Factors Impacting Quality of Life in Thalassemia Patients; Results from the Intercontinenthal Collaborative Study
Bibliographic record
Abstract
Abstract Thalassemia is one of the most common causes of inherited anemia worldwide. While significant advances has been made in clinical management of thalassemia patients over the past few decades, our knowledge on the factors affecting the quality of life of thalassemia patients is limited. The "IntercontinThal Study" is a collaborative effort to study the quality of life (QoL) and quality of care of thalassemia patients in populations across diverse social and health care systems. Data presented here are from the three participating centers in Canada, Lebanon and Iran. We have gathered study data through: a) QoL questionnaire SF-36 completed by patients, b) a specifically designed and validated questionnaire completed by patients which addressed patient's social status (marriage/relationship status, education, employment status, and access to social support and health care), and c) review of the patients' charts using a data collection form. This form included: patients' demographics, specifics of transfusion therapy and iron chelation, thalassemia-related and other clinical complications (endocrinopathies, bone disease, cardiac disease, hemolysis-related complications, etc.), tissue iron content (liver and cardiac) and/or serum ferritin within the past three years, and splenectomy status. All study questionnaires were translated into Persian (for Iranian patients) and Arabic (for Lebanese patients). Due to the variety of the clinical complications, all clinical complications were aggregated together for statistical analysis. We used univariate and multivariate regression analysis to study the association of predictors and patients' QoL Mental Component Summary (MCS) Score. Ninety seven patients [46 female, 59 transfusion-dependent beta-thalassemia (TDT) and 38 non-transfusion-dependent beta-thalassemia (NTDT)] were included in the analysis. All patients were older than 18 years of age (Mean 32 years, SD: 7 years). In univariate analysis age, access to social support and health care, marriage status, liver iron concentration and ferritin (strongly correlated with each other), and disease-related complications were found to be predictor of QoL MCS scores. In NTDT patients, splenectomy and lower baseline hemoglobin were also significantly associated with reduced QoL. In multivariate analysis, ferritin and age (and clinical complications in TDT patients) were found to independently be associated with reduced QoL. LIC was not found to be an independent factor likely due to the fewer number of patients who had recent LIC assessments. Of interest, patients with NTDT reported better QoL at younger age compared to TDT patients but there was a trend toward worse QoL at older age. Our results provide a better understanding of the factors that affect the QoL of thalassemia patients and highlights the importance of management of body iron in both TDT and specially in NTDT patients. In addition, it confirms the notion that while NTDT patients may not require regular transfusions based on conventional criteria, they may experience significant reduction in QoL especially at older ages. Further efforts to address the health and QoL of NTDT patients are required to improve the outcomes of this often neglected condition. (Funded by a research grant from the Thalassemia Foundation of Canada) Disclosures Taher: Celgene: Research Funding; Novartis: Honoraria, 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".