T Nakamura1, K Kamiya2, A Matsunaga3, N Hamazaki4, R Matsuzawa5, K Nozaki5, S Tanaka6, M Yamashita1, E Maekawa7, C Noda7, M Yamaoka-Tojo8, T Masuda9, J Ako7. 1. Department of Rehabilitation Sciences, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan. 2. Department of Rehabilitation Sciences, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan; Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Sagamihara, Japan. Electronic address: k-kamiya@kitasato-u.ac.jp. 3. Department of Rehabilitation Sciences, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan; Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Sagamihara, Japan. 4. Department of Rehabilitation, Kitasato University Hospital, Sagamihara, Japan; Department of Cardiovascular Medicine, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan. 5. Department of Rehabilitation, Kitasato University Hospital, Sagamihara, Japan. 6. Department of Cardiovascular Medicine, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan. 7. Department of Cardiovascular Medicine, Kitasato University School of Medicine, Sagamihara, Japan. 8. Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Sagamihara, Japan. 9. Department of Rehabilitation, Kitasato University School of Allied Health Sciences, Sagamihara, Japan; Department of Cardiovascular Medicine, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan.
Abstract
BACKGROUND AND AIM: Arm circumference (AC) and nutritional screening tools have been shown to have prognostic capability in patients with cardiovascular disease (CVD). This study aimed to compare the prognostic predictive capabilities of AC and nutritional screening tools in older patients with CVD. METHODS AND RESULTS: The study population consisted of 949 admitted patients ≥60 years old with CVD. Patients underwent AC measurement and nutritional screening before hospital discharge. We used the controlling nutritional status index (CONUT), the geriatric nutritional risk index (GNRI), and the prognostic nutritional index (PNI) as nutritional screening tools. The end point of the study was all-cause mortality. The mean age of the study population was 72.3 ± 7.2 years, and 68.2% of the patients were male. A total of 130 deaths occurred over a median follow-up period of 2.2 years (interquartile range, 1.1-3.8 years). After adjusting for other prognostic factors, AC (hazard ratio [HR]: 0.59; p < 0.001), CONUT (HR: 0.82; p = 0.016), GNRI (HR: 0.77; p = 0.040), and PNI (HR: 0.80; p = 0.014) were significant predictors of mortality. However, adding AC to the multivariate-adjusted model (0.739 vs. 0.714, respectively; p = 0.037), but not CONUT, GNRI, or PNI (0.724, 0.717, and 0.723 vs. 0.714; p = 0.072, p = 0.306, and p = 0.127, respectively), significantly increased the area under the curve on receiver operating characteristic curve. CONCLUSIONS: AC, but not nutritional screening tools, plays a complementary role to preexisting prognostic factors for predicting prognosis in older patients with CVD.
BACKGROUND AND AIM: Arm circumference (AC) and nutritional screening tools have been shown to have prognostic capability in patients with cardiovascular disease (CVD). This study aimed to compare the prognostic predictive capabilities of AC and nutritional screening tools in older patients with CVD. METHODS AND RESULTS: The study population consisted of 949 admitted patients ≥60 years old with CVD. Patients underwent AC measurement and nutritional screening before hospital discharge. We used the controlling nutritional status index (CONUT), the geriatric nutritional risk index (GNRI), and the prognostic nutritional index (PNI) as nutritional screening tools. The end point of the study was all-cause mortality. The mean age of the study population was 72.3 ± 7.2 years, and 68.2% of the patients were male. A total of 130 deaths occurred over a median follow-up period of 2.2 years (interquartile range, 1.1-3.8 years). After adjusting for other prognostic factors, AC (hazard ratio [HR]: 0.59; p < 0.001), CONUT (HR: 0.82; p = 0.016), GNRI (HR: 0.77; p = 0.040), and PNI (HR: 0.80; p = 0.014) were significant predictors of mortality. However, adding AC to the multivariate-adjusted model (0.739 vs. 0.714, respectively; p = 0.037), but not CONUT, GNRI, or PNI (0.724, 0.717, and 0.723 vs. 0.714; p = 0.072, p = 0.306, and p = 0.127, respectively), significantly increased the area under the curve on receiver operating characteristic curve. CONCLUSIONS: AC, but not nutritional screening tools, plays a complementary role to preexisting prognostic factors for predicting prognosis in older patients with CVD.
Authors: Carlos Serón-Arbeloa; Lorenzo Labarta-Monzón; José Puzo-Foncillas; Tomas Mallor-Bonet; Alberto Lafita-López; Néstor Bueno-Vidales; Miguel Montoro-Huguet Journal: Nutrients Date: 2022-06-09 Impact factor: 6.706