ArticleFrontiers in surgery2025
Development and validation of a frailty risk prediction model in patients with peripheral artery disease.
Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate the current status and risk factors of frailty among patients with peripheral artery disease, and to develop a risk prediction model to inform targeted clinical interventions. Methods: Patients were consecutively recruited for this investigation from August 2024 to May 2025. The study included 499 individuals with peripheral artery disease who were hospitalized in the vascular surgery department of a tertiary hospital in Southwest China. Data were collected using a general information questionnaire, laboratory test results, the Barthel Index, and the Social Support Rating Scale. The Tilburg Frailty Indicator was used to classify patients into a non-frailty group and a frailty group. The dataset was randomly split in a 7:3 ratio into a training set and a validation set. Independent predictors of frailty were identified through univariate and multivariate logistic regression analyses. The risk prediction model was developed using R software. Discrimination of the model was evaluated by plotting receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC), sensitivity, and specificity in both the training and validation sets. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration curves. Clinical utility was evaluated using decision curve analysis. Results: Age, hemoglobin level, number of comorbidities, and activities of daily living were identified as independent risk factors for frailty. In the training set, the AUC was 0.771 (95% CI: 0.721-0.821), with a sensitivity of 0.788 and a specificity of 0.808. In the validation set, the AUC was 0.704 (95% CI: 0.620-0.788), with a sensitivity of 0.743 and a specificity of 0.682. The Hosmer-Lemeshow test indicated good calibration in both the training set ( Conclusion: The developed risk prediction model exhibited predictive performance and can assist clinical healthcare providers in identifying populations at high risk of frailty among patients with PAD, thereby providing a reference for developing intervention strategies targeting relevant risk factors.
Indexed as
Identifiers
What Socratic 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 Socratic graph.