MétaCan
Menu
← Back to cohort
Record W2399992468 · doi:10.1182/blood.v122.21.982.982

High Resource Utilizers From The Multicenter Compact Study Of Complications In Patients With Sickle Cell Disease and Utilization Of Iron Chelation Therapy

2013· article· en· W2399992468 on OpenAlexaff
Lanetta Jordan, Patricia Adams‐Graves, Julie Kanter-Washko, Patricia O’Neal, Medha Sasané, Francis Vekeman, Christine Bieri, Andrea Marcellari, Matthew Magestro, Abigail Adams, Mei Sheng Duh

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineCohortPopulationPsychological interventionLogistic regressionAnemiaInternal medicineBlood transfusionEmergency medicinePediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Introduction While treating patients (pts) with sickle cell disease (SCD) can be costly, costs are not evenly distributed across pts; rather, a minority of pts accounts for a majority of costs. Identifying those pts who consume a disproportionately large share of healthcare resources can assist payers and providers in directing appropriate and targeted interventions to deliver better pt care with lower costs. The objective of this study was to understand characteristics of pts who have increased utilization of inpatient (IP) and emergency department (ED) resources in a population of SCD pts ≥16 years old. Method Medical records of 254 SCD pts ≥16 years old were retrospectively reviewed between 8/2011 and 7/2012 at three US tertiary care centers. The high utilization threshold was derived from the literature and defined as pts with ≥ 5 days of IP+ED care (assuming 1 day/ED visit) for SCD-related complications per year (high utilizer group). Pts were also classified into cohorts based on cumulative blood transfusion units and use iron chelation therapy (ICT): <15 units, no ICT (Cohort 1 [C1]), ≥15 units, no ICT (Cohort 2 [C2]), and ≥15 units, with ICT (Cohort 3 [C3]). SCD complication rates were expressed as the number of SCD complications per pt per year (PPPY); rate ratios (RRs) were used for cohort comparisons. A logistic regression was used to identify risk factors associated with high utilization of IP+ED care. Results Of the 254 pts (C1: 69, C2: 91, C3: 94), 30% (n =76) were classified as high utilizers (C1: 14 [18.4%], C2: 37 [48.7%], C3: 25 [32.9%]). Patients in the high utilizer group were younger (median [range] (21 years old [16-65], vs. 23 years old [16-59]) and had shorter follow-up (4.2 years [0.6-23.9], vs. 5.4 years [0.5-33.3]) compared to the rest of the sample. Those in the high utilizer group accounted for 68% of all SCD-related complications and over 88% of all IP+ED days for treatment of these complications. Similar to the rest of the sample, pain (81%) and infection (7%) were the two key complications seen in this high utilizer group. The rate of IP +ED days was significantly higher among the high utilizer group with 16.63 [16.28-16.99] IP+ED days PPPY compared to 0.89 [0.84-0.94] PPPY for other pts. Similarly, the high utilizer group had 4.58 [95% CI: 4.39-4.76] IP+ED visits PPPY, compared to 0.34 [0.31-0.37] visits PPPY for other pts (Table). Among regularly transfused pts (C2+C3) in the high utilizer group, those who received ICT had lower rates of IP+ED visits (C2 vs. C3 rate ratio [RR] [95% CI]: 1.31[1.20-1.44]), IP+ED days (C2 vs. C3 RR: 1.30 [1.24-1.36]), and readmission to IP+ED settings within 30 days (1.70 [1.49-1.93]) compared with those who did not (Table). History of infections (odds ratio: 7.45, p<0.0001) was associated with an increased risk of high utilization of IP+ED care. Conclusion Results from this study show that a relatively small fraction of SCD pts account for the majority of IP+ED visits. Moreover, among regularly transfused pts identified as high utilizers, those who received ICT had lower rates of IP+ED utilization than those who did not. Pts 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. Closer management of pts with SCD, especially those at risk of becoming high utilizers, is critical to lowering IP+ED utilization and reducing the overall costs of care. Disclosures: Jordan: Novartis Pharmaceuticals Corporation: Consultancy. Adams-Graves:Analysis Group, Inc.: Research Funding. Kanter-Washko:Analysis Group, Inc.: Research Funding. Oneal:Novartis Pharmaceuticals Corporation: Honoraria; Analysis Group, Inc.: Research Funding. Sasane:Novartis Pharmaceuticals: Employment. Vekeman:Novartis Pharmaceuticals: Research Funding. Bieri:Novartis Pharmaceuticals Corporation: Research Funding. Marcellari:Novartis Pharmaceuticals Corporation: Employment. Magestro:Novartis Pharmaceuticals: Employment. Adams: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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicHemoglobinopathies and Related Disorders→French-language works237,207→