Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer
Bibliographic record
Abstract
PURPOSE: Patients with cancer often prefer to avoid time in the hospital; however, data are lacking on the prevalence and predictors of potentially avoidable readmissions (PARs) among those with advanced cancer. METHODS: We enrolled patients with advanced cancer from September 2, 2014, to November 21, 2014, who had an unplanned hospitalization and assessed their patient-reported symptom burden (Edmonton Symptom Assessment System) at the time of admission. For 1 year after enrollment, we reviewed patients’ health records to determine the primary reason for every hospital readmission and we classified readmissions as PARs using adapted Graham’s criteria. We examined predictors of PARs using nonlinear mixed-effects models with binomial distribution. RESULTS: We enrolled 200 (86.2%) of 232 patients who were approached. For these 200 patients, we reviewed 277 total hospital readmissions and identified 108 (39.0%) of these as PARs. The most common reasons for PARs were premature discharge from a prior hospitalization (30.6%) and failure of timely follow-up (28.7%). PAR hospitalizations were more likely than non-PAR hospitalizations to experience symptoms as the primary reason for admission (28.7% v 13.0%; P = .001). We found that married patients were less likely to experience PARs (odds ratio, 0.30; 95% CI, 0.15 to 0.57; P < .001) and that those with a higher physical symptom burden were more likely to experience PARs (odds ratio, 1.03; 95% CI, 1.01 to 1.05; P = .012). CONCLUSION: We observed that a substantial proportion of hospital readmissions are potentially avoidable and found that patients’ symptom burdens predict PARs. These findings underscore the need to assess and address the symptom burden of hospitalized patients with advanced cancer in this highly symptomatic population.
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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 | 0.001 |
| 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".