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Record W4405054049 · doi:10.1182/blood-2024-203775

Impact of Clonal Size and High Disease Activity on Clinical Outcomes of Paroxysmal Nocturnal Hemoglobinuria: A Multicenter Registry

2024· article· en· W4405054049 on OpenAlexaboutno aff
Elia Ixel Apodaca Chavez, José Luis Álvarez Vera, Ana Florencia Ramírez, Juan Manuel Sánchez Pérez, Jose Antonio Dela Peña Celaya, Larisa Ramirez, David Solar Cafaggi, Álvaro Aguayo, Efreen Montaño Figueroa

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsnot available
Fundersnot available
KeywordsParoxysmal nocturnal hemoglobinuriaMedicineInternal medicineDiseasePediatricsImmunology

Abstract

fetched live from OpenAlex

Introduction Paroxysmal nocturnal hemoglobinuria (PNH) is a hematopoietic stem cell disorder that can lead to outcomes such as thrombosis, cardiovascular diseases, renal disfunction (both acute kidney injury [AKI] and chronic kidney disease [CKD]), and death (1,2). Complement inhibitors (CIs) are currently the standard of treatment, but there is no uniform consensus on whom to treat due to the rarity of the disease. The updated analysis of the International PNH Registry (2) suggests using CIs in patients with high disease activity (HDA), while the consensus of the Canadian PNH Network recommends using CIs in patients with HDA plus clonality >10% (2,3). Our aim is to determine whether HDA and clone size influence clinical outcomes in PNH in order to support either treatment recommendation. Methods This retrospective study was conducted at 4 centers in Mexico and included patients diagnosed with PNH by flow cytometry (FLAER+CD14/CD24). Characteristics at diagnosis such as symptoms, lactate dehydrogenase (LDH), thrombosis, cardiovascular events, clonality, and HDA (LDH ≥1.5 times the upper normal limit [UNL] plus PNH symptoms, hemoglobin ≤10g/dl, renal function impairment, or thrombosis) were collected. A descriptive analysis was performed, and a univariate logistic regression model was used to determine the impact of clone size and HAD on clinical outcomes. Results Information was collected from 136 patients diagnosed with PNH between 1988 and 2023, with 51.5% (n=70) being women, and a median age of 47 years (IQR;34-57). The classic PNH variant was most frequent, with 69.9% (n=95). The median follow-up was 65.99 months (IQR 14.1-106.5), with a median overall survival of 119 months (CI95% 91.6-183), and 68 patients (50.0%) deceased at the time of analysis. History of thrombosis was found in 19 patients (14%) and in 5 (3.7%) at diagnosis. Cardiovascular events (transient ischemic attack, myocardial infarction, unstable angina, and/or non-traumatic gangrene) occurred in 11 (8.08%). AKI was found in 22 (16.2%) at diagnosis and developed in 25 (18.4%) later. By last follow-up, 28 patients (20.6%) had CKD, 14 of them being on renal replacement therapy. LDH ≥1.5 times the UNL was found in 91 patients (66.9%) and 84 (61.8%) met the definition of HDA. An increase in LDH ≥1.5 times the UNL increased the probability of having hemoglobinuria (OR 11.5, CI95% 4.13-31.8, p<0.001), but not other symptoms. HDA increased the risk of thrombosis (OR 3.84, CI95% 1.06-13.9, p=0.03), cardiovascular disease (OR 14.5, CI95% 0.83-253, p=0.010), AKI (OR 7.81, CI95% 1.74-35.0, p=0.002), and CKD (OR 4.80, CI95%:1.56-14.8, p=0.003). Clonality ≥50% was found in 66.9%. Clone size was not associated with the development of thrombosis (OR 1.28, CI95% 0.46-3.57, p=0.632), AKI (OR 1.34, CI95% 0.51-3.49, p=0.55), CKD (OR 1.63, CI95% 0.63-4.18, p=0.307), or cardiovascular events (OR 2.02, CI95% 0.41-9.96, p=0.377). Discussion and Conclusions In the studied population, HDA was associated with severe outcomes such as thrombosis, AKI, CKD, and cardiovascular disease, while clone size was not associated with either of these outcomes. This suggests that the presence of HDA in PNH may justify the initiation of CI treatment regardless of clone size.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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