ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2025
A nomogram for predicting cancer-related cognitive impairment in lung cancer patients from a nursing science precision health model perspective.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development of a nomogram to predict 5-year tooth loss after root canal treatment in patients with cracked teeth and chronic irreversible pulpitis.Acta odontologica Scandinavica · 2026Article
- Construction and Validation of a Risk Prediction Model for Cancer-Related Cognitive Impairment in Lung Cancer Patients.International journal of nursing practice · 2026Observational
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Authors and funding
5 authors.
Funding
Abstract
purposeThe nursing science precision health (NSPH) model considers identifying the biological basis of symptoms in order to develop precise intervention strategies that ultimately improve the overall health of the symptomatic individual. This study sought to construct a nomogram for predicting cancer-related cognitive impairment (CRCI) in patients with lung cancer within the context of the NSPH model.
methodsA cohort of 252 patients with lung cancer was prospectively collected and randomly divided into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression method optimized variable selection, followed by multivariate logistic regression to develop a model, which subsequently formed the basis for the nomogram. The nomogram's discrimination and calibration were evaluated using a calibration plot, the Hosmer-Lemeshow test, and the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) quantified the net benefits of the nomogram across various threshold probabilities.
resultsFive pivotal variables were incorporated into the nomogram: age (≥ 65 years), treatment, education level, albumin, and platelet-to-lymphocyte ratio (PLR). The area under the ROC curve (0.970 for the training cohort and 0.973 for the validation cohort) demonstrated the nomogram's excellent discriminative ability. Calibration curves closely aligning with ideal curves indicated accurate predictive capability. Moreover, the nomogram exhibited a positive net benefit for predicted probability thresholds ranging from 1 to 98% in DCA.
conclusionKey risk factors, including advanced age (≥ 65 years), low education level, combined chemotherapy, low albumin, and high PLR, were significantly associated with higher CRCI incidence. This nomogram model has good performance and can help identify CRCI with high accuracy in lung cancer patients.
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Registered trials
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