Potentially avoidable hospitalizations in patients with advanced cancer.
Bibliographic record
Abstract
e18275 Background: Cancer patients and their clinicians often wish to avoid preventable hospital admissions, but efforts to understand the predictors of avoidable hospitalizations are lacking. We sought to examine reasons for hospital admissions in patients with advanced cancer, identify potentially avoidable hospitalizations (PAH), and explore predictors of PAH. Methods: We prospectively enrolled hospitalized patients with advanced cancer from 9/2014 - 11/2014 as part of a longitudinal data repository to define symptom burden in this population. Upon admission, we assessed patients’ symptom burden (Edmonton Symptom Assessment System [ESAS]; scored 0-10). We created a summated ESAS physical symptom variable. We used consensus-driven medical record review to identify the primary reason for each hospital admission and categorize it as PAH or not based on of an adaptation of Graham’s criteria for PAH. We used mixed multivariable logistic regression analyses to identify predictors of PAH. Results: We assessed 477 hospital admissions in 200 consecutively admitted patients (mean age = 64.6; 47% female; 67% married). Over half of admissions came through the emergency department (56%). The most common reasons for admissions were fever/infection (30%), symptoms (26%), and planned admission for chemotherapy or procedure (10%). We identified 149 (31%) as PAH. Among these PAH, 45 (30%) were readmissions due to failure of timely outpatient follow-up (within 7 days of discharge) and 44 (30%) were due to premature discharge from prior hospitalization. In a mixed logistic regression model, being married (odds ratio [OR] 0.48 [0.28-0.81]; p < 0.01) was associated with lower likelihood of PAH, while higher physical symptom burden (OR 1.02 [1.00-1.04]; p = 0.04) was associated with greater likelihood of PAH. Conclusions: We identified that a substantial proportion of hospitalizations in patients with advanced cancer are potentially avoidable, often related to failure of timely outpatient follow-up and premature hospital discharge. Our results demonstrate that patients’ symptom burden predicts PAH, thus underscoring the need to address patients’ symptoms in order to reduce preventable hospital admissions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".