Clinical Characteristics and Outcomes of Hospitalized Patients With Heart Failure From the Large-Scale Japanese Registry Of Acute Decompensated Heart Failure (JROADHF)

Ide, Tomomi; Kaku, Hisatoshi; Matsushima, Shouji; Tohyama, Takeshi; Enzan, Nobuyuki; Funakoshi, Kouta; Sumita, Yoko; Nakai, Michikazu; Nishimura, Kunihiro; Miyamoto, Yoshihiro; Tsuchihashi-Makaya, Miyuki; Hatano, Masaru; Komuro, Issei; Tsutsui, Hiroyuki

doi:10.1253/circj.cj-20-0947

Cited by 70 publications

(85 citation statements)

References 34 publications

(44 reference statements)

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“…First, Japan has the largest proportion of older adults (>65 years old) worldwide, because of which it is referred to as a 'super-ageing' society (https://population.un.org/wpp/Download/Standard/Population/). Therefore, the overall profile of patients with HF in the JROADHF also showed a greater degree of ageing than that observed in the previous cohort database, 14 and the median age of the participants in this study was 80 years. This difference in the ageing population between the JROADHF and previous studies might lead to an increased proportion of HF patients with preserved ejection fraction and average of LVEF in the JROADHF.…”

Section: Limitationssupporting

confidence: 39%

“…The data utilized in this study were obtained from the Japanese Registry of Acute Decompensated Heart Failure (JROADHF). 14 Across Japan, 128 facilities enrolled in the JROADHF study were selected by random cluster sampling. The inclusion criterion was acute HF in patients aged 16 years or older who were admitted to the enrolled facilities in 2013, excluding those with acute myocardial infarction.…”

Section: Trial and Participantsmentioning

confidence: 99%

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Machine learning‐based model for predicting 1 year mortality of hospitalized patients with heart failure

et al. 2021

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Aims Individual risk stratification is a fundamental strategy in managing patients with heart failure (HF). Artificial intelligence, particularly machine learning (ML), can develop superior models for predicting the prognosis of HF patients, and administrative claim data (ACD) are suitable for ML analysis because ACD is a structured database. The objective of this study was to analyse ACD using an ML algorithm, predict the 1 year mortality of patients with HF, and finally develop an easy-to-use prediction model with high accuracy using the top predictors identified by the ML algorithm. Methods and results Machine learning-based prognostic prediction models were developed from the ACD on 10 175 HF patients from the Japanese Registry of Acute Decompensated Heart Failure with 17% mortality during 1 year follow-up. The top predictors for prognosis in HF were identified by the permutation feature importance technique, and an easy-to-use prediction model was developed based on these predictors. The c-statistics and Brier scores of the developed ML-based models were compared with those of conventional risk models: Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC). A voting classifier algorithm (ACD-VC) achieved the highest c-statistics among the six ML algorithms. The permutation feature importance technique enabled identification of the top predictors such as Barthel index, age, body mass index, duration of hospitalization, last hospitalization, renal disease, and non-loop diuretics use (feature importance values were 0.054, 0.025, 0.010, 0.005, 0.005, 0.004, and 0.004, respectively). Upon combination of some of the predictors that can be assessed from a brief interview, the Simple Model by ARTificial intelligence for HF risk stratification (SMART-HF) was established as an easy-to-use prediction model. Compared with the conventional models, SMART-HF achieved a higher c-

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Section: Limitationssupporting

confidence: 39%

Section: Trial and Participantsmentioning

confidence: 99%

Machine learning‐based model for predicting 1 year mortality of hospitalized patients with heart failure

et al. 2021

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“…The average age of patients with HF in the UK and Japan is currently 77 and 78 years, respectively [ 9 , 10 ]. In addition to the problems with the effectiveness and adverse effects of medications, elderly patients with HF have multiple comorbidities, typically 5 to 6 comorbidities in addition to HF [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Contemporary use of SGLT2 inhibitors in heart failure patients with diabetes mellitus: a comparison of DPP4 inhibitors in a nationwide electric health database of the superaged society

Nakai

Iwanaga

Kanaoka

et al. 2022

Cardiovasc Diabetol

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Background There is a lack of recent data reflecting the actual use of sodium-glucose cotransporter-2 (SGLT2) inhibitors for heart failure (HF) and type 2 diabetes (DM) in the superaged society. The present study investigated the association between the use of SGLT2 inhibitors and one-year prognosis in patients hospitalized across a broad spectrum of HF patients with DM in the superaged society using the Nationwide Electric Health Database in Japan. Methods The patients hospitalized with the first episode of acute HF were identified from the National Database of Health Insurance Claims and Specific Health Checkups of Japan between April 2014 and March 2019. A cohort of 2,277 users of SGLT2 inhibitors and 41,410 users of the active comparator, dipeptidyl peptidase-4 (DPP4) inhibitors were compared. A propensity score-matched cohort study of 2,101 users of each inhibitor was also conducted. A multivariable multilevel mixed-effects survival model was conducted with adjustments, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Results Among 300,398 patients discharged with HF in 4,176 hospitals, 216,016 (71.9%) were 75 years or older, and 60,999 (20.3%) took antidiabetic medications. Among them, the patients treated with SGLT2 inhibitors were younger and had a more severe status than those treated with DPP4 inhibitors. Kaplan–Meier analysis showed that patients treated with SGLT2 inhibitors had a lower mortality risk and HF readmission. In propensity-matched cohorts, SGLT2 inhibitor use was associated with a lower risk of mortality and HF readmission than DPP-4 inhibitor use (HR [95% CI]; 0.70 [0.56, 0.89] and 0.52 [0.45, 0.61], respectively). Very elderly (≥ 75 years) patients showed similar results. Favorable effects were also observed across all age groups, including ≥ 75 years, in patients with coronary artery disease or atrial fibrillation and with concomitant β-blocker, diuretics, or insulin. Conclusion The use of SGLT2 inhibitors at discharge was associated with a lower risk of one-year mortality and HF readmission in patients across a broad spectrum of HF with DM in the superaged society. The findings further support the benefits of using SGLT2 inhibitors in very elderly HF care and complement the current evidence.

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“…An epidemiological survey conducted in Sado City estimated that the number of HF patients in Japan will increase from 979,000 in 2005 to 1,284,000 in 2025 [2]. In a large-scale registry, JCARAE-CARD, in 2004, the mean age of the registered patients was 71 years [3], and in the most recent registry in Japan in 2014, JROADHF, the mean age was 78 years, and elderly patients over 75 years accounted for 68.9% of patients [4].…”

Section: Introductionmentioning

confidence: 99%

Body Weight Measurement for Adjusting Diuretics Is Not Associated With Hospitalization and Mortality of Heart Failure Patients Under Home Medical Care

Chuma¹,

Kataoka²

2022

Cureus

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Introduction:The post-discharge readmission rate for heart failure (HF) is high. Although some previous studies have shown the efficacy of clinic-based patient education of regular weighing in preventing readmission; however, none of these studies were conducted in home care settings.Methods: This retrospective observational study aimed to assess whether using body weight as an indicator for treating HF with diuretics reduced hospitalization and mortality in home medical care settings. We included 70 patients diagnosed with HF who were treated with diuretics in home medical care. Seventeen patients were in the body weight measurement group, and 53 were in the control group. The primary outcome was the time of the first hospitalization or death due to any cause. The secondary outcome was the time to first hospitalization or death due to HF.Results: The crude hazard ratio (HR) for the primary outcome was 0.59 [95% confidence interval (CI), 0.33-1.1]. The crude HR for the secondary outcome was 0.95 (95% CI, 0.46-1.95). After adjusting for confounding factors, the results were found to be consistent. Conclusion: Body weight measurement for treating HF was not associated with reduced hospitalization and mortality in medical care settings. Hence, physicians need other appropriate indicators.

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Clinical Characteristics and Outcomes of Hospitalized Patients With Heart Failure From the Large-Scale Japanese Registry Of Acute Decompensated Heart Failure (JROADHF)

Cited by 70 publications

References 34 publications

Machine learning‐based model for predicting 1 year mortality of hospitalized patients with heart failure

Machine learning‐based model for predicting 1 year mortality of hospitalized patients with heart failure

Contemporary use of SGLT2 inhibitors in heart failure patients with diabetes mellitus: a comparison of DPP4 inhibitors in a nationwide electric health database of the superaged society

Body Weight Measurement for Adjusting Diuretics Is Not Associated With Hospitalization and Mortality of Heart Failure Patients Under Home Medical Care

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