GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • He, Xiumei  (2)
  • 1
    Online Resource
    Online Resource
    Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer
    Cambridge University Press (CUP) ; 2022
    In:  British Journal of Nutrition Vol. 127, No. 10 ( 2022-05-28), p. 1506-1516
    Details
    In: British Journal of Nutrition, Cambridge University Press (CUP), Vol. 127, No. 10 ( 2022-05-28), p. 1506-1516
    Abstract: The present study evaluated whether fat mass assessment using the triceps skinfold (TSF) thickness provides additional prognostic value to the Global Leadership Initiative on Malnutrition (GLIM) framework in patients with lung cancer (LC). We performed an observational cohort study including 2672 LC patients in China. Comprehensive demographic, disease and nutritional characteristics were collected. Malnutrition was retrospectively defined using the GLIM criteria, and optimal stratification was used to determine the best thresholds for the TSF. The associations of malnutrition and TSF categories with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HR). Malnutrition was identified in 808 (30·2 %) patients, and the best TSF thresholds were 9·5 mm in men and 12 mm in women. Accordingly, 496 (18·6 %) patients were identified as having a low TSF. Patients with concurrent malnutrition and a low TSF had a 54 % (HR = 1·54, 95 % CI = 1·25, 1·88) greater death hazard compared with well-nourished individuals, which was also greater compared with malnourished patients with a normal TSF (HR = 1·23, 95 % CI = 1·06, 1·43) or malnourished patients without TSF assessment (HR = 1·31, 95 % CI = 1·14, 1·50). These associations were concentrated among those patients with adequate muscle mass (as indicated by the calf circumference). Additional fat mass assessment using the TSF enhances the prognostic value of the GLIM criteria. Using the population-derived thresholds for the TSF may provide significant prognostic value when used in combination with the GLIM criteria to guide strategies to optimise the long-term outcomes in patients with LC.
    Type of Medium: Online Resource
    ISSN: 0007-1145 , 1475-2662
    URL: Article
    DOI: 10.1017/S0007114521002531
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016047-1
    SSG: 12
    SSG: 21
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Classification Tree–Based Machine Learning to Visualize and Validate a Decision Tool for Identifying Malnutrition in Cancer Patients
    Wiley ; 2021
    In:  Journal of Parenteral and Enteral Nutrition Vol. 45, No. 8 ( 2021-11), p. 1736-1748
    Details
    In: Journal of Parenteral and Enteral Nutrition, Wiley, Vol. 45, No. 8 ( 2021-11), p. 1736-1748
    Abstract: The newly proposed Global Leadership Initiative on Malnutrition (GLIM) framework is promising to gain global acceptance for diagnosing malnutrition. However, the role of machine learning in facilitating its application in clinical practice remains largely unknown. Methods We performed a multicenter, observational cohort study including 3998 patients with cancer. Baseline malnutrition was defined using the GLIM criteria, and the study population was randomly divided into a derivation group (n = 2998) and a validation group (n = 1000). A classification and regression trees (CART) algorithm was used to develop a decision tree for classifying the severity of malnutrition in the derivation group. Model performance was evaluated in the validation group. Results GLIM criteria diagnosed 588 patients (14.7%) with moderate malnutrition and 532 patients (13.3%) with severe malnutrition among the study population. The CART cross‐validation identified 5 key predictors for the decision tree construction, including age, weight loss within 6 months, body mass index, calf circumference, and the Nutritional Risk Screening 2002 score. The decision tree showed high performance, with an area under the curve of 0.964 (κ = 0.898, P 〈 .001, accuracy = 0.955) in the validation group. Subgroup analysis showed that the model had apparently good performance in different cancers. Among the 5 predictors constituting the tree, age contributed the least to the classification power. Conclusion Using the machine learning, we visualized and validated a decision tool based on the GLIM criteria that can be conveniently used to accelerate the pretreatment identification of malnutrition in patients with cancer.
    Type of Medium: Online Resource
    ISSN: 0148-6071 , 1941-2444
    URL: Issue
    URL: Article
    DOI: 10.1002/jpen.v45.8
    DOI: 10.1002/jpen.2070
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2170060-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...