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A total of 1, 234 patients [mean age, 87 (82-90) years; male/female, 906/328] were enrolled in this study. The proportion of nutritional risk, as defined by an NRS-2002 score ≥ 3, was 38.82% (479 cases). The clinical characteristics of patients with and without nutritional risk are presented in Table 1. There was a significant difference in age; BMI; CC; albumin, prealbumin, hemoglobin, and serum iron levels; and prevalence of neoplasms between the two groups. CC values were significantly lower in patients with nutritional risk compared to those without nutritional risk. However, there were no significant differences in sex; serum folic acid, vitamin B12, and 25(OH)D levels; drinking and smoking habit; and prevalence of cerebral infarction, COPD, coronary artery disease, diabetes, hypertension, and hyperlipidemia observed between patients with and without nutritional risk.
Table 1. Clinical and Biochemical Characteristics of Nutritional Risk and Non-nutritional Risk
Variable Nutritional Risk
(n = 479)Non-Nutritional Risk
(n = 755)P Age (years) 87.000 (83.000-90.000) 85.000 (81.000-89.000) 0.003 Gender (male%) 73.486 78.278 0.308 Body mass index (kg/m2) 20.761 (19.002-23.441) 23.945 (21.912-26.061) < 0.001 Calf circumference (cm) 27.000 (24.500-31.000) 31.000 (29.000-33.500) < 0.001 Prealbumin (mg/L) 180.384 ± 70.571 208.183 ± 52.156 < 0.001 Albumin (g/dL) 37.000 (33.000-40.000) 40.000(38.000-43.000) < 0.001 Hemoglobin (g/dL) 109.043 (93.251 ± 122.756) 124.021(112.752-134.078) < 0.001 Serum iron (μmol/L) 8.711 ± 4.446 13.657 ± 5.671 < 0.001 Folic acid (μg/L) 7.817 (5.156-13.313) 7.362 (4.741-11.687) 0.377 Vitamin B12 (ng/L) 805.951 (525.533-1158.752) 667.551 (457.482-962.933) 0.082 25-hydroxyitamin D (ng/mL) 16.122 (12.061-25.676) 17.377 (11.348-23.972) 0.896 Smoking (%) 18.998 17.881 0.821 Drinking (%) 3.555 3.179 0.184 Cerebral infarction (%) 56.576 47.417 < 0.001 Chronic obstructive pulmonary disease (%) 33.612 25.430 < 0.001 Coronary heart disease (%) 65.344 61.192 0.437 Diabetes (%) 36.326 33.377 0.001 Hypertension (%) 85.803 84.768 0.863 Hyperlipidemia (%) 18.580 30.199 < 0.001 Neoplasms (%) 23.800 11.258 < 0.001 To determine the influencing factors of CC, a correlation analysis between clinical and biochemical parameters and CC was conducted. It was found that CC was negatively correlated with age and nutritional risk. Moreover, CC was positively associated with the hemoglobin, albumin, prealbumin, and serum iron levels and prevalence of hyperlipidemia (Table 2).
Table 2. Correlation Analysis of Clinical and Biochemical Parameters with Calf Circumference
Variable r P Age -0.296 < 0.001 Body mass index 0.129 0.013 Hemoglobin 0.409 < 0.001 Albumin 0.320 < 0.001 Prealbumin 0.211 < 0.001 Folic acid -0.042 0.471 Vitamin B12 -0.070 0.232 Serum iron 0.335 < 0.001 25-hydroxyitamin D 0.097 0.116 Nutritional risk -0.331 < 0.001 To determine which factors were independently associated with nutritional risk, logistic regression was performed. Independent variables were indicated as risk factors, including age, BMI, hemoglobin level, albumin level, cerebral infarction, COPD, diabetes, hyperlipidemia, neoplasms, and CC. A logistic regression analysis of nutritional risk revealed that BMI (OR, 0.906; 95% CI, 0.856-4.636; P = 0.001), albumin level (OR = 0.845; 95% CI, 0.804-0.889; P < 0.001), hemoglobin level (OR = 0.984; 95% CI: 0.973-0.995; P = 0.004), cerebral infarction (OR = 1.788; 95% CI: 1.240-2.579; P = 0.002), neoplasms (OR = 2.90, 95% CI: 1.814-4.636; P < 0.001), and CC (OR = 0.897; 95% CI: 0.856-0.941; P < 0.001) were independent impact factors of nutritional risk (Table 3).
Table 3. Independent Factors for Nutritional Risk by Multivariable Logistic Regression Analysis
Factors β S.E Wald OR (95% CI) P BMI -0.098 0.029 11.351 0.906 (0.856-4.636) 0.001 Albumin -0.168 0.026 43.225 0.845 (0.804-0.889) < 0.001 Hemoglobin -0.016 0.006 8.346 0.984 (0.973-0.995) < 0.001 Cerebral infarction 0.581 0.187 9.673 1.788 (1.240-2.579) 0.002 Neoplasms 1.065 0.239 19.789 2.900 (1.814-4.636) < 0.001 Calf circumference -0.108 0.024 19.908 0.897 (0.856-0.941) < 0.001 The prevalence of nutritional risk in CC quartiles was also compared. The results showed that it increased with a decrease in CC. Patients in the first CC quartile had significantly greater nutritional risk compared with those in other quartiles. Compared with patients in Quartile 4 (reference), those in Quartile 3 (OR = 5.197, 95% CI: 2.999-9.008, P < 0.001), Quartile 2 (OR = 8.559, 95% CI: 4.081-17.953, P < 0.001), and Quartile 1 (OR = 9.281, 95% CI: 4.773-18.473, P < 0.001) had greater nutritional risk (Table 4).
Table 4. Nutritional Risk Prevalence in Different Calf Circumference Quartiles
CC OR (95% CI) P Q4 (≥ 33.0) 1 (reference) / Q3 (31.0-33.0) 5.197 (2.999-9.008) < 0.001 Q2 (27.5-31.0) 8.559 (4.081-17.953) < 0.001 Q1 (< 27.5) 9.281 (4.773-18.475) < 0.001 Because CC was an independent correlative factor for nutritional risk, ROC curve analysis was performed to reveal the optimal cutoff point of CC, predicting nutritional risk. In men, the cutoff point was 29.75 cm, the AUC was 0.771 (95% CI: 0.711-0.831, the Youden index at this level was 0.466, and its sensitivity and specificity were 75.5% and 71.1%, respectively. In women, the cutoff point was 28.25 cm, the AUC was 0.715 (95% CI: 0.589-0.840), the Youden index at this level was 0.437, and its sensitivity and specificity were 65.1% and 78.6%, respectively (Figure 1).
Figure 1. ROC curve analysis of CC for nutritional risk. (A) Men: AUC = 0.771 (P < 0.001); 95% CI, 0.711-0.831; identified CC cutoff point = 29.75 cm; Youden index = 0.466; sensitivity, 75.5%; specificity, 71.1%. (B) Women: AUC = 0.715 (P < 0.001), 95% CI, 0.589-0.840; identified CC cutoff point = 28.25 cm, Youden index = 0.437; sensitivity, 65.1%; specificity, 78.6%.
doi: 10.3967/bes2019.075
Low Calf Circumference Predicts Nutritional Risks in Hospitalized Patients Aged More Than 80 Years
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Abstract:
Objective The aim of this study wasto determine whether low calf circumference (CC) could predict nutritional risk and the cutoff values of CC for predicting nutritional risk in hospitalized patients aged ≥ 80 years. Methods A total of 1, 234 consecutive patients aged ≥ 80 years were enrolled in this study. On admission, demographic data, CC, and laboratory parameters were obtained. Patients with Nutritional Risk Screening 2002 (NRS-2002) total score ≥ 3 were considered as having nutritional risk. Results CC values were significantly lower in patients with nutritional risk compared to those in patients without nutritional risk[27.00 (24.50-31.00) vs. 31.00 (29.00-33.50], P < 0.001]. CC was negatively correlated with age and nutritional risk scores. Logistic regression analysis of nutritional risk revealed that body mass index, albumin level, hemoglobin level, cerebral infarction, neoplasms, and CC (OR, 0.897; 95% confidence interval, 0.856-0.941; P < 0.001) were independent impact factors of nutritional risk. Nutritional risk scores increased with a decrease in CC. In men, the best CC cutoff value for predicting nutritional risk according to the NRS-2002 was 29.75 cm. In women, the cutoff value was 28.25 cm. Conclusion CC is a simple, noninvasive, and valid anthropometric measure to predict nutritional risk for hospitalized patients aged ≥ 80 years. -
Key words:
- Calf circumference /
- Nutritional risk /
- NRS-2002
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Figure 1. ROC curve analysis of CC for nutritional risk. (A) Men: AUC = 0.771 (P < 0.001); 95% CI, 0.711-0.831; identified CC cutoff point = 29.75 cm; Youden index = 0.466; sensitivity, 75.5%; specificity, 71.1%. (B) Women: AUC = 0.715 (P < 0.001), 95% CI, 0.589-0.840; identified CC cutoff point = 28.25 cm, Youden index = 0.437; sensitivity, 65.1%; specificity, 78.6%.
Table 1. Clinical and Biochemical Characteristics of Nutritional Risk and Non-nutritional Risk
Variable Nutritional Risk
(n = 479)Non-Nutritional Risk
(n = 755)P Age (years) 87.000 (83.000-90.000) 85.000 (81.000-89.000) 0.003 Gender (male%) 73.486 78.278 0.308 Body mass index (kg/m2) 20.761 (19.002-23.441) 23.945 (21.912-26.061) < 0.001 Calf circumference (cm) 27.000 (24.500-31.000) 31.000 (29.000-33.500) < 0.001 Prealbumin (mg/L) 180.384 ± 70.571 208.183 ± 52.156 < 0.001 Albumin (g/dL) 37.000 (33.000-40.000) 40.000(38.000-43.000) < 0.001 Hemoglobin (g/dL) 109.043 (93.251 ± 122.756) 124.021(112.752-134.078) < 0.001 Serum iron (μmol/L) 8.711 ± 4.446 13.657 ± 5.671 < 0.001 Folic acid (μg/L) 7.817 (5.156-13.313) 7.362 (4.741-11.687) 0.377 Vitamin B12 (ng/L) 805.951 (525.533-1158.752) 667.551 (457.482-962.933) 0.082 25-hydroxyitamin D (ng/mL) 16.122 (12.061-25.676) 17.377 (11.348-23.972) 0.896 Smoking (%) 18.998 17.881 0.821 Drinking (%) 3.555 3.179 0.184 Cerebral infarction (%) 56.576 47.417 < 0.001 Chronic obstructive pulmonary disease (%) 33.612 25.430 < 0.001 Coronary heart disease (%) 65.344 61.192 0.437 Diabetes (%) 36.326 33.377 0.001 Hypertension (%) 85.803 84.768 0.863 Hyperlipidemia (%) 18.580 30.199 < 0.001 Neoplasms (%) 23.800 11.258 < 0.001 Table 2. Correlation Analysis of Clinical and Biochemical Parameters with Calf Circumference
Variable r P Age -0.296 < 0.001 Body mass index 0.129 0.013 Hemoglobin 0.409 < 0.001 Albumin 0.320 < 0.001 Prealbumin 0.211 < 0.001 Folic acid -0.042 0.471 Vitamin B12 -0.070 0.232 Serum iron 0.335 < 0.001 25-hydroxyitamin D 0.097 0.116 Nutritional risk -0.331 < 0.001 Table 3. Independent Factors for Nutritional Risk by Multivariable Logistic Regression Analysis
Factors β S.E Wald OR (95% CI) P BMI -0.098 0.029 11.351 0.906 (0.856-4.636) 0.001 Albumin -0.168 0.026 43.225 0.845 (0.804-0.889) < 0.001 Hemoglobin -0.016 0.006 8.346 0.984 (0.973-0.995) < 0.001 Cerebral infarction 0.581 0.187 9.673 1.788 (1.240-2.579) 0.002 Neoplasms 1.065 0.239 19.789 2.900 (1.814-4.636) < 0.001 Calf circumference -0.108 0.024 19.908 0.897 (0.856-0.941) < 0.001 Table 4. Nutritional Risk Prevalence in Different Calf Circumference Quartiles
CC OR (95% CI) P Q4 (≥ 33.0) 1 (reference) / Q3 (31.0-33.0) 5.197 (2.999-9.008) < 0.001 Q2 (27.5-31.0) 8.559 (4.081-17.953) < 0.001 Q1 (< 27.5) 9.281 (4.773-18.475) < 0.001 -
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