thresholds for prolonged and shortened QT(ea) and similarly, 365 and 295 ms for prolonged and shortened QT(pa), respectively. As a threshold for increased dispersion of global repolarization (T(peak)T(end) interval), 110 ms was established for white and Hispanic women and 120 ms for African-American and Asian women. ST elevation and depression values for the monitoring leads of each person with limb electrodes at Mason-Likar positions and chest leads at level of V1 and V2 were first computed from standard leads using lead transformation coefficients derived from 892 body surface maps, and subsequently normal standards were determined for the monitoring leads, including vessel-specific bipolar left anterior descending, left circumflex artery and right coronary artery leads. The results support the choice 150 µV as a tentative threshold for abnormal ST-onset elevation for all monitoring leads. Body mass index (BMI) had a profound effect on Cornell voltage and Sokolow-Lyon voltage in all racial groups and their utility for left ventricular hypertrophy classification remains open. CONCLUSIONS: Common thresholds for all racial groups are applicable for QT(ea), and QT(pa) intervals and ST elevation. Race-specific normal standards are required for many other ECG parameters.

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.

Automated serial ECG comparison improves computerized interpretation of 12-lead ECG.

BACKGROUND: Interpretation of a patient's 12-lead ECG frequently involves comparison to a previously recorded ECG. Automated serial ECG comparison can be helpful not only to note significant ECG changes but also to improve the single-ECG interpretation. Corrections from the previous ECG are carried forward by the serial comparison algorithm when measurements do not change significantly. METHODS: A sample of patients from three hospitals was collected with two or more 12-lead ECGs from each patient. There were 233 serial comparisons from 143 patients. 41% of patients had two ECGs and 59% of patients had more than two ECGs. ECGs were taken from a difficult population as measured by ECG abnormalities, 197/233 abnormal, 11/233 borderline, 14/233 otherwise-normal and 11/233 normal. ECGs were processed with the Philips DXL algorithm and then in time order for each patient with the Philips serial comparison algorithm. To measure accuracy of interpretation and serial change, an expert cardiologist corrected the ECGs in stages. The first ECG was corrected and used as the reference for the second ECG. The second ECG was then corrected and used as the reference for the third ECG and so on. At each stage, the serial comparison algorithm compared an unedited ECG to an earlier edited ECG. Interpretation accuracy was measured by comparing the algorithm to the cardiologist on a statement by statement basis. The effect of serial comparison was measured by the sum of interpretive statement mismatches between the algorithm and cardiologist. Statement mismatches were measured in two ways, (1) exact match and (2) match within the same diagnostic category. RESULTS: The cardiologist used 910 statements over 233 ECGs for an average number of 3.9 statements per ECG and a mode of 4 statements. When automated serial comparison was used, the total number of exact statement mismatches decreased by 29% and the total same-category statement mismatches decreased by 47%. CONCLUSION: Automated serial comparison improves interpretation accuracy in addition to its main role of noting differences between ECGs.

Automated discrimination of proximal right coronary artery occlusion from middle-to-distal right coronary artery occlusion and left circumflex occlusion in ST-elevation myocardial infarction.

BACKGROUND: Classifying the location of an occlusion in the culprit artery during ST-elevation myocardial infarction (STEMI) is important for risk stratification to optimize treatment. We developed a new logistic regression (LR) algorithm for 3-group classification of occlusion location as proximal right coronary artery (RCA), middle-to-distal RCA or left circumflex (LCx) coronary artery with inferior myocardial infarction. We compared the performance of the new LR algorithm with the recently introduced decision tree classifier of Fiol et al (Ann Noninvasive Electrocardiol. 2004;4:383-388) in the classification of the same 3 categories. METHODS: The new algorithm was developed on a set of electrocardiograms from an emergency department setting (n = 64) and tested on a different set from a prehospital setting (n = 68). All patients met the current STEMI criteria with angiographic confirmation of culprit artery and occlusion location. Using LR, 4 ST-segment deviation features were chosen by forward stepwise selection. Final LR coefficients were obtained by averaging more than 200 bootstrap iterations on the training set. In addition, a separate 4-feature classifier was designed adding ST features of V4R and V8, only available in the training set. RESULTS: The LR algorithm classified proximal RCA occlusion vs combined LCx occlusion and middle-to-distal RCA occlusion, with a sensitivity of 76% and specificity of 81% as compared with 71% and 62% for the Fiol classifier. The difference in specificity was statistically significant. The LR classifier trained with additional ST features of V4R and V8, but still limited to 4, improved the overall agreement in the training set from 65% to 70%. CONCLUSION: Discrimination of proximal RCA lesion location from LCx or middle-to-distal RCA using the new LR classifier shows improvement over decision tree-type classification criteria. Automated identification of proximal RCA occlusion could speed up the risk stratification of patients with STEMI. The addition of leads V4R and V8 should further improve the automated classification of the occlusion site in RCA and LCx.