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Final Results From the Multicenter Compact Study of Complications in Patients with Sickle Cell Disease and Utilization of Iron Chelation Therapy: A Retrospective Medical Records Review.

2012· article· en· W2578330893 on OpenAlexaff
Lanetta Jordan, Patricia Adams‐Graves, Julie Kanter-Washko, Patricia O’Neal, Francis Vekeman, Christine Bieri, Medha Sasané, Andrea Marcellari, Matthew Magestro, Zachary Gorn, Mei Sheng Duh

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineCohortMedical recordRetrospective cohort studyCohort studyComplicationBlood transfusionAnemiaPediatricsDiseaseInternal medicine

Abstract

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Abstract Abstract 2106 Introduction: Over the past few decades, lifespans of sickle cell disease (SCD) patients have increased; hence, they encounter multiple complications. Early detection, appropriate comprehensive care, and treatment may prevent or delay onset of complications. There is a gap in the literature describing the SCD complication rates, blood transfusion patterns, iron chelation therapy (ICT) use, and associated resource utilization in SCD patients ≥16 years old. This study contributes to addressing this gap. Method: Medical records of 254 SCD patients ≥ 16 were retrospectively reviewed between August 2011 and July 2012 at three US tertiary care centers (University of Tennessee: 117; Tulane University: 72; Howard University: 65). Data were collected from patient's first visit after age 16 (index date) until the earliest indication of death, loss to follow-up, or last patient record on file prior to the centers' IRB submission dates. Patients were classified into one of three cohorts based on cumulative units of blood transfused and history of ICT: <15 units of blood and no ICT (minimally transfused, Cohort 1 [C1]), ≥15 units of blood and no ICT (Cohort 2 [C2]), and ≥15 units of blood and receiving ICT (Cohort 3 [C3]). SCD complication rates were expressed as the number of SCD complications recorded from patient charts per patient per year (PPPY) and compared among cohorts using rate ratios (RRs). Results: Cohorts 1, 2, and 3 consisted of 69, 91, and 94 patients, respectively. Mean (range) age at index date was similar across cohorts (27 yrs [16–65]) and all patients were African American. Mean length of observation was shorter among patients in C1 (yrs, C1: 6.6; C2: 8.2; C3: 8.1). Post index date, patients in C1 received an average of 1 unit of blood PPPY (p<0.001 vs. C2 and C3), whereas patients in C2 and C3 received an average of 10 and 15 units PPPY (p=0.112), respectively. Among patients with serum ferritin (SF) assessment within 60 days before ICT (n=57), mean (median) SF level was 4,881 ng/mL (4,040). Across all three cohorts, the most common SCD complication was acute pain crisis (69.8%), followed by infection/sepsis (5.1%), leg ulcers (2.9%), and avascular necrosis (2.3%). The rate (95% CI) of any SCD complications was the highest in C2 at 3.02 PPPY (2.89–3.14), followed by 2.26 PPPY (2.16–2.37) in C3, and 1.66 PPPY (1.54–1.77) in C1 (Table 1). Among transfused patients (C2+C3), those receiving ICT were less likely to experience SCD complications than those who did not (RR [95% CI] C2 vs. C3: 1.33 [1.25–1.42]). Similar trends (RR [95% CI]) were observed in emergency room (ER) visits and hospitalizations associated with SCD complications (C2 vs. C3, ER: 1.94 [1.70–2.21]; hospitalizations: 1.61 [1.45–1.78]), but not in outpatient visits. Conclusion: Results from this study highlight the significant burden of complications and the associated healthcare resource utilization for SCD patients. The results suggest that among regularly transfused patients, those who received ICT were less likely to experience complications than those without ICT. However, transfusions are not necessary for all patients with SCD and patients with more complications may have started transfusion therapy earlier. Patients receiving ICT may also receive closer monitoring, which may help with early identification and intervention to delay or prevent the development of complications and improve outcomes. Disclosures: Jordan: Novartis Pharmaceuticals Corporation: Consultancy, Speakers Bureau. Oneal:Novartis Pharmaceuticals Corporation: Honoraria. Vekeman:Novartis Pharmaceuticals: Research Funding. Bieri:Novartis Pharmaceuticals Corporation: Research Funding. Sasane:Novartis Pharmaceuticals: Employment. Marcellari:Novartis Pharmaceuticals Corporation: Employment. Magestro:Novartis Pharmaceuticals: Employment. Gorn:Novartis Pharmaceuticals Corporation: Research Funding. Duh:Novartis Pharmaceuticals: Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.275
Teacher spread0.253 · 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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Citations0
Published2012
Admission routes1
Has abstractyes

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