Mortality associated with warm autoimmune hemolytic anemia among Medicare beneficiaries
Bibliographic record
Abstract
Abstract INTRODUCTION: Warm autoimmune hemolytic anemia (wAIHA) is a rare, autoimmune-mediated hematologic disorder associated with significant morbidity and mortality and may occur as a primary condition or secondary to an underlying disorder. It is the most common subtype of autoimmune hemolytic anemia (AIHA), accounting for 75-80% of AIHA cases. Older adults with wAIHA are particularly vulnerable, as they often have multiple comorbidities and are already at elevated risk of death. This study aimed to assess mortality associated with wAIHA. METHODS: Adults with wAIHA (wAIHA cohort) and without AIHA (non-AIHA cohort) were identified from the 100% Medicare Fee-for-Service database (10/01/2019–12/31/2023). Evidence of wAIHA was defined as one of the following: 1) a primary wAIHA diagnosis (International Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM]: D59.11) in the inpatient setting (first instance of wAIHA diagnosis defined the index date), 2) ≥2 wAIHA diagnosis in any setting ≥30 days apart (second instance of wAIHA diagnosis defined the index date), or 3) a wAIHA diagnosis preceded ≥30 days by any AIHA diagnosis (ICD-10-CM: D59.10, D59.19, D59.8, D59.9; first instance of wAIHA diagnosis defined the index date). Patients in the wAIHA cohort were excluded if they had a diagnosis for cold or mixed-type AIHA (ICD-10-CM: D59.12, D59.13) during the baseline period (12 months before index). The non-AIHA cohort had no diagnoses for any AIHA during the study period; a random index date during continuous Medicare eligibility was selected and patients were matched 1:10 with the wAIHA cohort based on age on index date. Using overlap weighting, age-matched cohorts were further weighted on sex, race, region, index year, and baseline comorbidities associated with mortality, excluding conditions defining secondary wAIHA. Mortality during the follow-up period was evaluated between the weighted cohorts using the Kaplan-Meier method and Cox proportional hazard models. Patients whose death was not observed were censored at the earlier of the end of continuous Medicare eligibility or data availability. RESULTS: 3,112 and 31,120 patients were included in the wAIHA and non-AIHA cohorts, respectively. After weighting, among the wAIHA vs non-AIHA cohorts, mean [standard deviation; SD] age was 74.4 [11.6] vs 74.6 [11.2] years, the proportion of female were 56.1% vs 56.1%, mean [SD] Quan-CCI without secondary wAIHA-defining conditions was 1.9 [2.0] vs 1.9 [2.1], and mean [SD] follow-up time was 17.2 [11.7] vs 14.9 [11.4] months, respectively. In addition, 53.7% of the wAIHA cohort had secondary wAIHA. The most common secondary wAIHA-defining conditions were autoimmune and inflammatory diseases (wAIHA cohort: 27.8%; non-AIHA cohort: 11.0%), hematologic and lymphoproliferative disorders (wAIHA cohort: 23.8%; non-AIHA cohort: 1.3%), and solid tumors (wAIHA cohort: 16.8%; non-AIHA cohort: 12.6%). The most common comorbidities for both cohorts were hypertension (wAIHA cohort: 77.4%; non-AIHA cohort: 78.3%) and dyslipidemia (wAIHA cohort: 72.2%; non-AIHA cohort: 74.7%). Survival rates in the wAIHA and non-AIHA cohorts were 80.3% vs 84.0% at 12 months, 70.7% vs 77.6% at 24 months, and 63.7% vs 73.1% at 36 months, respectively (all p<0.001). Patients with wAIHA were at 20%, 27%, and 30% higher risk of death at 12, 24, and 36 months, respectively, compared to patients without AIHA (hazard ratios: 1.20, 1.27, and 1.30; all p<0.001). CONCLUSIONS: This large retrospective study is the first to assess real-world incremental mortality associated with wAIHA among Medicare beneficiaries. Patients with wAIHA were at significantly greater risk of death following the initial diagnosis. These results may point to potential gaps in early diagnosis and effective disease management, which could contribute to increased mortality. Future research should focus on specific subgroups within the wAIHA cohort to identify differences in mortality as well as characteristics that may be associated with death.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".