PMID- 37715131
OWN - NLM
STAT- MEDLINE
DCOM- 20230918
LR  - 20231123
IS  - 1471-2318 (Electronic)
IS  - 1471-2318 (Linking)
VI  - 23
IP  - 1
DP  - 2023 Sep 15
TI  - A nomogram model for predicting malnutrition among older hospitalized patients 
      with type 2 diabetes: a cross-sectional study in China.
PG  - 565
LID - 10.1186/s12877-023-04284-4 [doi]
LID - 565
AB  - BACKGROUND: Malnutrition remains a pervasive issue among older adults, a 
      prevalence that is markedly higher among those diagnosed with diabetes. The 
      primary objective of this study was to develop and validate a risk prediction 
      model that can accurately identify instances of malnutrition among elderly 
      hospitalized patients with type 2 diabetes mellitus (T2DM) within a Chinese 
      demographic. METHODS: This cross-sectional study was conducted between August 
      2021 and August 2022, we enrolled T2DM patients aged 65 years and above from 
      endocrinology wards. The creation of a nomogram for predicting malnutrition was 
      based on risk factors identified through univariate and multivariate logistic 
      regression analyses. The predictive accuracy of the model was evaluated by the 
      receiver operating characteristic curve (ROC),the area under the ROC (AUC), the 
      concordance index (C-index), and calibration curves. RESULTS: The study included 
      a total of 248 older T2DM patients, with a recorded malnutrition prevalence of 
      26.21%. The identified critical risk factors for malnutrition in this cohort were 
      body mass index, albumin, impairment in activities of daily living, dietary 
      habits, and glycosylated hemoglobin. The AUC of the nomogram model reached 0.914 
      (95% CI: 0.877-0.951), with an optimal cutoff value of 0.392. The model 
      demonstrated a sensitivity of 80.0% and a specificity of 88.5%. Bootstrap-based 
      internal verification results revealed a C-index of 0.891, while the calibration 
      curves indicated a strong correlation between the actual and predicted 
      malnutrition risks. CONCLUSIONS: This study underscores the critical need for 
      early detection of malnutrition in older T2DM patients. The constructed nomogram 
      represents a practical and reliable tool for the rapid identification of 
      malnutrition among this vulnerable population.
CI  - (c) 2023. BioMed Central Ltd., part of Springer Nature.
FAU - Ran, Qian
AU  - Ran Q
AD  - Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of 
      Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, 
      People's Republic of China.
FAU - Zhao, Xili
AU  - Zhao X
AD  - Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of 
      Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, 
      People's Republic of China. 300313@hospital.cqmu.edu.cn.
FAU - Tian, Jiao
AU  - Tian J
AD  - Department of Endocrinology, Jiangnan Campus, the Second Affiliated Hospital of 
      Chongqing Medical University, Tianwen Street, Nanan District, Chongqing, 401336, 
      People's Republic of China.
FAU - Gong, Siyuan
AU  - Gong S
AD  - Department of Neurology, the Second Affiliated Hospital of Chongqing Medical 
      University, Chongqing, 401336, People's Republic of China.
FAU - Zhang, Xia
AU  - Zhang X
AD  - Department of Endocrinology, Chongqing City Hospital of Traditional Chinese 
      Medicine, Chongqing, 400011, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20230915
PL  - England
TA  - BMC Geriatr
JT  - BMC geriatrics
JID - 100968548
SB  - IM
MH  - Aged
MH  - Humans
MH  - *Diabetes Mellitus, Type 2/complications/diagnosis/epidemiology
MH  - Cross-Sectional Studies
MH  - Activities of Daily Living
MH  - Nomograms
MH  - China/epidemiology
MH  - *Malnutrition/diagnosis/epidemiology
PMC - PMC10503093
OTO - NOTNLM
OT  - Malnutrition
OT  - Nomogram
OT  - Older
OT  - Prediction model
OT  - Risk factors
OT  - Type 2 diabetes
COIS- The authors declare no competing interests.
EDAT- 2023/09/16 05:42
MHDA- 2023/09/18 12:43
PMCR- 2023/09/15
CRDT- 2023/09/15 23:38
PHST- 2022/12/14 00:00 [received]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/09/18 12:43 [medline]
PHST- 2023/09/16 05:42 [pubmed]
PHST- 2023/09/15 23:38 [entrez]
PHST- 2023/09/15 00:00 [pmc-release]
AID - 10.1186/s12877-023-04284-4 [pii]
AID - 4284 [pii]
AID - 10.1186/s12877-023-04284-4 [doi]
PST - epublish
SO  - BMC Geriatr. 2023 Sep 15;23(1):565. doi: 10.1186/s12877-023-04284-4.