Smith, Stephen W.

Abstract Objectives Understanding whether the novel long-T ITC complex might assist in interpreting clinical prognostic utility compared to established cardiac biomarkers is needed. We determined long term major adverse cardiac events (MACE) and all-cause mortality (ACM) predicated on novel long-T ITC complex and high sensitivity cardiac troponin I (hs-cTnI) and T (hs-cTnT) assays in the emergency department (ED). Methods cTn assays were measured on the Mindray CL-1200i platform at presentation in consecutive patients enrolled in ‘Mindray hs-cTnI Assay Analytical and Clinical Evaluation for the Diagnosis and RIsk Assessment of Myocardial InfarctIon’ (MERITnI) (NCT05853042) trial with suspected ischemia. Sex specific 99th percentiles upper reference limits (URLs) were: hs-cTnI female 14 ng/L, male 15 ng/L; hs-cTnT female 10 ng/L, male 19 ng/L; long-T ITC complex 0.9 ng/L, same for female and male. Results Of 1,070 patients, 59.4 % were male, 43.4 % White, 45.5 % Black, and 39.5 % ≥65 years. Independent of biomarkers, a continuum of risk over 1 year showed MACE increased to 16.1 % and ACM to 16.5 %. Long-T ITC complex did not show a continuum change, increasing only 0.1 % (HR: 1.001 (1.0–1.001) per ng/L. However, hs-cTnI increased 1.8 % (HR: 1.018 [1.009–1.027] for each ng/L (p<0.0001) and hs-cTnT increased 0.3 % per ng/L (HR: 1.003 [1.001–1.006]) (p=0.0028). Between subjects ≤URLs and >URLs, MACE and ACM all biomarkers showed significant (p<0.0001) differences over 1 year. For MACE in those >URL long-T ITC complex increased 11.2–25.2 %, hs-cTnI 18.0–35.0 % and hs-cTnT 8.9–24.2 %. For ACM in those >URL long-T ITC complex increased 4.5–23.3 %, hs-cTnI 5.0–26.2 %, and hs-cTnT 3.6–24.1 %. Conclusions Novel long-T ITC complex did not provide new prognostic value to either hs-cTnI or hs-cTnT. Patients with increased hs-cTnI and hs-cTnT concentrations had marked increases in MACE and ACM risk at 1-year.

Background: Gaps in ECG interpretation competency among medical professionals exist, with a need for practical, evidence-based, and accessible learning solutions. We conducted an international, prospective, randomized controlled trial to assess the effectiveness of web-based, self-directed ECG learning. Methods: A total of 1206 diverse medical learners and professionals underwent an initial test, followed by random assignment to one of four groups: (i) online question-bank (questions), (ii) online lectures (lectures), (iii) online, question-bank and lectures (hybrid), or (iv) no resources (control). After 4 months, a post-test was administered to evaluate the overall change in performance. Results: Of all participants, 863 (72%) completed the trial. After the 4-month follow-up, all three online, self-directed intervention groups (questions, lectures, and hybrid) showed significant improvement in overall performance (P<0.001), while the control group did not (P=0.544). The questions, lectures, and hybrid interventions demonstrated score improvements of +11.4% (95% CI, 9.1-13.7; P<0.001), +9.8% (95% CI, 7.8-11.9; P<0.001), and +11.0% (95% CI, 9.2-12.9; P<0.001) respectively, compared to +0.8% (95% CI, -1.2-2.8; P=0.544) for the control group. These improvements were consistent across medical professional groups (Figure 1) . Conclusions: Web-based, self-directed learning resources effectively improved ECG interpretation proficiency for diverse medical learners and professionals.

Background: ECG interpretation is crucial in medical practice, but professionals struggle with achieving and maintaining competency. Identifying proficiency gaps aids in designing educational interventions. Methods: Medical professionals from various disciplines interpreted 30 12-lead ECGs containing commonly taught urgent and non-urgent findings. Performance metrics evaluated were overall adjusted score (% of correctly identified findings using a point-value score based on clinical relevance), unadjusted score (% of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on an ordinal scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FIT), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, 249 (21%) allied health professionals, 571 (47%) physicians, and 453 (38%) non-physicians. Participants had a mean adjusted score of 46.9% (± 15.9%), unadjusted score of 56.4% (± 17.2%), interpretation time of 142 seconds (± 67 seconds), and confidence of 0.83 (± 0.53) (Table 1) . Performance varied across groups; cardiology FIT had superior performance in all metrics. Physicians generally outperformed non-physicians, with higher overall adjusted score (52% vs. 43%; p<0.01), unadjusted score (62% vs. 52%; p<0.01), and confidence (0.91 vs. 0.80; p<0.01). Unadjusted score for PCPs was higher than nurses and APPs (58% vs. 47% and 51%; p<0.01) but lower than resident physicians (58% vs. 60%; p<0.01). Allied health professionals outperformed nurses and APPs and closely matched resident physicians and PCPs. Conclusions: Significant gaps in ECG interpretation proficiency exist, emphasizing the need for comprehensive, scalable, and accessible educational tools.

Abstract Background The incidence and impact of acute infection among patients with type 1 (T1MI) and 2 myocardial infarction (T2MI) is not well known. Inflammation and increased procoagulant activity can lead to T1MI, whereas cytokine release can cause hemodynamic alterations affecting myocardial oxygen consumption and contribute to T2MI. Purpose Determine the incidence and prognostic impact of acute infection among patients with T1MI and T2MI. Methods Post-hoc analysis of UTROPIA (NCT02060760), a prospective, observational cohort study involving 1,640 consecutive emergency department patients with serial cardiac troponin (cTn) I measurements obtained on clinical indication. The incidence of acute infection, antibiotic use, and/or laboratory evidence of infection were examined among patients with T1MI and T2MI. 5-year cumulative survival curves were plotted using the Kaplan-Meier method, frequencies were compared via Chi-Square, and a multivariate cox-proportional hazards model was used to assess the impact of infection on mortality. Results Among 217 patients with acute MI (T1MI, n=77 and T2MI, n=140), acute infection occurred in 63 (29%) of patients. Acute infection was more common in patients with T2MI than T1MI (35% vs. 18%, p=0.009). The predominant source of infection was pneumonia (59%), followed by urinary tract infections (11%). Patients with T2MI were also more likely than T1MI to have sepsis (11% vs. 1%, p=0.012), bacteremia (30% vs. 18%, p=0.057), and antibiotic use (35% vs. 17%, p=0.005). At 5-years, patients with acute MI and acute infection had a higher mortality rate than patients without infection (49% vs. 25%, p=0.0006) (Figure). Among the 31 deaths in MI patients with acute infection, most deaths were non-cardiac (65%) and occurred in those with T2MI (77%). Following adjustment for age, sex, and comorbidities in a Cox proportional hazards model, acute infection was an independent predictor of death (hazard ratio: 2.2, 95% CI: 1.3–3.5, p=0.0016). Conclusion Acute infection occurs in almost a third of patients that are diagnosed with acute MI during the index hospitalization. Most infections are due to pneumonia and occur most often in patients classified as having T2MI. Acute infection is an independent risk factor for 5-year mortality, with nearly half of all patients with acute MI in whom concomitant acute infection is present during the index hospitalization dead at 5-years. Most deaths are non-cardiac and the vast majority occur in patients with T2MI. Acknowledgement/Funding Abbott Diagnostics and Hennepin Healthcare Research Institute (formerly MMRF)e

Abstract Background Few studies address long-term outcomes among patients with type 2 myocardial infarction (T2MI). It has been suggested that patients with T2MI with concomitant stable ischemic heart disease are at higher-risk. Purpose Our goals were two-fold. First, to examine 5-year mortality rates and mode of death among patients with type 1 (T1MI) and T2MI. Second, to evaluate the prognostic impact of coronary artery disease (CAD) on long-term outcomes among patients with T2MI. Methods Post-hoc analysis of UTROPIA (NCT02060760), a prospective, observational cohort study involving 1,640 consecutive emergency department patients with serial cardiac troponin (cTn) I measurements obtained on clinical indication. 5-year death rates, including mode of death, were examined among patients with T1MI and T2MI. CAD was defined as the presence of any of the following: history of CAD, prior MI, prior coronary artery bypass graft surgery, prior percutaneous coronary intervention, prior coronary stenting, or documented CAD (≥50%) on index admission angiography. Results Among 1640 patients, acute MI was diagnosed in 217 patients, including 77 (4.7%) classified as T1MI and 140 (8.5%) as T2MI. At 5-years, 23% (n=18) of patients with T1MI had died, with 61% (n=11) categorized as cardiac deaths. For T2MI, 37% (n=52) had died at 5-years, with 36% (n=19) categorized as cardiac deaths. Patients with T2MI had a higher 5-year mortality than T1MI (37% vs 23%, p=0.038) (Figure, Panel A), with T2MI more likely than T1MI to die from non-cardiac causes (64% vs. 39%, p=0.04). CAD was present in 22% (n=31) of patients with T2MI and associated with worse outcomes (p=0.0496) (Figure, Panel B). Conclusion T2MI has worse long term outcomes than T1MI. The predominant mode of death among patients with T2MI is non-cardiac. However, approximately 40% of deaths are cardiac. Further, we confirm the presence of CAD identifies a subset of patients with T2MI at high-risk. Acknowledgement/Funding Abbott Diagnostics and Hennepin Healthcare Research Institute (formerly MMRF)

Background: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. Methods: A machine learning algorithm (myocardial-ischemic-injury-index [MI 3 ]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI 3 uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI 3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Results: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI 3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI 3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI 3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]). Conclusions: Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. Clinical Trial Registration: URL: https://www.anzctr.org.au . Unique identifier: ACTRN12616001441404.