Left Ventricular Ejection Fraction Class Research Articles

Combining Loop and Thiazide DiureticsAcross the Left Ventricular Ejection Fraction Spectrum: The CLOROTIC Trial.

The addition of hydrochlorothiazide (HCTZ) to furosemide in the CLOROTIC (Combining Loop with Thiazide Diuretics for Decompensated HeartFailure) trial improved the diuretic response in patients with acute heart failure (AHF). This work aimed to evaluate if these results differ across the spectrum of left ventricular ejection fraction (LVEF). This post hoc analysis of the randomized, double-blind, placebo-controlled CLOROTIC trial enrolled 230patients with AHF to receive either HCTZ or a placebo in addition to an intravenous furosemide regimen. The influence of LVEF on primary and secondary outcomes was evaluated. The median LVEF was 55%: 166 (72%) patients had LVEF >40%, and 64 (28%) had LVEF≤40%. Patients with a lower LVEF were younger, more likely to be male, had a higher prevalence of ischemic heart disease, and had higher natriuretic peptide levels. The addition of HCTZ to furosemide was associated with the greatest weight loss at 72of 96 hours, better metrics of diuretic response, and greater 24-hour diuresis compared with placebo, with no significant differences according to the LVEF category (using 2 LVEF cutoff points: 40% and 50%) or LVEF as a continuous variable (all P values were insignificant). There were no significant differences observed with the addition of HCTZ in terms of mortality, rehospitalizations, or safety endpoints (impaired renal function, hyponatremia, and hypokalemia) among the 2 LVEF groups (all P values were insignificant). Adding HCTZ to intravenous furosemide seems to be effective strategy for improving diuretic response in AHF without treatment effect modification according to baseline LVEF. (Combining Loop with Thiazide Diuretics for Decompensated HeartFailure [CLOROTIC], NCT01647932; Randomized, double blinded, multicenter study, to asses Safety and Efficacy of the Combination of Loop With Thiazide-type Diuretics vs Loop diuretics with placebo in Patients With Decompensated, EudraCT Number 2013-001852-36).

Differential sex-related effect of left ventricular ejection fraction trajectory on the risk of mortality and heart failure readmission following hospitalization for acute heart failure: A longitudinal study.

There is limited information on the sex-specific longitudinal changes of left ventricular ejection fraction (LVEF) after an acute heart failure (AHF) hospitalization. We aimed to investigate whether LVEF trajectories over time and their impact on mortality and AHF readmission rates differ between men and women. We conducted a retrospective sex-specific analysis of longitudinal LVEF measurements (n = 9581) in 3383 patients with an index hospitalization for AHF in a single tertiary-level hospital. Statistical techniques suited for longitudinal data analysis were used. The mean age of the sample was 73.8 ± 11.2 years, and 47.9% were women. The mean LVEF was 49.4 ± 15.3%. At a median follow-up of 2.58 years (interquartile range 0.77-5.62), we registered 2197 deaths (64.9%) and 2597 AHF readmissions in 1302 (38.5%) patients. The longitudinal analysis showed that women had consistently higher LVEF values throughout the follow-up with both trajectories characterized by an early peak-approximately at 1 year-followed by decreasing values in men but a plateau in women. Multivariate between-sex comparisons across LVEF categories revealed that women had lower rates of AHF readmissions when LVEF ≤40%. On the contrary, women displayed an excess risk of AHF readmissions when LVEF >60%. A trend in the same direction was found for cardiovascular and all-cause mortality. Sex was a significant factor in determining the follow-up trajectory of LVEF and predicting differences in outcomes after an AHF admission. The findings suggest that women have a higher risk of AHF readmissions at higher LVEF values, while men have a higher risk at lower LVEF values. For all-cause and cardiovascular mortality, the same direction of the association was inferred but they were not significant.

Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction.

Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high. We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later. LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

Artificial intelligence–based screening for cardiomyopathy in an obstetric population: A pilot study

Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. To evaluate the performance of an artificial intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) <45% and <50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF <45% and <50%, respectively, with 100% sensitivity and 90% specificity. We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.

Treatment effects of empagliflozin in hospitalized heart failure patients across the range of left ventricular ejection fraction-Results from the EMPULSE trial.

The EMPULSE (EMPagliflozin in patients hospitalised with acUte heart faiLure who have been StabilizEd) trial showed that, compared to placebo, the sodium-glucose cotransporter 2 inhibitor empagliflozin (10 mg/day) improved clinical outcomes of patients hospitalized for acute heart failure (HF). We investigated whether efficacy and safety of empagliflozin were consistent across the spectrum of left ventricular ejection fraction (LVEF). A total of 530 patients hospitalized for acute de novo or decompensated HF were included irrespective of LVEF. For the present analysis, patients were classified as HF with reduced (HFrEF, LVEF ≤40%), mildly reduced (HFmrEF, LVEF 41-49%) or preserved (HFpEF, LVEF ≥50%) ejection fraction at baseline. The primary endpoint was a hierarchical outcome of death, worsening HF events (HFE) and quality of life over 90 days, assessed by the win ratio. Secondary endpoints included individual components of the primary endpoint and safety. Out of 523 patients with baseline data, 354 (67.7%) had HFrEF, 54 (10.3%) had HFmrEF and 115 (22.0%) had HFpEF. The clinical benefit (hierarchical composite of all-cause death, HFE and Kansas City Cardiomyopathy Questionnaire total symptom score) of empagliflozin at 90 days compared to placebo was consistent across LVEF categories (≤40%: win ratio 1.35 [95% confidence interval 1.04, 1.75]; 41-49%: win ratio 1.25 [0.66, 2.37)] and ≥50%: win ratio 1.40 [0.87, 2.23], pinteraction = 0.96) with a favourable safety profile. Results were consistent across individual components of the hierarchical primary endpoint. The clinical benefit of empagliflozin proved consistent across LVEF categories in the EMPULSE trial. These results support early in-hospital initiation of empagliflozin regardless of LVEF.

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constrained settings, electrocardiograms (ECGs) are a cost-effective alternative. We developed computer vision-based multi-task deep learning models to analyze 12-lead ECG 2D images, predicting MS and LVEF < 50%. Our dataset comprises 14,052 ECGs with clinical features, utilizing ground truth labels from CMR. Our top-performing model achieved AUC values of 0.838 (95% CI 0.812–0.862) for MS and 0.939 (95% CI 0.921–0.954) for LVEF < 50% classification, outperforming cardiologists. Moreover, MS predictions in a prevalence-specific test dataset recorded an AUC of 0.812 (95% CI 0.810–0.814). Extracted 1D signals from ECG images yielded inferior performance, compared to the 2D approach. In conclusion, our results demonstrate the potential of computer-based MS and LVEF < 50% classification from ECG scan images in clinical screening offering a cost-effective alternative to CMR.

Presence and utility of electrocardiographic abnormalities in long-term childhood cancer survivors

BackgroundWe assessed the prevalence and diagnostic value of ECG abnormalities for cardiomyopathy surveillance in childhood cancer survivors.MethodsIn this cross-sectional study, 1381 survivors (≥5 years) from the Dutch Childhood Cancer Survivor...

Poor in-hospital congestion improvement in acute heart failure patients classified according to left ventricular ejection fraction: prognostic implications.

Residual congestion in acute heart failure (AHF) is associated with poor prognosis. However, there is a lack of data on the prognostic value of changes in a combined assessment of in-hospital congestion. The present study sought to assess the association between in-hospital congestion changes and subsequent prognosis according to left ventricular ejection fraction (LVEF) classification. Patients (N = 244, 80.3 ± 7.6 years, 50.8% male) admitted for acute HF in two European tertiary care centres underwent clinical assessment (congestion score included dyspnoea at rest, rales, third heart sound, jugular venous distention, peripheral oedema, and hepatomegaly; simplified congestion score included rales and peripheral oedema), echocardiography, lung ultrasound, and natriuretic peptides (NP) measurement at admission and discharge. The primary outcome was a composite of all-cause mortality and/or HF re-hospitalization. In the 244 considered patients (95 HF with reduced EF, 57 HF with mildly reduced EF, and 92 HF with preserved EF), patients with limited improvement in clinical congestion score (hazard ratio 2.33, 95% CI 1.51-3.61, P = 0.0001), NP levels (2.29, 95% CI 1.55-3.38, P < 0.0001), and the number of B-lines (6.44, 95% CI 4.19-9.89, P < 0.001) had a significantly higher risk of outcome compared with patients experiencing more sizeable decongestion. The same pattern of association was observed when adjusting for confounding factors. A limited improvement in clinical congestion score and in the number of B-lines was related to poor prognosis for all LVEF categories. In AHF, the degree of congestion reduction assessed over the in-hospital stay period can stratify the subsequent event risk. Limited reduction in both clinical congestion and B-lines number are related to poor prognosis, irrespective of HF subtype.

Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.

The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking. Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF≤40% and 0.79 for the algorithms identifying LVEF<50% and ≥50%. For LVEF≤40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category. The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Contrasting patterns of symptoms compared to mortality in women and men with Congestive Heart Failure (the G-CHF registry)

Abstract Background There is a paucity of data on the clinical characteristics, management, and outcomes of women compared with men with heart failure, especially in low-and middle-income countries. Purpose In this study from the Global Congestive Heart Failure registry, we examined sex differences in risk factors, clinical characteristics, treatments, and the risk of heart failure hospitalization and mortality by country economic status and by left ventricular ejection fraction (LVEF) category. Methods In the prospective Global-Congestive Heart Failure (G-CHF) study, participants with established heart failure were considered for inclusion from 40 high-, middle-, and low-income countries. We recorded information on the demographic characteristics, medical history, and treatments of participants. We report data on heart failure hospitalization and mortality by sex overall, by country economic status, and by LVEF category. Results From December 2016 to July 2022, 23,000 participants were recruited and followed up. The average age of women in the study was 62 years compared to 64 years in men. Fewer women than men had a LVEF ≤40 (51.7% women vs 66.3% men). By contrast, more women than men had an LVEF≥ 50 (33.2% women vs 18.6% men). Hypertensive heart failure was the most common etiology in women (25.5% women vs 16.8% men), and ischemic heart failure was the most common etiology in men (45.6% men vs 26.6% women). Signs and symptoms of heart failure were more common in women than men: 42.6% of women were NYHA functional class III/IV compared to 37.9% of men. Among participants with a LVEF &amp;lt;35, use of implantable cardiac defibrillator (ICD) was lower in women than men in the overall study (8.7% women vs 17.2% men), and within countries categorized by economic status. However, the use of heart failure medicines and cardiac tests did not differ systematically by sex. Differences by sex were not observed in the adjusted risk of heart failure hospitalization overall (women-to-men adjusted hazard ratio 1.0 (95% CI 0.94 to 1.07)). This pattern was consistent within countries categorized by economic status, geographic region, and by LVEF category. Women had a lower adjusted risk of mortality overall (women-to-men adjusted hazard ratio 0.82 (95% CI 0.77 to 0.87), which was consistently observed within countries categories by economic status, geographic region, and LVEF levels. Conclusions There are differences between women and men in heart failure symptoms, underlying causes, ejection fraction categories, and use of an ICD. However, use heart failure medications, cardiac tests, and hospitalization were similar in women and men; but women had a lower risk of death.

Semaglutide in Patients With Obesity and Heart Failure Across Mildly Reduced or Preserved Ejection Fraction.

Many therapies for heart failure (HF) have shown differential impact across the spectrum of left ventricular ejection fraction (LVEF). In this prespecified analysis, the authors assessed the effects of semaglutide across the baseline LVEF strata in patients with the obesity phenotype of HF with preserved ejection fraction (HFpEF) in the STEP-HFpEF (Semaglutide Treatment Effect in People with obesity and HFpEF) trial. STEP-HFpEF randomized 529 patients (263 semaglutide; 266 placebo). For this prespecified analysis, patients were categorized into 3 groups based on LVEF: 45% to 49% (n=85), 50% to 59% (n=215), and≥60% (n=229). At 52weeks, semaglutide improved the dual primary endpoints of Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (estimated treatment difference: EF [ejection fraction] 45%-49%: 5.0 points [95%CI:-2.7 to 12.8 points], EF 50%-59%: 9.8 points [95%CI: 5.0 to 14.6 points], and EF≥60%: 7.4 points [95%CI: 2.8 to 12.0 points]; P interaction=0.56) and body weight (EF: 45%-49%:-7.6 [95%CI:-10.7 to-4.4], EF 50%-59%:-10.6 [95%CI:-12.6 to-8.6] and EF≥60%:-11.9 [95%CI:-13.8 to-9.9]; P interaction=0.08), to a similar extent across LVEF categories. Likewise, LVEF did not influence the benefit of semaglutide on confirmatory secondary endpoints: 6-minute walk distance (P interaction=0.19), hierarchal composite endpoint (P interaction=0.43), and high-sensitivity C-reactive protein (P interaction=0.26); or exploratory endpoint of N-terminal pro-brain natriuretic peptide (Pinteraction=0.96). Semaglutide was well-tolerated across LVEF categories. In patients with HFpEF and obesity, semaglutide 2.4mg improved symptoms, physical limitations, and exercise function, and reduced inflammation and body weight to a similar extent across LVEF categories. These data support treatment with semaglutide in patients with the obesity phenotype of HFpEF regardless of LVEF. (Research Study to Investigate How Well Semaglutide Works in People Living With HeartFailure and Obesity [STEP-HFpEF]; NCT04788511).

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction.

Background There is a need to develop electronic health record-based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1-year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all-cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF <40%), HF with mildly reduced EF (LVEF 40%-49%), and HF with preserved EF (LVEF ≥50%). Model discrimination was evaluated using area under the curve and calibration using mean squared error. We identified 338 426 adults with HF: 61 045 (18.0%) had HF with reduced EF, 49 618 (14.7%) had HF with mildly reduced EF, and 227 763 (67.3%) had HF with preserved EF. The 1-year risks of any WHF event and death were, respectively, 22.3% and 13.0% for HF with reduced EF, 17.0% and 10.1% for HF with mildly reduced EF, and 16.3% and 10.3% for HF with preserved EF. The WHF model displayed an area under the curve of 0.76 and mean squared error of 0.13, whereas the model for death displayed an area under the curve of 0.83 and mean squared error of 0.076. Performance and predictors were similar across WHF encounter types and LVEF categories. Conclusions We developed risk prediction models for 1-year WHF events and death across the LVEF spectrum using structured and unstructured electronic health record data and observed no substantial differences in model performance or predictors except for death, despite differences in underlying HF cause.

Usefulness of heart failure categories based on left ventricular ejection fraction

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): This study was sponsored by AstraZeneca. Introduction Heart failure, a common disease with a mortality on par with many cancers, is one of the costliest diseases to society in the western world. Current heart failure guidelines are based on categories of left ventricular ejection fraction (LVEF) and have recently introduced a narrow category with mid-range/mildly reduced ejection fraction (HFmrEF; LVEF 41-49%) between the previous categories with reduced (HFrEF; LVEF ≤40%) and preserved (HFpEF; LVEF ≥50%) ejection fraction. The grouping of continuous variables into narrow categories can be questioned if the variability is high. Purpose To determine the shape of the LVEF distribution and quantify the stability of LVEF categories over time in a large real-world heart failure population, in order to establish the usefulness of LVEF categories for treatment decisions. Methods Population-based observational cohort study of all new cases of chronic heart failure in Region Stockholm, Sweden, from 2016-01-01 until 2020-12-31. All available values of LVEF were collected over time and patients were followed until death or end of study. Mixed models were used to quantify within-person variance in LVEF, and Multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. Results During the study period, 8,455 patients were diagnosed with heart failure. LVEF values followed a perfect normal distribution. The standard deviation of the within-person variance in LVEF was 7.4%. Regardless of initial LVEF category, age or sex, the mean time spent in any LVEF category before transition to another category (mean sojourn time) was on average less than 0.6 years (Figure 1). The one-year probabilities of transitioning between states were substantial (Figure 2). Patients with HFmrEF had a probability of 1/4 of remaining in that category one year later; the probability of patients with HFrEF of remaining in that category one year later was 1/4 for women and 1/3 for men; and patients with HFpEF had a probability of remaining in that category of 2/3 for women and 1/2 for men. Conclusion In patients with heart failure, LVEF has considerable variability over time. Hence, LVEF has limited use for categorisation of the heart failure population into narrow categories.

Advancing heart failure research using machine learning

Advancing heart failure research using machine learning

MRI Investigation of the Differential Impact of Left Ventricular Ejection Fraction After Myocardial Infarction in Elderly vs. Nonelderly Patients to Predict Readmission for Heart Failure.

Patients with ST-segment elevation myocardial infarction (STEMI), especially elderly individuals, have an increased risk of readmission for acute heart failure (AHF). To study the impact of left ventricular ejection fraction (LVEF) by MRI to predict AHF in elderly (>70 years) and nonelderly patients after STEMI. Prospective. Multicenter registry of 759 reperfused STEMI patients (23.3% elderly). 1.5-T. Balanced steady-state free precession (cine imaging) and segmented inversion recovery steady-state free precession (late gadolinium enhancement) sequences. One-week MRI-derived LVEF (%) was quantified. Sequential MRI data were recorded in 579 patients. Patients were categorized according to their MRI-derived LVEF as preserved (p-LVEF, ≥50%), mildly reduced (mr-LVEF, 41%-49%), or reduced (r-LVEF, ≤40%). Median follow-up was 5 [2.33-7.54] years. Univariable (Student's t, Mann-Whitney U, chi-square, and Fisher's exact tests) and multivariable (Cox proportional hazard regression) comparisons and continuous-time multistate Markov model to analyze transitions between LVEF categories and to AHF. Hazard ratios (HR) with 95% confidence intervals (CIs) were computed. P < 0.05 was considered statistically significant. Over the follow-up period, 79 (10.4%) patients presented AHF. MRI-LVEF was the most robust predictor in nonelderly (HR 0.94 [0.91-0.98]) and elderly patients (HR 0.94 [0.91-0.97]). Elderly patients had an increased AHF risk across the LVEF spectrum. An excess of risk (compared to p-LVEF) was noted in patients with r-LVEF both in nonelderly (HR 11.25 [5.67-22.32]) and elderly patients (HR 7.55 [3.29-17.34]). However, the mr-LVEF category was associated with increased AHF risk only in elderly patients (HR 3.66 [1.54-8.68]). Less transitions to higher LVEF states (n=19, 30.2% vs. n=98, 53%) and more transitions to AHF state (n=34, 53.9% vs. n=45, 24.3%) were observed in elderly than nonelderly patients. MRI-derived p-LVEF confers a favorable prognosis and r-LVEF identifies individuals at the highest risk of AHF in both elderly and nonelderly patients. Nevertheless, an excess of risk was also found in the mr-LVEF category in the elderly group. 2. Stage 2.

306 HEART FAILURE AS THE CANCER FOR THE HEART: THE PROGNOSTIC ROLE OF THE HLM SCORE

Abstract Background The multiorgan involvement of heart failure is similar to the spread observed in cancer. We proposed a new score, named HLM, analogous to TNM classification used in oncology. HLM refers to (i) H: heart damage, instead of “T” for tumor; (ii) L: lung involvement, instead of “N” for lymph nodes; (iii) M: systemic multiorgan involvement, instead of “M” for metastasis. Objectives to compare HLM score to NYHA class, ACC/AHA stages and left ventricular ejection fraction (LVEF) classification, to assess the most accurate prognostic tool for HF patients. Methods We performed a multicentric, observational, prospective study of consecutive patients admitted for HF, or at risk for HF. Parameters for heart, lungs and organs’ function were collected. Each patient was classified according to HLM, NYHA, ACC/AHA stages and LVEF classification. Patients were followed up for 12 months. The primary composite endpoint was all-cause death and rehospitalization due to HF. Results We enrolled 1720 patients who completed the 12-month follow-up. As shown by Kaplan Meier curves, HLM was the most accurate score to predict primary endpoint at 12- month. The area under the ROC curve (AUC) was greater for HLM score than NYHA, ACC/AHA stages and LVEF classification, regarding the composite endpoint (HLM=0.645; NYHA=0.580; ACC/AHA=0.589; LVEF=0.572) (Figure 1). HLM score related AUC showed statistically significant differences compared to LVEF (p=0.002), ACC/AHA (p=0.029) and NYHA (p=0.009). Conclusions HLM score has a greater prognostic power compared to other nosologies, in terms of the composite endpoint of all-cause death and rehospitalization due to HF, at 12-month follow-up. Key words: heart failure; HLM score; prognosis; mortality; rehospitalization. Figure 1.

Cardiovascular vs. non-cardiovascular deaths after heart failure hospitalization in young, older, and very old patients.

The long-term outcome in patients with heart failure (HF) after hospitalization may vary substantially depending on their age and left ventricular ejection fraction (LVEF). We aimed to assess the relative rates of cardiovascular death (CVD) and non-CVD based on the age and how the rates differ under the updated LVEF classification system. Consecutively registered hospitalized patients with HF (N=3558; 39.7% women with a mean age of 73.9±13.3years) were followed for a median of 2 (interquartile range, 0.8-3.1) years. The CVDs and non-CVDs were evaluated based on age [young (<65years), older (65-84years), and very old (≥85years)] and LVEF classification [HF with preserved EF (HFpEF; LVEF ≥50%) and non-HFpEF (LVEF <50%)]. The adverse clinical events were adjudicated independently by a central committee. Overall, 1505 (42.3%) had HFpEF [young: n=182 (12.1%), older: n=894 (59.4%), very old: n=429 (28.5%)], and 2053 (57.7%) had non-HFpEF [young: n=575 (28.0%), older: n=1159 (56.5%), very old: n=319 (15.5%)]. During the follow-up, the crude incidence of all-cause death was higher in non-HFpEF than in HFpEF across all age groups (non-HFpEF vs. HFpEF, young: 10.4% vs. 5.5%, log-rank P=0.10; older: 26.6% vs. 20.9%, log-rank P=0.002; very old: 36.7% vs. 31.7%, log-rank P=0.043). CVDs accounted for more than half of all deaths in non-HFpEF (young 65.0%, older 64.2%, and very old 55.6%), whereas the proportion of CVDs remained less than half in HFpEF (young 50.0%, older 41.2%, very old 38.2%). HF readmission was associated with subsequent all-cause death in non-HFpEF [hazard ratio (HR): 1.72, 95% confidence interval (CI): 1.41-2.09, P<0.001], but not in HFpEF (HR: 1.12, 95% CI: 0.87-1.43, P=0.39). The probability of a non-CVD increases in both LVEF categories with advancing age, but that it is greater in the HFpEF category. The findings indicate that mitigating CV-related outcomes alone may be insufficient for treating HF in older population, particularly in the HFpEF category.

Abstract 13653: Leveraging Natural Language Processing and Machine Learning to Predict Worsening Heart Failure Events

Background: Prior risk models in patients with heart failure (HF) have focused on hospitalizations for worsening HF (WHF) and have not evaluated for differences in predictors by left ventricular ejection fraction (LVEF). We used natural language processing (NLP) and machine learning methods with access to longitudinal electronic health record (EHR) data to develop risk prediction models for WHF events across practice settings and by LVEF category. Methods: We identified all adults with HF and known LVEF on January 1 st of each year from 2011-2019 in an integrated health care system. WHF events within 1 year were defined as any hospitalization, emergency department, or outpatient encounter with ≥1 symptom, ≥2 objective findings including ≥1 sign, and ≥1 change in HF-related therapy. Signs and symptoms were ascertained using rule-based NLP. We conducted boosted decision tree-based ensemble models for any WHF event within each LVEF category: HF with reduced EF (HFrEF; LVEF ≤40%), HF with mildly reduced EF (HFmrEF; LVEF 41-49%), and HF with preserved EF (HFpEF; LVEF ≥50%). We evaluated model discrimination using area under the curve (AUC) and model calibration using Brier scores. Results: Among 359,298 patients from 2011-2019, 65,838 (18%) had HFrEF, 52,491 (15%) had HFmrEF, and 240,969 (67%) had HFpEF. Mean age was 75±12, 47% were women, and 37% were minorities including 10% Black, 11% Asian/Pacific Islander, and 12% of Hispanic ethnicity. WHF events occurred in 22% of patients with HFrEF, 17% with HFmrEF, and 16% with HFpEF. The models displayed an AUC of 0.75 and Brier score of 0.15 for HFrEF and an AUC of 0.77 and Brier scores of 0.12 for both HFmrEF and HFpEF. Clinical predictors were similar across LVEF categories ( Table ). Conclusions: Longitudinal EHR data can be leveraged using NLP and machine learning for accurate risk estimation that reliably identifies clinical predictors across a range of LVEF. These findings may provide novel insight into the natural history of HF.

Abstract 14664: Heart Failure as the Cancer for the Heart: The Prognostic Role of the New HLM Score

Introduction: The multiorgan involvement of heart failure is similar to the spread observed in cancer. We proposed a new score system, named HLM, analogous to TNM classification used in oncology. HLM refers to (i) H: heart damage, instead of “T” for tumor; (ii) L: lung involvement, instead of “N” for lymph nodes; (iii) M: involvement of peripheral organs, instead of “M” for metastasis. Hypothesis: to compare HLM score to NYHA class, ACC/AHA stages and HF classification by left ventricular ejection fraction (LVEF), to assess the most accurate prognostic tool for HF patients. Methods: We performed a multicentric, observational, prospective study of consecutive patients admitted for HF, or at risk for HF. All parameters for heart, lungs and organs’ function were collected. HLM score, made up of 4 stages of severity, was built starting from HLM classification. Each patient was classified according to HLM, NYHA, ACC/AHA stages and LVEF. The primary composite endpoint was all-cause death and rehospitalization due to HF; patients were followed up for 12 months. Results: We enrolled 1720 patients who completed the 12-months follow-up. As showed by Kaplan Meier curves, HLM was the most accurate score to predict primary endpoint at 12 months. The area under the ROC curve (AUC) was greater for HLM score than NYHA, ACC/AHA stages and LVEF classification, regarding the composite endpoint (HLM=0.645; NYHA=0.580; ACC/AHA=0.572; LVEF=0.572). HLM score related AUC showed statistically significant differences compared to LVEF (p=0.002), ACC/AHA (p=0.002) and NYHA (p=0.009). Conclusions: HLM score has a greater prognostic power compared to other nosologies, in terms of the composite endpoint of all-cause death and rehospitalization, at 12 months follow up.

Clinically Acquired High Sensitivity Cardiac Troponin T is a Poor Predictor of Reduced Left Ventricular Ejection Fraction After ST Elevation Myocardial Infarction: A National Cohort Study-ANZACS-QI 65.

Cardiac troponins (cTn) have been used historically to estimate infarct size in ST elevation myocardial infarction (STEMI). Within a resource constrained health care environment, cTn could therefore be used for prioritisation of patients for cardiac imaging, in particular echocardiography. We aimed to determine how useful routinely collected cTn would be in predicting significant left ventricular (LV) impairment. All patients in the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry with their first episode of STEMI between January 2013 and November 2018, who had high sensitivity troponin T measured, were included. We excluded patients with no left ventricular ejection fraction (LVEF) assessment, known LV dysfunction, or prior myocardial infarction. In total,3,698 patients were included in the analysis. A higher mean hsTnT (admission and peak) was seen in patients with more severely impaired LV function but there was significant overlap in the range of hsTnT between the different LVEF categories. Cardiac troponins demonstrated poor discriminative ability to either predict or exclude significant LV impairment (LVEF <40%). At an optimal cutpoint of 3,405 ng/L, peak hsTnT had a sensitivity of 56.5% (95% confidence interval [CI] 42-62%), a specificity of 65.3% (95% CI 62-79%) and an area under the receiver operating curve of 0.62 (95% CI 0.60-0.64). This is the largest study comparing clinically measured troponin levels and LV function in patients presenting with STEMI. A definite, but weak, association was seen between peak troponin and the degree of LV dysfunction, with significant overlap in troponin levels between levels of myocardial dysfunction. Routinely acquired troponin is not suitable for clinical use as a method of prioritising patients for cardiac imaging.