Adverse Outcomes Associated with Preventable Complications in Hospitalized Patients with CKD
Bibliographic record
Abstract
BACKGROUND: Patients with CKD are at risk of hospital-acquired complications (HACs). We sought to determine the association of preventable HACs with mortality, length of stay (LOS), and readmission. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: and/or albumin-to-creatinine ratio >3-30 mg/mmol for >3 months in the time frame from 365 to 90 days before admission. Regression models examined the association of HACs with outcomes. RESULTS: =45,733) had CKD and 9.8% of patients with CKD had one or more potentially preventable HAC. In patients with potentially preventable HACs, proportions of death within index hospitalization and from discharge to 90 days were 17.7% and 6.8%, respectively. In patients with CKD, comparing with those hospitalizations without potentially preventable HACs, the adjusted odds ratio (OR) of mortality during index hospitalization and from hospital discharge to 90 days in patients with one or more preventable HAC was 4.67 (95% confidence interval [95% CI], 4.17 to 5.22) and 1.08 (95% CI, 0.94 to 1.25), respectively. Median incremental LOS in patients with one or more preventable HAC was 9.86 days (95% CI, 9.25 to 10.48). The OR for readmission with preventable HAC was 1.24 (95% CI, 1.15 to 1.34). In a cohort with and without CKD, the adjusted ORs of mortality during index hospitalization in patients with CKD and no preventable HACs, patients without CKD and with preventable HACs, and patients with CKD and preventable HACs were 2.22 (95% CI, 1.69 to 2.94), 5.26 (95% CI, 4.98 to 5.55), and 9.56 (95% CI, 7.23 to 12.56), respectively (referenced to patients without CKD or preventable HACs). CONCLUSIONS: Preventable HACs are associated with higher mortality, incremental LOS, and greater risk of readmission, especially in people with CKD. Targeted strategies to reduce complications should be a high priority.
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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 | 0.005 |
| 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 | 0.000 |
| 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".