Development of a Lasso-Nomogram-Based Risk Prediction Model for Cognitive Frailty in Elderly MHD Patients
Bibliographic record
Abstract
Introduction: Cognitive frailty is increasingly recognized among older adults receiving maintenance hemodialysis (MHD), yet effective screening tools tailored for this population are lacking. This study aimed to develop a predictive model to identify MHD patients of advanced age who are at high risk for cognitive frailty, thereby facilitating early detection and intervention. Methods: A cross-sectional study was conducted between February and December 2024, enrolling 223 older individuals undergoing MHD at a tertiary hospital in Nanjing, China. Data on demographic and clinical characteristics were collected, along with assessments using standardized instruments, including the Kidney Disease Quality of Life Instrument, Geriatric Depression Scale-5, Subjective Cognitive Decline Questionnaire-9, Montreal Cognitive Assessment, Clinical Dementia Rating, Fried Frailty Phenotype, and Cognitive Reserve Index Questionnaire. Least absolute shrinkage and selection operator (LASSO) regression was used to identify relevant variables, which were subsequently entered into multivariate logistic regression to determine independent risk factors. A nomogram was constructed based on the final model. Results: Cognitive frailty was identified in 85 patients, indicating a prevalence of 38.1%. Eight variables were found to be independent risk factors: serum phosphorus, hemoglobin level, depression score, cognitive reserve, age, dialysis duration, dialysis adequacy (Kt/V), and intradialytic hypotension. The predictive nomogram showed excellent discriminative performance, with an area under the receiver operating characteristic curve of 0.986 (95% confidence interval: 0.970-0.999), sensitivity of 94.9%, and specificity of 97.6%. Decision curve analysis demonstrated favorable clinical utility. Conclusion: Cognitive frailty is prevalent among older patients receiving MHD. The nomogram incorporating eight key variables provides a practical tool for early screening and personalized intervention in this high-risk population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".