ArticlePeerJ2024
Construction and validation of a predictive model for the risk of malnutrition in hospitalized patients over 65 years of age with malignant tumours: a single-centre retrospective cross-sectional study.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction model for malnutrition in older adults: a systematic review.BMC geriatrics · 2026Pooled it
- Risk prediction models for malnutrition in patients with cancer: a systematic review.Frontiers in nutrition · 2026Pooled it
- Machine learning model for predicting malnutrition risk in lung cancer patients after thoracoscopic resection: a multi-center study.Frontiers in oncology · 2026Article
- Response to the letter regarding 'Construction and validation of a nomogram prediction model for predicting the risk of chemotherapy-induced myelosuppression after chemotherapy in patients with triple-negative breast cancer'.Translational cancer research · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: Nutritional status is a critical indicator of overall health in individuals suffering from malignant tumours, reflecting the complex interplay of various contributing factors. This research focused on identifying and analysing the factors influencing malnutrition among older patients aged ≥65 with malignant tumours and aimed to develop a comprehensive risk model for predicting malnutrition. Methods: This study conducted a retrospective analysis of clinical data from 3,387 older inpatients aged ≥65 years with malignant tumours collected at our hospital from July 1, 2021, to December 31, 2023. The dataset was subsequently divided into training and validation sets at an 8:2 ratio. The nutritional status of these patients was evaluated using the Nutritional Risk Screening Tool 2002 (NRS-2002) and the 2018 Global Leadership Initiative on Malnutrition (GLIM) Standards for Clinical Nutrition and Metabolism. Based on these assessments, patients were categorized into either malnutrition or non-malnutrition groups. Subsequently, a risk prediction model was developed and presented through a nomogram for practical application. Results: The analysis encompassed 2,715 individuals in the development cohort and 672 in the validation cohort, with a malnutrition prevalence of 40.42%. A significant positive correlation between the incidence of malnutrition and age was observed. Independent risk factors identified included systemic factors, tumour staging (TNM stage), age, Karnofsky Performance Status (KPS) score, history of alcohol consumption, co-infections, presence of ascites or pleural effusion, haemoglobin (HGB) levels, creatinine (Cr), and the neutrophil-to-lymphocyte ratio (NLR). The predictive model exhibited areas under the curve (AUC) of 0.793 (95% confidence interval (CI) [0.776-0.810]) for the development cohort and 0.832 (95% CI [0.801-0.863]) for the validation cohort. Calibration curves indicated Brier scores of 0.186 and 0.190, while the Hosmer-Lemeshow test yielded chi-square values of 5.633 and 2.875, respectively ( Conclusion: This study successfully devised a straightforward and efficient prediction model for malnutrition among older patients aged 65 and above with malignant tumours. The model represents a significant advancement as a clinical tool for identifying individuals at high risk of malnutrition, enabling early intervention with targeted nutritional support and improving patient outcomes.
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