Intensity of End of Life Care for Hematologic Malignancy Patients in Western Washington, United States and Alberta, Canada
Bibliographic record
Abstract
Background: Aggressive care at the end of life (EOL) leads to unnecessary suffering and healthcare costs for patients with cancer. We have previously shown that among patients with solid tumor malignancies, EOL utilization of chemotherapy, intensive care unit (ICU) admissions and >1 emergency department (ED) visits are higher in Washington State vs Alberta (AB). In this study, we use cancer registry and claims data to compare EOL care among patients with hematological malignancies between western Washington (WW) and AB. Methods: Adult patients with hematological malignancies diagnosed between 2007 and 2017 who died before December 31, 2018 were identified from regional population-based cancer registries in WW and AB. Data sources were 1) WW Cancer Surveillance System (a regional SEER registry) with data from 13 counties linked to enrollment files and claims from four regional insurers and 2) Canada National Ambulatory Care Reporting System, Discharge Abstracts Database, and chemotherapy records from AB Health Services. Proportions of patients receiving chemotherapy, ICU admission, or >1 ED visit in the last 30 days of life (DOL) in WW and AB were determined among all patients and those ≥65 years and compared using two sample z-test with two-tailed hypothesis (α=0.006 after Bonferroni correction). Results: 7859 AB and 3767 WW patients met study inclusion criteria. Median age was 76 (IQR 66-83) and 79 (IQR 71-86) for AB and WW, respectively; 78% and 85% were over age 65, 33% and 59% with ≥2 Charlson Comorbidity Score. Cancer distribution was 33% (AB) and 54% (WW) non-Hodgkin lymphoma, 14% (AB) and 20% (WW) myeloma and 27% (AB) and 19% (AB) leukemia. Table 1 shows utilization of chemotherapy, >1 ED visits and ICU admissions in AB and WW for all patients and Table 2 in those ≥65 years. More patients in WW vs AB were treated with chemotherapy (21% WW vs 7% AB) and admitted to ICU (34% WW vs 9% AB) in the last 30 DOL, whereas multiple ED visits were more similar between WW and AB (17% vs 19%, respectively). Similarly, among patients ≥65 years, chemotherapy use and ICU admissions were higher in WW. The same was true for patients in the last 60 and 90 DOL. Conclusions: Similar to what was noted in solid tumor patients, intensity of healthcare use at EOL is greater in WW vs AB for patients with hematological malignancies. However, ≥1 ED visits were similar between populations. Further work is needed to understand drivers of high intensity healthcare use and identify interventions to minimize low value care at EOL. Disclosures Khaki: Merck: Other: share/stockholder; Pfizer: Other: share/stockholder. Ramsey:AstraZeneca: Other: Personal Fees. Cowan:Janssen: Consultancy, Research Funding; Abbvie: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy; Bristol Myers Squibb: Research Funding.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".