Impact of malnutrition on survival and healthcare utilization in Medicare beneficiaries with diabetes: a retrospective cohort analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the impact of pre-existing malnutrition on survival and economic implications in elderly patients with diabetes. RESEARCH DESIGN AND METHODS: A retrospective observational study was conducted to examine the impact of malnutrition with or without other significant health conditions on survival time and healthcare costs using the Centers for Medicare and Medicaid Services (CMS) data from 1999 to 2014 for beneficiaries with a confirmed first date of initial diagnosis of diabetes (n=15 121 131). The primary outcome was survival time, which was analyzed using all available data and after propensity score matching. Healthcare utilization cost was a secondary outcome. RESULTS: A total of 801 272 beneficiaries were diagnosed with malnutrition. The analysis on propensity score-matched data for the effect of common conditions on survival showed that the risk for death in beneficiaries with diabetes increased by 69% in malnourished versus normo-nourished (HR, 1.69; 99.9% CI 1.64 to 1.75; P<0.0001) beneficiaries. Malnutrition increased the risk for death within each of the common comorbid conditions including ischemic heart disease (1.63; 1.58 to 1.68), chronic obstructive pulmonary disorder (1.60; 1.55 to 1.65), stroke or transient ischemic attack (1.57; 1.53 to 1.62), heart failure (1.54; 1.50 to 1.59), chronic kidney disease (1.50; 1.46 to 1.55), and acute myocardial infarction (1.47; 1.43 to 1.52). In addition, the annual total spending for the malnourished beneficiaries was significantly greater than that for the normo-nourished beneficiaries ($36 079 vs 20 787; P<0.0001). CONCLUSIONS: Malnutrition is a significant comorbidity affecting survival and healthcare costs in CMS beneficiaries with diabetes. Evidence-based clinical decision pathways need to be developed and implemented for appropriate screening, assessment, diagnosis and treatment of malnourished patients, and to prevent malnutrition in normo-nourished patients with diabetes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".