Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus

Abstract Background Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool—the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM. Methods Participants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF. Results SBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787–0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44–0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56–0.79, p = 0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73–0.74), reduced GLS (AUC 0.76, 95% CI 0.73–0.74) and LVH (AUC 0.90, 95% CI 0.88–0.89). Conclusions Machine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis. Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 1261700011632

Has invasive management for acute coronary syndromes become more 'risk-appropriate': Pooled results of five Australian registries

Background: Despite being recommended in acute coronary syndrome (ACS) guidelines, the use of invasive management within specific risk groups continues to be debated. This study examines the change in the use of invasive management in ACS by patient risk and the associated change in mortality within Australia over the last 17 years. Methods: Pooled cohorts derived from five ACS registries (ACACIA, CONCORDANCE, GRACE, Snapshot-ACS, and Predict) spanned from 1999 to 2015. After excluding patients without a final diagnosis of ACS (n = 4460), enrolled outside Australia (n = 1477) and without an enrolling year (n = 4), 15 912 patients were analysed. Data was stratified across three time periods (1999–2004, 2005–2009, and 2010–2015) using clinical risk characteristics (age, ACS diagnosis, biomarker elevation, and GRACE score) to monitor change in practice. Results: Over the 17-year period, the use of invasive management increased (4073/6863 (59.3%) cases [1999–2009] vs. 6670/8706 (76.6%) cases [2010–2015]). Invasive management accounted for improvements in mortality in intermediate- and high-risk groups (intermediate risk: 14% (95% CI 1–66%) [1999–2009] vs. 49% (95% CI 2–59%) [2010–2015]; high risk: 24% (95% CI 6–42%) [1999–2009] vs. 48% (95% CI 19–76%) [2010–2015]). Patients receiving no angiography compared with interventional management had worse outcomes (1999–2004 1.55 HR [95% CI 1.36–1.80], P < 0.0001 vs. 2010–2015 1.90 HR [95% CI 1.45–2.51], P < 0.0001). Conclusions: Clinical practice in ACS has changed over the last 17 years with positive outcomes seen with invasive management among high-risk patients. Unfortunately, a considerable burden of mortality remains in patients managed medically, highlighting a need for more focused strategies that improve care and outcomes in this group

Rational clinical evaluation of suspected acute coronary syndromes: The value of more information

Objective: Many meta-analyses have provided synthesised likelihood ratio data to aid clinical decision-making. However, much less has been published on how to safely combine clinical information in practice. We aimed to explore the benefits and risks of pooling clinical information during the ED assessment of suspected acute coronary syndrome. Methods: Clinical information on 1776 patients was collected within a randomised trial conducted across five South Australian EDs between July 2011 and March 2013. Bayes theorem was used to calculate patient-specific post-test probabilities using age- and gender-specific pre-test probabilities and likelihood ratios corresponding to the presence or absence of 18 clinical factors. Model performance was assessed as the presence of adverse cardiac outcomes among patients theoretically discharged at a post-test probability less than 1%. Results: Bayes theorem-based models containing high-sensitivity troponin T (hs-troponin) outperformed models excluding hs-troponin, as well as models utilising TIMI and GRACE scores. In models containing hs-troponin, a plateau in improving discharge safety was observed after the inclusion of four clinical factors. Models with fewer clinical factors better approximated the true event rate, tended to be safer and resulted in a smaller standard deviation in post-test probability estimates. Conclusions: We showed that there is a definable point where additional information becomes uninformative and may actually lead to less certainty. This evidence supports the concept that clinical decision-making in the assessment of suspected acute coronary syndrome should be focused on obtaining the least amount of information that provides the highest benefit for informing the decisions of admission or discharge.</p