ArticleCardiovascular therapeutics2026
NHANES-Derived Machine Learning Model for Early Identification of Frailty Risk in CKM: Optimizing Resource Allocation Through Predictive Analytics.
Article in Cardiovascular therapeutics, 2026. 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.
- NHANES-Derived Machine Learning Model for Early Identification of Frailty Risk in CKM: Optimizing Resource Allocation Through Predictive Analytics.Cardiovascular therapeutics · 2026Article
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
backgroundCKM encompasses a complex group of disease states involving cardiovascular, kidney, and metabolic dysfunction. Frailty is a significant health issue among older adults and is closely associated with adverse outcomes. However, there is currently a lack of predictive tools specifically designed for assessing frailty risk in middle-aged and older patients with CKM Stages 0-3.
objectiveThis study is aimed at developing a machine learning model to predict frailty risk in this population and to identify key predictive factors.
methodsUsing data from NHANES 2007-2018, a total of 6749 participants aged 40 years and older were included. Feature selection was conducted using LASSO regression, and the cohort was randomly divided into training and testing sets in a ratio of 7:3. Five machine learning models LR, LightGBM, XGBoost, RF, and DT were compared. Model performance was evaluated via 10-fold cross-validation, and the optimal model was selected based on accuracy, AUC, precision, recall, and F1 score. Feature importance was further explored using SHAP values.
resultsAmong the 6749 participants, 1427 (21.14%) were identified as frail. LASSO regression identified 21 important variables, which were used to construct the five machine learning models. The RF model achieved the best performance in predicting frailty risk, as demonstrated by its AUC (0.90), accuracy (0.81), precision (0.54), recall (0.84), and F1 score (0.66). Decision curve analysis and calibration plots further confirmed the clinical utility of this approach. SHAP analysis revealed that the most significant predictors were PIR, antihypertensive medication use, hemoglobin, red blood cell count, albumin, and diabetes.
conclusionThe machine learning model developed in this study can effectively predict the risk of frailty in middle-aged and older adults with CKM Stages 0-3, providing a valuable tool for early identification of high-risk individuals in clinical practice. This model may help optimize healthcare resource allocation, guide-targeted interventions, and improve health management for patients with CKM.
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.