Factors Associated with Unplanned Dialysis Starts in Patients followed by Nephrologists: A Retropective Cohort Study
Bibliographic record
Abstract
The number of patients starting dialysis is increasing world wide. Unplanned dialysis starts (patients urgently starting dialysis in hospital) is associated with increased costs and high morbidity and mortality. Risk factors for starting dialysis urgently in hospital have not been well studied. The primary objective of this study was to identify risk factors for unplanned dialysis starts in patients followed in a multidisciplinary chronic kidney disease (CKD) clinic. We performed a retrospective cohort study of 649 advanced CKD patients followed in a multidisciplinary CKD clinic at a tertiary care hospital from January 01, 2010 to April 30, 2013. Patients were classified as unplanned start (in hospital) or elective start. Multivariable logistic regression was used to identify variables associated with unplanned dialysis initiation. 184 patients (28.4%) initiated dialysis, of which 76 patients (41.3%) initiated dialysis in an unplanned fashion and 108 (58.7%) starting electively. Unplanned start patients were more likely to have diabetes (68.4% versus 51.9%; p = 0.04), CAD (42.1% versus 24.1%; p = 0.02), congestive heart failure (36.8% versus 17.6%; p = 0.01), and were less likely to receive modality education (64.5% vs 89.8%; p < 0.01) or be assessed by a surgeon for access creation (40.8% vesrus 78.7% p < 0.01). On multivariable analysis, higher body mass index (OR 1.07, 95% CI 1.02, 1.13), and a history of congestive heart failure (OR 2.41, 95% CI 1.09, 5.41) were independently associated with an unplanned start. Unplanned dialysis initiation is common among advanced CKD patients, even if they are followed in a multidisciplinary chronic kidney disease clinic. Timely education and access creation in patients at risk may lead to lower costs and less morbidity and mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".