Comparing the characteristics and outcomes of hospitalizations between cancer and non-cancer survivors.
Bibliographic record
Abstract
12138 Background: Cancer prevalence is rising, with a corresponding increase in hospitalizations across the cancer continuum. However, little is known about how in-hospital patterns of care and outcomes of cancer survivors compare with non-cancer survivors as administrative data may not capture in-hospital details (e.g., investigations and medications) required for characterization. Understanding differences in how cancer and non-cancer inpatients are managed and their outcomes can help optimize their acute care delivery. Methods: In a multicenter registry of all patients (pts) admitted to medical wards across 26 hospitals (Ontario, Canada) from 2015-2022, we deterministically linked population-level administrative data, including ambulatory oncology data for cancer survivors, with each hospital’s electronic information (pharmacy, orders, notes, laboratory, imaging) at the patient level. Multivariable regression models compared resource use and outcomes between cancer and non-cancer pts for the top 5 discharge diagnoses among non-cancer pts. Results: Of 1,221,067 hospitalizations belonging to 666,569 pts, 30% of medical ward hospitalizations were for pts with a cancer history, with median admission date 4 years post-diagnosis; most common cancer sites were genitourinary (21%), gastrointestinal (20%), breast (12%), lung (10%). Most common discharge diagnoses among cancer pts were heart failure (HF) (5%), palliative care (5%), urinary tract infection (UTI) (2%), pneumonia (2%) renal failure (2%); while for non-cancer pts were HF (5%), myocardial infarction (3%), coronary artery disease (3%), COPD (2%) and UTI (2%). Compared to non-cancer pts, cancer pts were older (72 vs 66), had greater length of stay (LOS; 10 vs 8.7 days), in-hospital mortality (11% vs 6%) and 30 day re-admission rates (16% vs 11%) and were more likely to receive CTs (21% vs 15%), MRIs (9% vs 8%) and interventional procedures (6% vs 4%) (p < 0.001, all comparisons). When evaluating the top 5 discharge diagnoses among non-cancer patients, cancer survivors had higher LOS (aOR=1.06 95% [1.05-1.07] p<0.001), in-hospital mortality (aOR=1.20 [1.14-1.26] p<0.001), and 30 day re-admission rates (aOR=1.24 [1.14-1.35] p<0.001) and were more likely to receive CTs (aOR=1.25 [1.21-1.30] p<0.001), MRIs (aOR=1.36 [1.25-1.48] p<0.001) and interventional procedures (aOR=1.36 [1.25-1.47] p<0.001). Subgroup analyses focusing on cancer survivors admitted 3 and 5 years out from their diagnosis showed resource use and outcomes were closer to non-cancer patients. Conclusions: Cancer survivors represent a unique population on medical wards and have higher resource use, mortality and LOS compared to non-cancer patients, even for the same non-cancer diagnoses. Specialized models of care for hospitalized cancer survivors may be warranted, in particular for those admitted closer to their diagnosis date.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".