Observational studyMedicine2025
Prediction of medication nonadherence in patients with lung cancer based on nomogram model construction.
Observational study in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Lung cancer is the leading cause of death among patients with cancer. Medication nonadherence affects survival time and remission of disease symptoms in patients with lung cancer. Therefore, this study analyzed the risk factors for medication nonadherence in patients with lung cancer and established a nomogram prediction model. The basic information and clinical characteristics of patients with lung cancer were collected from the Affiliated Hospital of Chengde Medical University from April 2020 to March 2023. The Chinese version of the Morisky Medication Adherence Questionnaire-8 was used to evaluate patients' medication adherence. A least absolute shrinkage and selection operator regression model and multivariate logistic regression analysis were used to identify the risk factors for medication nonadherence and establish a nomogram prediction model. The predictive ability of the nomogram was evaluated using the concordance index (C-index) and the area under the operating characteristic curve. Decision curve analysis (DCA) and the clinical impact curve were used to assess the potential clinical value of the nomogram. A total of 161 patients with lung cancer were included in this study, with a medication nonadherence rate of 47.20%. Risk factors included age, surgery, education level, bone metastases, comorbidities, well-being, and constipation. The C-index and area under the operating characteristic curve were 0.946. The threshold probability for DCA ranged from 0.07 to 0.95. The nomogram can predict the risk of medication nonadherence in patients with lung cancer and help identify those at risk early in clinical settings, allowing for the development of intervention programs and improved clinical management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.