Malnutrition associated with readmission in elderly patients with chronic obstructive pulmonary disease : Chinese Medical Journal

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Malnutrition associated with readmission in elderly patients with chronic obstructive pulmonary disease

Zhao, Ting; Chen, Jiayan; Li, Xiangyang; Zhu, Yinggang

Editor(s): Wei, Peifang; Pan, Xiangxiang

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Chinese Medical Journal ():10.1097/CM9.0000000000002467, February 21, 2023. | DOI: 10.1097/CM9.0000000000002467

To the Editor: Compared with non-readmission chronic obstructive pulmonary disease (COPD) patients, the mortality rate of readmission patients is significantly higher (2.3% vs. 13.4%),[1] which undoubtedly causes socio-economic pressures on rehabilitation care and medical resources. The potential factors affecting the risk of readmission are required to be identified. The incidence of malnutrition, occurring frequently in elderly COPD patients, varies from 20% to 45% depending on the screening tools used. As a practical, economical and effective scoring system, the Mini Nutrition Assessment (MNA) is of great value to assessing nutritional status in the elderly. Malnutrition is a state of vulnerability in elder populations and is associated with a poor long-term prognosis. This study aimed to research the impact of nutritional status related risk factors on readmission and assess the value of MNA scoring system and updated sarcopenia diagnostic criteria in predicting readmission risk for elderly COPD patients.

This observational prospective study protocol was approved by the Human Research Ethics Committee of Huadong Hospital, Fudan University (No. 2019K125) and has been registered and approved by Chinese Clinical Trial Registry (, chiCTR1900022576). The written informed consent was obtained from each participant.

This study was conducted at the Department of Pulmonary and Critical Care Medicine, Huadong Hospital, between November 2018 and December 2019. Inclusion criteria were: (1) COPD patients diagnosed by the Global Initiative for Chronic Obstructive Lung Disease guideline[2]; (2) patients were admitted to hospital due to acute exacerbation of COPD (AECOPD); (3) age ≥65 years; and (4) patients who met the criteria of discharge with stable condition. The exclusion criteria were: (1) patients who were unable to stand steadily or finish the walk test; (2) patients had a history of pacemaker implantation; (3) severe cognitive impairment; (4) clinically visible edema; and (5) patients who declined to participate. All the candidates, who were admitted to hospital for AECOPD, received the first interview in person to obtain baseline data within 48 h before discharge. Follow-up assessments were offered to all the participants by experienced physicians (TZ and JYC) up to 180 days after discharge, and the follow-up information was obtained from digital medical records or telephone calls with permission.

At the first interview, the information on demographic characteristics, mid-arm circumference, calf circumference (CC), Charlson Comorbidity Index (CCI), drug usage, and laboratory results such as the level of serum albumin was collected. And the smoking information as well as chronic cough history during childhood was also collected. Additionally, pulmonary function tests were performed according to the American Thoracic Society/European Respiratory Society criteria using standardized equipment (Master Screen PFT System, Jager, Germany) at the time of baseline data collection. Besides, dyspnea and the quality of life were evaluated with the modified scale of the Medical Research Council (mMRC) and the COPD assessment test (CAT) questionnaire, respectively. The mMRC score ranges from 0 to 4, and higher score indicates worse dyspnea. The CAT score ranges from 0 to 40, and a score more than 31.0 points indicates a very serious impact on the quality of life. At the time of monthly follow-up, information on readmission events was recorded.

MNA [Supplementary files,] scores were measured to assess nutritional status at baseline. A score <17.0 points was considered as malnutrition, a score between 17.0 points and 23.5 points was considered to be at risk of malnutrition, and well-nourished patients had a MNA score >23.5 points. All participants have completed the assessments within 48 h before discharge.

Sarcopenia was defined following the updated Asian Working Group for Sarcopenia (AWGS): 2019 Consensus.[3] Body composition was measured by bioelectrical impedance analysis (InBody 570, InBody Co., Seoul, Korea) using a standard hand-to-foot tetra-polar technique. The skeletal muscle mass index (SMI) was calculated using the appendicular skeletal muscle mass divided by the square of body height in meters. Low muscle mass was defined as SMI <7.0 kg/m2 in men and SMI <5.7 kg/m2 in women. Muscle strength was assessed by digital handgrip strength using a JAMAR Dynamometer (JAMAR Co., Ltd., DuLuth, Minnesota, USA) in a standard posture. Low muscle strength was defined as handgrip strength <28 kg in men and <18 kg in women. Physical performance was measured by gait speed (GS): a 4-m walking test was performed to measure the usual GS. A score of <0.8 m/s was used to identify low GS.

Statistical differences between groups were assessed with the chi-squared tests, Fisher's exact tests, Mann-Whitney U tests, or one-way analysis of variance tests as appropriate. We used multivariable logistic regression analysis to measure the independent risk factors of sarcopenia. A Cox regression analysis, was performed to investigate risk factors for readmission. The variables with P values <0.10 in univariable analysis were included in multivariable analysis. We created Kaplan–Meier curve of cumulative risk of readmission with log-rank test. Statistical analyses were performed with IBM SPSS Statistics 25.0 (IBM SPSS Inc., Chicago, IL, USA). A two-sided P-value of <0.05 was considered statistically significant. Also, we had conducted collinearity diagnostics by calculating the variance inflation factor (VIF) between MNA scores and sarcopenia. The results showed that VIF is 1.032, lower than 10, indicating no significant multicollinearity between two variables.

Totally 302 COPD patients, including 55.0% (166/302) of male, were enrolled in this study. A total of 54 (17.9%) patients had readmission within 6 months after discharge from hospital. The average age of all patients was 77.0 ± 7.6 years and the mean body mass index (BMI) was 24.3 ± 3.4 kg/m2. Patients with readmission events were significantly elder (79.7 ± 7.9 years vs. 76.4 ± 7.4 years, t = 2.99, P = 0.003), and had lower BMI (23.0 ± 3.2 kg/m2vs. 24.6 ± 3.4 kg/m2, t = −3.14, P = 0.002), skeletal muscle mass index (5.9 ± 1.3 kg/m2vs. 7.2 ± 1.0 kg/m2, in males, t = −6.21, P < 0.001, and 5.4 ± 1.0 kg/m2vs. 5.8 ± 1.3 kg/m2, in females, t = −2.36, P = 0.020) and handgrip strength (22.0 [15.3–31.2] kg vs. 27.0 [22.0–33.2] kg, Z = −3.41, P < 0.001) than those without readmission within 6 months. Higher CAT scores (18.0 [11.8–22.0] vs. 15.0 [10.0–22.0], Z = −1.36, P = 0.001) and lower forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio (57.0 ± 10.4% vs. 61.3 ± 9.1%, t = −2.90, P = 0.004) were also found among patients with readmission events, when compared with patients without readmission within six months. While the levels of serum albumin as well as drug usage were similar between patients with and without readmission. Thirty-one (10.3%) and 84 (27.8%) of all patients were malnourished and at risk of malnutrition according to the MNA score, respectively. Compared with non-readmission group, the CCI was significantly higher in patients with readmission events (3 [2–4) vs. 2 [1–3], Z = −3.74, P < 0.001]. Higher percentages of cerebrovascular diseases (22.2% [12/54] vs. 11.3% [28/248], χ2 = 4.61, P = 0.033), diabetes (25.9% [14/54] vs. 12.9% [32/248], χ2 = 5.82, P = 0.017), and rheumatologic diseases (5.6% [3/54] vs. 0 [0/248], P = 0.005) were found in patients with readmission than those without readmission [Supplementary Table 1,]. Our findings showed that the average time to readmission was 151.2 ± 46.8 days, 157.5 ± 43.3 days, and 171.6 ± 30.1 days among malnourished, risk of malnutrition, and normal nutrition groups, respectively. Also, the mean time from discharge to readmission in sarcopenia patients was significantly shorter than that in non-sarcopenia patients (152.3 ± 49.2 days vs. 172.7 ± 25.6 days; P < 0.001). Moreover, among all readmitted patients, 16.7% (9/54) of patients were hospitalized for no less than twice within 6 months. No statistical significance was found in readmission frequency among different nutritional status groups.

A total of 107 (35.4%) participants were diagnosed as sarcopenia according to the 2019 AWGS criteria. The univariate analysis showed that factors such as BMI, FEV1/FVC ratio, FEV1 of predicted, CAT, and malnutritional status were related to sarcopenia. Multivariate logistic regression analyses revealed that malnutrition (odds ratio [OR] = 2.88, 95% confidence interval [CI]: 1.14–7.23, P = 0.025), BMI (OR = 0.90, 95% CI: 0.83–0.98, P = 0.025), and FEV1/FVC ratio (OR = 0.95, 95% CI: 0.92–0.98, P = 0.003) were more likely to be associated with sarcopenia. Our findings showed that 32.3% (10/31) of malnourished patients were readmitted to hospital after discharge, which occurred in 28.6% (24/84) of patients at risk of malnutrition and 10.7% (20/187) of well-nourished patients. Cox regression analysis suggested that poor nutritional status, sarcopenia, and current smoking were associated with risk of readmission. Patients with malnutrition had a 2.97-fold (95% CI: 1.18–7.47, P = 0.021) of readmission risk compared to those who were well-nourished. Current smokers had a 2.82-fold (95% CI: 1.02–7.80, P = 0.046) of readmission risk compared to those who never smoked. By the end of 6 months follow-up, the incidences of readmission were 22% and 18% higher in patients with malnutrition and at risk of malnutrition than well-nourished patients. The incidence of readmission was 24% in patients with sarcopenia, higher than that in non-sarcopenia patients, as shown in Figure 1.

Figure 1:
Kaplan–Meier curves of the cumulative incidence of non-readmission within 180 days among COPD patients with different nutritional status (A) and with or without sarcopenia (B).

Among all the 54 patients, who suffered readmission within 6 months after discharge, 66.7% (36/54) were due to the AECOPD, and 33.3% (18/54) were readmitted for other causes, including cardiovascular disease, disturbance of consciousness, lung cancer, pleural effusion, pulmonary embolism, etc. Among patients admitted due to AECOPD, 69.4% (25/36) were at worse nutritional status (malnutrition or at risk of malnutrition), and 16.7% (6/36) were current smokers.

In our study, malnutrition accounted for 10.3% of AECOPD hospitalized patients, besides, 27.8% AECOPD hospitalized patients were at risk of malnutrition. The prevalence of at risk of malnutrition was similar to the study of Min Fang Hsu et al.[4] With the threshold of a score <17 as malnutrition, our study revealed that it occurred in 18.7% (20/107) of patients with sarcopenia, and malnutrition was also an independent risk factor for the presence of sarcopenia. Existing evidence suggested that poor nutritional status among elderly COPD patients, even at risk of malnutrition, would be accompanied with the elevated resting energy consumption, the increased systemic protein turnover and higher levels of adenosine triphosphate consumption during muscle contraction. The disease-related increases in daily metabolism and the energy imbalance of intake and consumption played a role in muscle atrophy and functional decline. Given that skeletal muscle function (muscle strength and endurance), was closely related to muscle mass, COPD patients with either poor nutritional status or sarcopenia may be more vulnerable to weakness as well as the next exacerbation of disease.

Existing evidence has demonstrated that sarcopenia is a risk factor for all-cause mortality,[5] and in hospitalized patients with AECOPD, the group with muscle loss had higher in-hospital mortality, longer average hospital stay and higher hospitalization cost. In our study, the mean time to readmission was significantly shorter in patients with sarcopenia, suggesting that sarcopenia, diagnosed by updated sarcopenia diagnostic criteria, can be used as an indicator to predict poor prognosis of COPD patients as well as to offer practical interventions, including dietary modification and rehabilitation strategies to improve nutritional status against sarcopenia.

However, the study has some limitations. First, our study was conducted in a single center, with the limited participants focusing on elderly hospitalized patients in a tertiary hospital. Second, the proportion of female participant in this study is relatively higher. Thirdly, the measurements of body composition, muscle strength, and serum sample tests were only performed at baseline due to the limited funds and labor resources.

In summary, our findings suggested that worse nutritional status identified by MNA score system as well as sarcopenia which was diagnosed with the updated criteria were supposed to be reliable predictors for readmission of COPD patients, which help to better understand the value of screening for nutritional status and sarcopenia in elderly COPD individuals as well as to provide supporting evidence on the necessity of taking nutritional precautions for preventing readmission of COPD patients.


This work was supported by grants from the National Natural Science Foundation of China (Nos. 81900072 and 82170092).

Conflicts of interest



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