Susumu Hirose1, Yuya Matsue2, Kentaro Kamiya3, Nobuyuki Kagiyama4, Masaru Hiki1, Taishi Dotare1, Tsutomu Sunayama1, Masaaki Konishi5, Hiroshi Saito6, Kazuya Saito7, Yuki Ogasahara8, Emi Maekawa9, Takeshi Kitai10, Kentaro Iwata11, Kentaro Jujo12, Hiroshi Wada13, Takatoshi Kasai14, Shin-Ichi Momomura15, Tohru Minamino16. 1. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. 2. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Cardiovascular Respiratory Sleep Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. Electronic address: yuya8950@gmail.com. 3. Department of Rehabilitation, School of Allied Health Sciences, Kitasato University, Sagamihara, Japan. 4. Department of Cardiology, The Sakakibara Heart Institute of Okayama, Okayama, Japan; Department of Digital Health and Telemedicine R&D, Juntendo University, Tokyo, Japan; Department of Cardiovascular Biology and Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan. 5. Division of Cardiology, Yokohama City University Medical Center, Yokohama, Japan. 6. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Rehabilitation, Kameda Medical Center, Kamogawa, Japan. 7. Department of Rehabilitation, The Sakakibara Heart Institute of Okayama, Okayama, Japan. 8. Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan. 9. Department of Cardiovascular Medicine, Kitasato University School of Medicine, Kanagawa, Japan. 10. Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. 11. Department of Rehabilitation, Kobe City Medical Center General Hospital, Kobe, Japan. 12. Department of Cardiology, Nishiarai Heart Center Hospital, Tokyo, Japan. 13. Department of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University, Shimotsuke, Japan. 14. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Cardiovascular Respiratory Sleep Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. 15. Saitama Citizens Medical Center, Saitama, Japan. 16. Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Japan Agency for Medical Research and Development-Core Research for Evolutionary Medical Science and Technology (AMED-CREST), Japan Agency for Medical Research and Development, Tokyo, Japan.
Abstract
BACKGROUND & AIMS: Although the Global Leadership Initiative on Malnutrition (GLIM) proposed a consensus scheme for diagnosing malnutrition in adults in clinical settings globally, the prevalence and prognostic value of malnutrition defined by GLIM criteria have yet to be evaluated in elderly patients with heart failure. This study aimed to determine the prevalence and prognostic implication of malnutrition as defined by GLIM criteria in comparison to those for a pre-existing definition of malnutrition, the geriatric nutritional risk index (GNRI), in elderly patients with heart failure METHODS: We evaluated malnutrition by two metrics, the GLIM criteria and geriatric nutritional risk index (GNRI), in 890 hospitalized patients with decompensation of heart failure aged ≥65 years, able to ambulate at discharge. The primary outcome was all-cause death within 1 year of discharge. RESULTS: According to GLIM criteria and GNRI <92, 42.4% and 46.5% of participants, respectively, had malnutrition, with moderate agreement (Cohen's kappa coefficient: 0.46 [95% confidence interval: 0.40-0.51]). During 1 year of follow-up, 101 (11.4%) deaths were observed, and malnutrition defined by either the GLIM criteria or GNRI was associated with a higher mortality rate, independent of other prognostic factors (GNRI: hazard ratio, 1.45, P = 0.031; GLIM: hazard ratio, 1.57, P = 0.016). Although malnutrition defined by either criterion showed additive prognostic value when added to a model incorporating pre-existing prognostic factors, defining malnutrition by GLIM criteria instead of the GNRI yielded a statistically significant improvement in model prognostic predictive ability (net-reclassification improvement, 0.44, P < 0.001; integrated discrimination index, 0.013, P < 0.001). CONCLUSIONS: In elderly patients with heart failure, 42.4% are malnourished according to the GLIM criteria, which is associated with a poor prognosis, independent of known prognostic factors. CLINICAL TRIAL REGISTRATION: University Hospital Medical Information Network (UMIN-CTR, https://www.umin.ac.jp/ctr/index.htm, study unique identifier: UMIN000023929).
BACKGROUND & AIMS: Although the Global Leadership Initiative on Malnutrition (GLIM) proposed a consensus scheme for diagnosing malnutrition in adults in clinical settings globally, the prevalence and prognostic value of malnutrition defined by GLIM criteria have yet to be evaluated in elderly patients with heart failure. This study aimed to determine the prevalence and prognostic implication of malnutrition as defined by GLIM criteria in comparison to those for a pre-existing definition of malnutrition, the geriatric nutritional risk index (GNRI), in elderly patients with heart failure METHODS: We evaluated malnutrition by two metrics, the GLIM criteria and geriatric nutritional risk index (GNRI), in 890 hospitalized patients with decompensation of heart failure aged ≥65 years, able to ambulate at discharge. The primary outcome was all-cause death within 1 year of discharge. RESULTS: According to GLIM criteria and GNRI <92, 42.4% and 46.5% of participants, respectively, had malnutrition, with moderate agreement (Cohen's kappa coefficient: 0.46 [95% confidence interval: 0.40-0.51]). During 1 year of follow-up, 101 (11.4%) deaths were observed, and malnutrition defined by either the GLIM criteria or GNRI was associated with a higher mortality rate, independent of other prognostic factors (GNRI: hazard ratio, 1.45, P = 0.031; GLIM: hazard ratio, 1.57, P = 0.016). Although malnutrition defined by either criterion showed additive prognostic value when added to a model incorporating pre-existing prognostic factors, defining malnutrition by GLIM criteria instead of the GNRI yielded a statistically significant improvement in model prognostic predictive ability (net-reclassification improvement, 0.44, P < 0.001; integrated discrimination index, 0.013, P < 0.001). CONCLUSIONS: In elderly patients with heart failure, 42.4% are malnourished according to the GLIM criteria, which is associated with a poor prognosis, independent of known prognostic factors. CLINICAL TRIAL REGISTRATION: University Hospital Medical Information Network (UMIN-CTR, https://www.umin.ac.jp/ctr/index.htm, study unique identifier: UMIN000023929).