The Kidney Failure Risk Equation Score and CKD Care Delivery Measures: A Cross-sectional Study
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: The 4-variable kidney failure risk equation (KFRE) allows for the prediction of chronic kidney disease (CKD) progression using age, sex, estimated glomerular filtration rate, and urine albumin/creatinine ratio. Electronic health records enable KFRE auto-calculation, and registries allow population-level application. We assessed whether 2-year KFRE score categories are associated with CKD care metrics. STUDY DESIGN: Cross-sectional cohort. SETTING & PARTICIPANTS: This study included individuals with CKD in March 2020 who were receiving care within the Partners HealthCare system in Massachusetts. OUTCOMES: The presence of sufficient data to calculate the KFRE and, among those with a KFRE score, performance on CKD clinical care metrics, including (1) prescription of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker; (2) blood pressure at goal (<140/90 mm Hg) based on clinic measurements; (3) composite metric of hepatitis B virus immunity; (4) composite metric of referral, evaluation, or waitlist status for kidney transplantation; (5) advance directive documentation; (6) yearly influenza vaccination; and (7) pneumonia vaccination. ANALYTICAL APPROACH: Multivariable logistic regression analysis was used to analyze the association of KFRE score category with CKD care metrics. RESULTS: Of 61,546 patients, 18,272 (30%) had auto-calculated 2-year KFRE scores; the remaining patients lacked KFRE scores because of absent albuminuria assessment. Individuals with a KFRE score were more likely to have a primary care provider or nephrologist. Among patients with 2-year KFRE scores, high-risk patients had increased odds of completing advance directives (OR, 1.52; 95% CI, 1.07-2.17), while low-risk patients had decreased odds of influenza vaccination (OR, 0.85; 95% CI, 0.75-0.97). Patients with moderate- and high-risk KFRE scores had lower odds of having blood pressure at goal (OR, 0.77; 95% CI, 0.61-0.96 and OR, 0.63; 95% CI, 0.44-0.88, respectively). LIMITATIONS: Albuminuria data may have been assessed outside of the Partners system. CONCLUSIONS: A higher-risk KFRE score is associated with the delivery of some but not all CKD care measures. An opportunity exists to improve albuminuria measurement.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".