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Epidemiologic and Clinical Characteristics of Thalassemia (Thal) Intermedia (TI) in the United States

2015· article· en· W2361747243 on OpenAlexaff
Elliott Vichinsky, Janet L. Kwiatkowski, Patricia J. Giardina, Carole Paley, Francis Vekeman, Wendy Y. Cheng, Joseph Damron, Nora McCormick, Medha Sasané, Ying Qiu, Mei Sheng Duh, Alexis A. Thompson

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineThalassemiaInternal medicinePediatricsGastroenterology

Abstract

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Abstract Introduction TI is increasingly prevalent in the US due to changing immigration patterns. It is underdiagnosed, leading to inadequate or delayed management. This study reviewed the prevalence, epidemiology, and clinical characteristics of TI in patients (pts) in the US. Methods Medical records from 1/1997- 4/2014 at 4 US hematology centers were reviewed. Index date was the 1st TI visit at a center on or after 1/1/1997. Eligible pts had a TI diagnosis (≤8 mean packed red blood cell transfusions per year (yr) over a ≥3-y period after index date) and ≥12 months of follow-up. Data spanned from index date to death or last record. Descriptive analyses of demographic and clinical data were done by TI subtype. Results Of 138 pts enrolled, 84 had α-thal, 39 had β-thal, and 15 had E/β-thal. 74% of α-thal pts had deletional (del) Hb H, and 26% had non-deletional (ndel) Hb H. 59% of β-thal pts had homozygous or compound heterozygous β-globin mutations (8% with α deletion, 51% without), 20% had a single β mutation with α-gene triplication, 13% had autosomal dominant β thal, and 8% had other β-globin mutations. Of the E/β-thal pts, 80% had E/β0 and 20% had E/β+. Median age at index date was 2.3 yr (1.64 del; 6.1 ndel) in the α-thal group, 9.2 yr in the β-thal group, and 2.2 yr in the E/β-thal group. Most α-thal (77%) and E/β-thal (87%) pts were Asian; most β-thal pts were White (46%) or African-American (36%). Most α-thal (56%) and E/β-thal (53%) pts were of Southeast Asian origin; most β-thal pts were of Mediterranean (31%) or African (21%) origin. 21%, 10%, and 20% of α, β, and E/β-thal pts, respectively, were foreign-born, and 5%, 3% and 7%, respectively, were transfused outside of the US. Observation length was similar across subtypes (median: 5.3 yr). Clinical comorbidities are shown in Table 1. 22% of pts received ≥1 transfusion, while 7% of pts received ≥8 transfusions in any 1 yr to treat anemia, acute hemolysis, or cardiac abnormality; increased transfusions were initiated due to growth failure, acute hemolysis, or unspecified reasons. β-thal pts had a higher mean number of transfusions per pt per yr (PPPY) (α: 0.4 (0.0 del; 1.5 ndel), β: 0.9, E/β: 0.2) and higher mean serum ferritin (ng/mL) (204.3; 511.7; 362.4), and more often had iron chelation therapy (ICT) (11%; 28%; 7%). There was an association between higher serum ferritin, more frequent transfusions, and older age. In pts <10 yr, mean number of transfusions PPPY for regularly and ever transfused was 4.0 and 2.1, and serum ferritin for regularly, ever, and never transfused was 723.7, 438.0, and 146.4 ng/mL. In pts >18 yr, these values were 9.8 and 4.6 transfusions PPPY and 1166.8, 1042.2, and 521.4 ng/mL. An association also existed between ICT and higher mean serum ferritin (ICT: 769.9; no ICT: 463.7 ng/mL). 22% of pts had ≥1 liver iron test, and 18% had ≥1 cardiac iron test. Tested pts were older than those not tested (median yr, liver iron: 26.1 vs 7.4; cardiac: 20.1 vs 4.6). Among tested pts, 78% (25/32) had abnormal liver iron results. Median (range) LIC based on R2 or SQUID was 10.8 (2.5-18.2) mg/g dw, with corresponding within-12-month serum ferritin of 494 (127-1770) ng/mL. 49% (17/35) of pts tested had abnormal cardiac results based on electrocardiogram or echocardiogram. Table 1. Clinical comorbidities All α-thal α-thal del α-thal ndel β-thal E/β-thal N=138 N=84 N=62 N=22 N=39 N=15 Splenomegaly 54% 55% 39% 100% 51% 60% Extramedullary hematopoiesis 28% 24% 13% 55% 36% 33% Growth retardation 21% 23% 18% 36% 13% 33% Hepatomegaly 21% 24% 13% 55% 15% 20% Infections needing hospitalization/IV antibiotics 21% 25% 19% 41% 15% 13% Hypoparathyroidism/hypocalcemia 15% 17% 8% 41% 10% 20% Osteopenia/osteoporosis 13% 11% 5% 27% 21% 7% Bone deformities 9% 6% 5% 9% 21% 0% Splenectomy and/or cholecystectomy 9% 5% 2% 14% 21% 0% Cardiomegaly 4% 6% 2% 18% 0% 7% Conclusion TI in the US affects a diverse population. Our data showed a higher prevalence in African-Americans than previously documented. Rates of comorbidity and transfusion frequency increased with age. 18% and 4% of pts were born and transfused outside of the US, potentially leading to additional transfusion-related morbidity. Consistent with extant data, serum ferritin in TI often underestimated actual LIC, rendering more pts potentially eligible for ICT than observed. Morbidities observed in this study underscore the need for better and earlier diagnosis, substantiating the need for nationwide TI screening/surveillance to optimize management. Disclosures Vichinsky: Novartis: Research Funding. Kwiatkowski:Novartis: Research Funding; Sideris Pharmaceuticals: Consultancy; Shire Pharmaceuticals and Sideris Pharmaceuticals: Consultancy; ISIS: Membership on an entity's Board of Directors or advisory committees. Paley:Novartis: Employment. Vekeman:Novartis: Research Funding. Cheng:Novartis: Research Funding. Damron:Novartis: Research Funding. McCormick:Novartis: Research Funding. Sasane:Novartis: Employment. Qiu:Novartis: Employment. Duh:Novartis: Research Funding. Thompson:Bluebird bio: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Mast: Research Funding; Apopharma: Consultancy; Baxter: Consultancy, Research Funding.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.046
GPT teacher head0.328
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2015
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