Makoto Takei

Background: The absence of practice standards in vasoactive agent usage for acute decompensated heart failure (ADHF) has resulted in significant treatment variability across hospitals, potentially affecting patient outcomes. This study aimed to assess temporal trends and institutional differences in vasodilator and inotrope/vasopressor utilization among ADHF patients, considering their clinical phenotypes. Methods: Data were extracted from a government-funded multicenter registry covering the Tokyo metropolitan area, comprising 44,444 consecutive patients urgently hospitalized in intensive/cardiovascular care units with a primary diagnosis of ADHF between 2013 and 2021. Clinical phenotypes, i.e., pulmonary congestion or tissue hypoperfusion, were defined through a comprehensive assessment of clinical signs and symptoms, vital signs, and laboratory findings. We assessed the frequency and temporal trends in phenotype-based drug utilization of vasoactive agents and investigated institutional characteristics associated with adopting the phenotype-based approach. Results: Throughout the study period, both overall and phenotype-based vasodilator utilization showed significant declines, with overall usage dropping from 61.4% in 2013 to 48.6% in 2021 ( p for trend < 0.001). Conversely, no temporal changes were observed in overall inotrope/vasopressor utilization from 24.6% in 2013 to 25.8% in 2021 or the proportion of phenotype-based utilization. Notably, there was considerable variability in phenotype-based drug utilization among hospitals, ranging from 0% to 100%. Particularly, hospitals with a large number of board-certified cardiologists demonstrated reduced phenotype-based vasodilator utilization and phenotypically inappropriate inotrope/vasopressor utilization over time. Conclusions: Substantial variability existed among hospitals in phenotype-based drug utilization of vasoactive agents for ADHF patients, highlighting the need for standardization in their adoption during hospitalization. Key Learning Points: a. What is known: The absence of standardized drug utilization practices for acute decompensated heart failure contributes to notable variations in treatment approaches across different healthcare facilities, and these facility-specific differences have potential to influence patient outcomes. b. What the study adds: This study demonstrated that, using a government-funded multicenter registry of hospitalized patients for acute decompensated heart failure, overall and phenotype-based inotrope/vasopressor utilization was relatively stable between 2013 and 2021, while significant declines in both overall and phenotype-based vasodilator utilization was observed over the same period. Significant variability was noted in the extent of phenotype-based drug utilization among hospitals, with the utilization rates ranging from 0% to 100%: Specifically, hospitals with a large number of board-certified cardiologists demonstrated gradual declines in phenotype-based vasodilator utilization and phenotypically inappropriate utilization of inotropes/vasopressors.

Background: Recently, patients with supra-normal left ventricular ejection fraction (snLVEF) are reported to have high risk of adverse outcomes, especially in women. We sought to evaluate sex-related differences in the association between LVEF and long-term outcomes in heart failure (HF) patients. Methods: The multicenter WET-HF Registry enrolled all patients hospitalized for acute decompensated HF (ADHF). We analyzed 3,943 patients (age 77 years; 59.8% male) registered from 2006 to 2017. According to LVEF the patients were divided into the 3 groups: HF with reduced EF (HFrEF), mildly reduced EF (HFmrEF) and preserved EF (HFpEF). The primary endpoint was defined as the composite of cardiac death and ADHF rehospitalization after discharge. Results: Implementation of guideline-directed medical therapy (GDMT) such as renin-angiotensin-system inhibitor (RASi), β-blocker and their combination at discharge was significantly lower in women than men in HFmrEF. Lower prescription of RASi + β-blocker combination in female HFmrEF was noted even after adjustment for covariates (p=0.007). There were no such sex-related differences in HFrEF. Female sex was associated with higher incidence of the primary endpoint and ADHF rehospitalization after adjustment for covariates exclusively in HFmrEF. Restricted cubic spline analysis demonstrated a U-shaped relationship between LVEF and the hazard ratio of the primary endpoint in women, but such relationship was not observed in men ( p for interaction=0.037). Conclusions: In women, not only lower LVEF but also snLVEF were associated with worse long-term outcomes. Additionally, sex-related differences in the GDMT implementation for HFmrEF highlight the need for sex-specific guidelines to optimize HF management.

Background: The burden of noncardiovascular conditions is becoming increasingly prevalent in patients with heart failure (HF). We aimed to identify novel phenogroups incorporating noncardiovascular conditions to facilitate understanding and risk stratification in elderly patients with HF. Methods and Results: Data from a total of 1881 (61.2%) patients aged ≥65 years were extracted from a prospective multicenter registry of patients hospitalized for acute HF (N=3072). We constructed subgroups of patients with HF with preserved ejection fraction (HFpEF; N=826, 43.9%) and those with non‐HFpEF (N=1055, 56.1%). Latent class analysis was performed in each subgroup using 17 variables focused on noncardiovascular conditions (including comorbidities, Clinical Frailty Scale, and Geriatric Nutritional Risk Index). The latent class analysis revealed 3 distinct clinical phenogroups in both HFpEF and non‐HFpEF subgroups: (1) robust physical and nutritional status (Group 1: HFpEF, 41.2%; non‐HFpEF, 46.0%); (2) multimorbid patients with renal impairment (Group 2: HFpEF, 40.8%; non‐HFpEF, 41.9%); and (3) malnourished patients (Group 3: HFpEF, 18.0%; non‐HFpEF, 12.1%). After multivariable adjustment, compared with Group 1, patients in Groups 2 and 3 had a higher risk for all‐cause death over the 1‐year postdischarge period (hazard ratio [HR], 2.79 [95% CI, 1.64–4.81] and HR, 2.73 [95% CI, 1.39–5.35] in HFpEF; HR, 1.96 [95% CI, 1.22–3.14] and HR, 2.97 [95% CI, 1.64–5.38] in non‐HFpEF; respectively). Conclusions: In elderly patients with HF, the phenomapping focused on incorporating noncardiovascular conditions identified 3 phenogroups, each representing distinct clinical outcomes, and the discrimination pattern was similar for both patients with HFpEF and non‐HFpEF. This classification provides novel risk stratification and may aid in clinical decision making.

Aims: Available predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients.Methods and results: In a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals’ electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong’s test] with good calibration (P = 0.11; Hosmer–Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9–64%; P = 0.009]. The Fine–Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04–1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%.Conclusion: To improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF.

Aims: 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. Methods and results: Consecutively registered hospitalized patients with HF (N = 3558; 39.7% women with a mean age of 73.9 ± 13.3 years) 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 (<65 years), older (65–84 years), and very old (≥85 years)] 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). Conclusions: 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.

Aims The impact of worsening renal function (WRF) on the prognosis of patients with acute heart failure (AHF) remains controversial. We aimed to identify phenotypically distinct subgroups among individuals with both AHF and WRF using cluster analysis. Methods and results Overall, the data of 483 patients with both AHF and WRF enrolled in the West Tokyo Heart Failure Registry were analysed. Using cluster analysis, we identified three phenotypically distinct subgroups (phenogroups 1, 2, and 3). We assessed the impact of WRF on the prognosis of each phenogroup by comparing the incidence of composite endpoints, including all-cause death and re-hospitalization due to heart failure, with those of a propensity score-matched, non-WRF control group. Participants in phenogroup 1 (N = 122) were the youngest (69.3 ± 13.7 years), had relatively preserved estimated glomerular filtration rate (eGFR, 70.0 ± 27.7 mL/min/1.73 m²), and reduced left ventricular ejection fraction (LVEF) (41.8 ± 13.7%). Conversely, participants in phenogroup 3 (N = 122) were the oldest (81.7 ± 8.5 years), had the worst eGFR (33.0 ± 20.9 mL/min/1.73 m²), and had preserved LVEF (51.7 ± 14.8%). The characteristics of the participants in phenogroup 2 (N = 239) were between those of phenogroups 1 and 3. The propensity score matching analysis showed that WRF was associated with a higher incidence of composite endpoints in phenogroup 1, whereas this association was not observed in phenogroups 2 and 3. Conclusions Using cluster analysis, we revealed three phenotypically distinct subgroups of patients with both AHF and WRF. WRF was associated with worse clinical outcomes in the subgroup of younger patients with reduced LVEF and preserved renal function.

Oncoimaging using positron emission tomography (PET) with a specific radioprobe would facilitate individualized cancer management. Evidence indicates that ectopically expressed metabotropic glutamate 1 (mGlu1) receptor independently induces melanocyte carcinogenesis, and it is therefore becoming an important target for personalized diagnosis and treatment strategies for melanomas. Here, we report the development of an oncoprotein-based PET imaging platform in melanomas for noninvasive visualization and quantification of mGlu1 with a novel mGlu1-specific radioprobe, 4-¹⁸F-fluoro-N-[4-[6-(isopropyl amino)pyrimidin-4-yl]-1,3-thiazol-2-yl]-N-methylbenzamide (¹⁸F-FITM). ¹⁸F-FITM shows excellent pharmacokinetics, namely the dense and specific accumulation in mGlu1-positive melanomas versus mGlu1-negative hepatoma and normal tissues. Furthermore, the accumulation levels of radioactivity corresponded to the extent of tumor and to levels of mGlu1 protein expression in melanomas and melanoma metastasis. The ¹⁸F-FITM PET imaging platform, as a noninvasive personalized diagnostic tool, is expected to open a new avenue for defining individualized therapeutic strategies, clinical trials, patient management and understanding mGlu1-triggered oncologic events in melanomas.