Risk of Hospital-Acquired Complications in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Unintended injuries or complications in hospitalized patients are common, potentially preventable, and associated with adverse consequences, including greater mortality and health care costs. Patients with CKD may be at higher risk of hospital-acquired complications (HACs). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Adults from a population-based cohort (Alberta Kidney Disease Network) who were hospitalized from April 1, 2003, to March 31, 2008, made up the study cohort. Kidney function was defined using outpatient eGFR and proteinuria (protein-to-creatinine ratio or dipstick) in the year before index hospitalization. Comorbid conditions were identified using validated algorithms applied to administrative data. A specific diagnostic indicator was used to identify HACs. Complications were classified into clinically homogeneous groups and subclassified as potentially preventable (p-HACs) or always preventable (a-HACs). Multivariable logistic regressions models were used to examine the association of CKD with HACs, accounting for confounders. RESULTS: Of 536,549 patients, 8.5% had CKD; those with CKD were older and more likely to be admitted for circulatory system diseases than those without CKD. In fully adjusted models, the odds ratio (OR) of any hospital complication in patients with CKD (reference: no CKD) was 1.19 (95% confidence interval [95% CI], 1.18 to 1.26); there was a graded relation between the risk of HACs and CKD severity, with an OR of 1.81 (95% CI, 1.51 to 2.17) in those with the most severe CKD (eGFR, 15-29 ml/min per 1.73 m(2) and proteinuria, >30 mg/mmol). Findings were similar for p-HACs (OR, 1.20 [95% CI, 1.16 to 1.24] and 1.78 [95% CI, 1.43 to 2.11], respectively). The a-HACs had similar point estimates. CONCLUSIONS: The presence of CKD and its severity are associated with a higher risk of HACs, including those considered preventable. Targeted strategies to reduce complications in patients with CKD admitted to the hospital should be considered.
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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 | 0.001 | 0.000 |
| 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".