Kidney Failure Risk Equation and Cost of Care in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
Background and objectives Patients with CKD exhibit heterogeneity in their rates of progression to kidney failure. The kidney failure risk equation (KFRE) has been shown to accurately estimate progression to kidney failure in adults with CKD. Our objective was to determine health care utilization patterns of patients on the basis of their risk of progression. Design, setting, participants, & measurements We conducted a retrospective cohort study of adults with CKD and eGFR of 15–59 ml/min per 1.73 m 2 enrolled in multidisciplinary CKD clinics in the province of Saskatchewan, Canada. Data were collected from January 1, 2004 to December 31, 2012 and followed for 5 years (December 31, 2017). We stratified patients by eGFR and risk of progression and compared the number and cost of hospital admissions, physician visits, and prescription drugs. Results In total, 1003 adults were included in the study. Within the eGFR of 15–29 ml/min per 1.73 m 2 group, the costs of hospital admissions, physician visits, and drug dispensations over the 5-year study period comparing high-risk patients with low-risk patients were (Canadian dollars) $89,265 versus $48,374 ( P =0.008), $23,423 versus $11,231 ( P <0.001), and $21,853 versus $16,757 ( P =0.01), respectively. Within the eGFR of 30–59 ml/min per 1.73 m 2 group, the costs of hospital admissions, physician visits, and prescription drugs were $55,944 versus $36,740 ( P =0.10), $13,414 versus $10,370 ( P =0.08), and $20,394 versus $14,902 ( P =0.02) in high-risk patients in comparison with low-risk patients, respectively, for progression to kidney failure. Conclusions In patients with CKD and eGFR of 15–59 ml/min per 1.73 m 2 followed in multidisciplinary clinics, the costs of hospital admissions, physician visits, and drugs were higher for patients at higher risk of progression to kidney failure by the KFRE compared with patients in the low-risk category. The high-risk group of patients with CKD and eGFR of 15–29 ml/min per 1.73 m 2 had stronger association with hospitalizations costs, physician visits, and drug utilizations.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".