P1008: ASSOCIATION BETWEEN FRAILTY AND CLINICAL OUTCOMES IN MYELOPROLIFERATIVE NEOPLAMS: A POPULATION-BASED STUDY FROM ONTARIO, CANADA
Bibliographic record
Abstract
Background: Frailty independently predicts adverse outcomes in several different cancers and community-dwelling older adults. Its impact on clinical outcomes in myeloproliferative neoplasms (MPN) is unknown. Aims: To measure the association between frailty and all-cause mortality and thrombosis in MPN patients. Methods: Method: A retrospective, population-based study using province-wide administrative databases of Ontario. Study population: We included MPN patients in the Ontario Cancer Registry from 2004 to 2019 ((Total n= 10,336: ET, n=5,108; PV, n=3,843; MF, n=1,385). Baseline frailty was measured during two years prior to date of MPN diagnosis using either (i) the Johns Hopkins Adjusted Clinical Groups® frailty indicator (ACG-F), categorized as fit or frail if any of the ten frailty-defining diagnoses were present or (ii) the McIsaac’s cumulative deficit frailty index (mFI), categorized as fit, prefrail, or frail if mFI <0.10, 0.10-0.19, > 0.19 respectively. Main outcome measures: Cox proportional hazard model was used to generate the Hazard Ratios (HRs) with 95% confidence interval (CI) for all-cause mortality comparing frail vs. prefrail vs. fit patients. Subdistribution hazard ratios (SHR) using the Fine-Gray models were used to evaluate the effect of frailty on development of thrombosis, taking death as the competing event. Analyses were adjusted for age, comorbidities, prior thrombosis, and level of marginalization. Results: Results: The mean duration of follow-up for ET, PV and MF was 3.8, 4.0 and 2.9 years, respectively. 16% MPN cases were categorized as mFI-frail and 51% as mFI-prefrail. Patient with MF were more likely to be mFI-frail or mFI-prefrail compared to ET and PV (34%, 23%, and 20% respectively, p<0.001). In all MPN subtypes, frailty was independently associated with increased risk of mortality after adjusting for age, sex, and comorbidities. Compared to fit patients, the HRs for all-cause mortality for prefrail and frail patients were: 1.6 (1.3-1.9), and 3.6 (2.9-4.4) in ET; 1.3 (1.1-1.5) and 2.7 (2.1-3.4) in PV, and 1.2 (1.0-1.5) and 2.0 (1.5-2.7) in MF. Other predictors associated with increased all-cause mortality were advanced age (compared to age <40 years, HRs for 40-65 years, 65-75 years, >75 years: ET: 3.3 (2.0-5.5), 5.34 (3.2-8.7), 12.9 (7.9-21.2); for PV: 3.2 (1.6-6.7), 7.36 (3.6-15.0), 17.3 (8.5-35.2), for MF: 1.8 (0.8-3.8), 3.0 (1.4-6.5), 4.8 (2.2-10.4)) and comorbidities (1 and ≥ 2 comorbidities vs no comorbidities: ET: 1.3 (1.1-1.5), 2.4 (2.0-2.8); for PV: 1.0 (0.8-1.2), 1.2 (1.0-1.5); for MF: 1.2 (1.0-1.5), 1.4 (1.1-1.8)). With ACG-F, frailty was noted in 11% in MPN cases and was associated with increased mortality after adjusting for comorbidities and advanced age in all MFN subtypes [HRs in ET 2.5 (2.2-2.8), PV: 2.1 (1.8-2.4), and MF: 1.8 (1.5-2.2)]. In multivariable Fine-Gray regression, neither mFI-prefrail nor mFI-frail patients had an increased risk of thrombosis in all MPN subtypes. Compared to fit patients, frail patients using ACG-F had lower SHRs of thrombosis (ET: 0.6 (0.4-0.8), PV: 0.6 (0.4-0.8), MF: 0.2 (0.05-0.8), but higher SHRs for the competing event of death (ET: 3.0 (2.6-3.5), PV: 2.3 (1.9-2.9), MF: 1.9 (1.5-2.4)). Image:Summary/Conclusion: Conclusions: Using large population-based databases, we found that a significant proportion of MPN patients are frail or prefrail at diagnosis despite younger age or less comorbidity burden. After adjusting for confounding from advanced age and increasing co-morbidity burden, frailty independently predicts increased risk of all-cause mortality in ET, PV, and MF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".