Death during hospitalization in patients on chronic hemodialysis
Bibliographic record
Abstract
Mortality from various causes is higher in patients on chronic hemodialysis (HD) than in the general population. There is evidence suggesting that some of the deaths in HD patients are preventable. To identify potentially preventable causes of death, we analyzed deaths that occurred in HD patients during hospitalization over a period of 15 years. We performed a retrospective cohort analysis of 410 patients on HD for at least 6 months between 1995 and 2009 (included), who had all their hospitalizations in the same hospital. The patients were classified into 3 groups: Those who died during hospitalization (group A, n=120), those who died away from the hospital (group B, n=135), and those who were alive at the end of the observation period (group C, n=155). Continuous variables were compared between groups by the Kruskall-Wallis statistic. Logistic regression was used to identify predictors of dying during the observation period and predictors of death in the hospital. For the whole HD group of 410 patients, only 9 (2.2%) were women. 59% of the patients had diabetes mellitus. Age at the onset of HD was 65.8 ± 11.5 years and the duration of HD was 34.4 ± 27.9 months. Group A patients had a higher annual rate and duration of hospitalization and a higher Charlson comorbidity index than either of the other 2 groups, and, in comparison with patients in group C, were older at the end of observation and had a shorter duration of HD. Cardiac disease (19.2%), vascular access complications (18.3%), peripheral vascular disease (16.7%), infections (15.8%), trauma (11.7%), central nervous system disease (7.5%), respiratory failure (4.2%), malignancy (3.3%), and gastrointestinal disease (3.3%) were the causes of the last hospitalization in group A. Compared with the patients who died during hospitalization without discontinuing HD, group A patients who discontinued HD had a longer duration of their last hospitalization (52.7 ± 77.7 vs. 14.3 ± 23.8 days, P<0.001). Discontinuation of HD occurred in 80% of the hospitalizations for respiratory failure, 75% of the hospitalizations for malignancy, 57% of the hospitalizations for trauma, and 56% of the hospitalizations for central nervous system disease. Logistic regression identified a high Charlson index, advanced age, and short duration of HD as predictors of death, and an absence of diabetes, high Charlson index, prolonged annual duration of hospitalization, and short distance of the patient's domicile from the dialysis unit as predictors of death in the hospital. A substantial number of hospitalizations leading to the death of HD patients are caused by potentially preventable conditions, including vascular access complications, peripheral vascular disease, and trauma. Implementation of measures preventing these hospitalizations is a worthwhile undertaking.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".