Abstract 162: Burden And Impact Of Chronic Kidney Disease On Admissions Of Cancer Survivors And Assessing The Odds Of Major Adverse Cardiac And Cerebrovascular Events
Bibliographic record
Abstract
Background: Cancer-specific adverse events are not well defined despite chronic kidney disease (CKD) being associated with high mortality rates in cancer patients. This study explores the prevalence of major adverse cardiac and cerebrovascular events (MACCE) in cancer survivors with CKD from a national cohort. Methods: We identified cancer survivors with and without concomitant CKD along with their age, sex, race and other comorbidities who were admitted to hospitals in a National Inpatient Sample database (2018). We then identified odds of MACCE including all-cause mortality, AMI, cardiac arrest (including ventricular fibrillation) and stroke while subsequently analyzing healthcare resource utilization in patients with versus without CKD. Multivariable regression analyses were adjusted for patient and hospital level covariates and pre-existing comorbidities. A p-value <0.05 was considered statistically significant. Results: CKD prevalence was higher among patients with a prior history of cancer versus those without it (21.5% vs 14.6%, p<0.001) in the cohort. Higher rates of traditional cardiovascular disease risk factors, prior history of MI, stroke/TIA, VTE, CHF, coagulopathy and MACCE (11.5% vs 8.1%, OR 1.22 [CI 1.20 -1.25]) were observed in patients with versus without CKD [Table 1] in hospitalized cancer survivors. Said survivors with CKD were specifically noted to have higher rates of all-cause mortality (3.4% vs 2.2%, OR 1.33 [CI 1.29 -1.37]) and AMI (6.0% vs 3.3%, OR 1.54 [CI 1.50 - 1.58]) (all p<0.001). The CKD cohort had fewer routine discharges, more frequent transfers to other facilities, higher length of stay and hospital costs versus the non-CKD cohort (p<0.001). Conclusion: This large retrospective analysis shows elevated burden of CKD (21.5%) amongst hospitalized cancer survivors, which is associated with not only increased rates of MACCE, all-cause mortality, AMI, cardiac arrest but also greater overall healthcare costs.
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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 | 0.003 |
| 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) | 0.003 | 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".