scholarly journals Adaptive Servo-Ventilation Therapy Improves Long-Term Prognosis in Heart Failure Patients With Anemia and Sleep-Disordered Breathing

2014 ◽  
Vol 55 (4) ◽  
pp. 342-349 ◽  
Author(s):  
Satoshi Suzuki ◽  
Akiomi Yoshihisa ◽  
Makiko Miyata ◽  
Takamasa Sato ◽  
Takayoshi Yamaki ◽  
...  
Author(s):  
Keita Goto ◽  
Noriaki Takama ◽  
Masahiko Kurabayashi

<p><strong>Background:</strong> Adaptive servo-ventilation (ASV) is used to treat sleep apnea in heart failure (HF). However, it is unclear whether ASV improves the long-term prognosis for all patients with HF, regardless of the severity of sleep-disordered breathing (SDB). We therefore aimed to estimate the long-term prognosis associated with ASV therapy for patients with HF by the severity of SDB.</p><p><strong>Methods:</strong> Sixty-one consecutive patients with HF (mean age ± standard deviation: 70 ± 10 years) were initiated on ASV therapy for HF treatment after polysomnography. Patients were then classified into the following three groups based on their apnea–hypopnea index (AHI): a severe group with an AHI of ≥40/h (n = 28); a moderate group with an AHI of ≥20/h but &lt;40/h (n = 20); and a mild group with an AHI of &lt;20/h (n = 13). To estimate long-term prognosis, we reviewed the 3-year follow-up data, including that concerning fatal cardiovascular events (death from myocardial infarction, cardioembolic stroke, and fatal cardiac arrhythmias).</p><p><strong>Results:</strong> No significant differences were observed between the three study groups in the risk of fatal cardiovascular events (p = 0.207).<strong></strong></p><p><strong>Conclusions:</strong> Our results suggest that ASV therapy is associated with a good prognosis and that ASV therapy is effective, regardless of the severity of SDB.</p><p> </p>


2012 ◽  
Vol 28 (6) ◽  
pp. 728-734 ◽  
Author(s):  
Akiomi Yoshihisa ◽  
Satoshi Suzuki ◽  
Takashi Owada ◽  
Shoji Iwaya ◽  
Hiroyuki Yamauchi ◽  
...  

2020 ◽  
Vol 6 (1) ◽  
pp. 16-22
Author(s):  
Farida Hanum Margolang ◽  
Refli Hasan ◽  
Abdul Halim Raynaldo ◽  
Harris Hasan ◽  
Ali Nafiah ◽  
...  

Background: Acute heart failure is a global health problem with high morbidity and mortality. Short term and long term prognosis of these patients is poor. Therefore, early identification of patients at high risk for major adverse cardiovascular events (MACEs) during hospitalization was needed to improve outcome. Creatinine levels at admission could be used as predictors of major adverse cardiovascular events in acute heart failure patients because creatinine is a simple and routine biomarker of renal function examined in patients with acute heart failure. This study aimed to determine whether creatinine can be used as a predictor of major adverse adverse cardiovascular events in patients with acute heart failure.Methods: This study is a prospective cohort study of 108 acute heart failure patients treated at H. Adam Malik Hospital from July 2018 to January 2019. Creatinine cut-off points were determined using the ROC curve, then bivariate and multivariate analyzes were performed to determine predictors of major adverse cardiovascular events during hospitalization.Results: From 108 study subjects, 24 (22.2%) subjects experienced major adverse cardiovascular events during hospitalization. The subjects who died were 20 people (83.4%), subjects with arrhythmia were 2 people (8.3%), and those who had stroke were 2 people (8.3 %). Through the ROC curve analysis, we found creatinine cut-off values of ≥1.7 mg / dl (AUC 0.899, 95% CI 0.840- 0.957, p <0.05). Creatinine ≥1.7 mg/dl could predict major adverse cardiovascular events with a sensitivity of 87.5% and specificity of 79.5%. Multivariate analysis showed that creatinine ≥1.7 mg / dl was an independent factor to predict MACEs during hospitalization in this study (OR 18,310, p 0.001) as well as creatinine clearance and heart rate.Conclusion: Creatinine levels at admission is an independent predictor for major adverse cardiovascular events during hospitalization in acute heart failure patients.


Sign in / Sign up

Export Citation Format

Share Document