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Record W2985736759 · doi:10.1182/blood-2019-123069

Comorbidities and Complications in Adults with Pyruvate Kinase Deficiency

2019· article· en· W2985736759 on OpenAlexaffabout
Audra Boscoe, Yan Yan, Elizabeth Hedgeman, Eduard J. van Beers, Hanny Al‐Samkari, Wilma Barcellini, Stefan Eber, Bertil Glader, Hassan M. Yaish, Satheesh Chonat, Jennifer Rothman, Mukta Sharma, Nina Kollmar, Christine Knoll, Kevin H.M. Kuo, Janet L. Kwiatkowski, Yves Pastore, Alexis A. Thompson, Yaddanapudi Ravindranath, Ellis J. Neufeld, Heng Wang, Susanne Holzhauer, Vicky R. Breakey, Madeleine Verhovsek, Joachim B. Kunz, Sujit Sheth, Melissa A. McNaull, Melissa J. Rose, Dagmar Pospı́šilová, Jenny M. Despotovic, Rachael F. Grace

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicinePyruvate kinase deficiencyComorbidityPopulationCohortPyruvate kinasePediatricsAnemiaRetrospective cohort studyNatural historyCohort studyBlood transfusionInternal medicine

Abstract

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Introduction: Pyruvate kinase (PK) deficiency causes a defect in the glycolytic pathway, leading to a hereditary hemolytic anemia. Management is supportive and consists of splenectomy, transfusions, and chelation therapy. Aim: To better understand the comorbidity and complication profile of adults with PK deficiency, and the extent to which transfusion frequency contributes, the objectives of this study were to (1) quantify the prevalence of comorbidities and complications according to transfusion history and (2) compare the types and rates of select comorbidities and complications with the general population. Methods: Data were obtained from the enrollment survey of the PK Deficiency Natural History Study (NHS), a longitudinal, retrospective and prospective cohort study in which clinical, laboratory, transfusion, and radiologic data were collected; all participants were confirmed to have 2 mutations in the PKLR gene. Patients (n=122) were eligible for this analysis if they were ≥18 years of age and had sufficient data on transfusion history to enable classification into 1 of 3 cohorts: "Ever Regularly Transfused" (ERT, defined as ≥6 transfusions in any 12-month period), "Never Regularly Transfused" (NRT, defined as having ≥1 lifetime transfusion but never having >4 transfusions in any 12-month period), or "Never Transfused" (NT). To contextualize the findings, the frequencies of select conditions were compared with an age- and gender-matched cohort of individuals from the insured, general US population who did not have any hemolytic anemia diagnoses and had ≥5 years of continuous enrollment in the Truven MarketScan administrative claims database. The NHS reported lifetime prevalence rates, whereas rates obtained from the MarketScan data were based on diagnosis and procedure codes over varying look-back periods; therefore, to minimize bias, we limited PK deficiency vs. general population comparisons to (1) chronic conditions that require lifetime management and would thus still be recorded in claims data years after initial diagnosis, and/or (2) conditions for which a diagnosis/procedure date was available in the NHS and could be matched in time to the average 8-year look-back period for the general population. Frequencies were compared across mutually exclusive cohorts using Fisher's exact 2-tailed tests of significance. Results: ERT (n=65), NRT (n=30), and NT patients (n=27) had a mean age of 34.2, 39.5, and 37.2 years at enrollment, respectively (not significant [ns]), with 46.2%, 56.7%, and 59.3%, respectively, being male (ns). ERT patients trended toward being more likely than NT patients to be Amish and have the homozygous R479H splice variant (30.8% vs 11.1% [p=0.064]) but were significantly less likely to have a missense/missense PKLR genotype (32.3% vs 70.4% [p=0.001]). Compared with the general population, patients with PK deficiency had significantly higher rates of splenectomy, cholecystectomy, osteoporosis, liver cirrhosis, pulmonary hypertension, and current prophylactic antibiotic and anticoagulant use (Table). Rates of splenectomy, cholecystectomy, and osteoporosis were significantly higher in patients with PK deficiency, regardless of transfusion cohort, and both ERT and NRT patients had significantly higher rates of liver cirrhosis than individuals from the general population. A gradient was seen across transfusion cohorts for other conditions. Notably, 83.1% of ERT patients, 50.0% of NRT patients, and 25.9% of NT patients had a history of liver iron overload. ERT patients were also significantly more likely than NRT and NT patients to have had a splenectomy, cholecystectomy, and/or thrombosis, and to currently use prophylactic antibiotics. Findings were consistent when the analysis was restricted to non-Amish patients with PK deficiency. Conclusions: Patients with PK deficiency have higher rates of select comorbidities and complications than age- and gender-matched individuals who do not have PK deficiency. Even patients with PK deficiency who have never been transfused are at increased risk of complications of the disease and its treatment. Disclosures Boscoe: Agios Pharmaceuticals, Inc.: Employment, Equity Ownership. Yan:Agios Pharmaceuticals, Inc.: Consultancy. Hedgeman:IBM Watson Health: Employment. van Beers:Agios Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Research Funding; RR Mechatronics: Research Funding. Al-Samkari:Agios: Consultancy, Research Funding; Dova: Consultancy, Research Funding; Moderna: Consultancy. Barcellini:Incyte: Consultancy; Alexion: Consultancy, Speakers Bureau; Agios Pharmaceuticals, Inc.: Consultancy; Novartis: Speakers Bureau; Apellis: Consultancy; bioverativ: Consultancy. Eber:Agios Pharmaceuticals, Inc.: Consultancy. Glader:Agios Pharmaceuticals, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Chonat:Alexion: Other: advisory board; Agios Pharmaceuticals, Inc.: Other: advisory board. Rothman:Agios: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding. Kuo:Agios: Consultancy; Alexion: Consultancy, Honoraria; Apellis: Consultancy; Bioverativ: Other: Data Safety Monitoring Board; Bluebird Bio: Consultancy; Celgene: Consultancy; Novartis: Consultancy, Honoraria; Pfizer: Consultancy. Kwiatkowski:Agios: Consultancy; bluebird bio, Inc.: Consultancy, Research Funding; Imara: Consultancy; Apopharma: Research Funding; Novartis: Research Funding; Celgene: Consultancy; Terumo: Research Funding. Ravindranath:Agios Pharmaceuticals, Inc.: Other: I am site PI on several Agios-sponsored studies, Research Funding. Neufeld:Octapharma, Shire Pharmaceuticals (Baxalta), Novo Nordisk, Celgene, NHLBI/NIH: Research Funding; Octapharma, Agios, Acceleron, Grifols, Pfizer, CSL Behring, Shire Pharmaceuticals (Baxalta), Novo Nordisk, ApoPharma, Genentech, Novartis, Bayer Healthcare: Consultancy; Octapharma: Other: study investigator, NuProtect study (Octapharma-sponsored). Holzhauer:Agios Pharmaceuticals, Inc.: Consultancy. Verhovsek:Sickle Cell Disease Association of Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding; Canadian Haemoglobinopathy Association: Membership on an entity's Board of Directors or advisory committees; Vertex: Consultancy; Sickle Cell Awareness Group of Ontario: Membership on an entity's Board of Directors or advisory committees. Kunz:Novartis: Membership on an entity's Board of Directors or advisory committees. Sheth:Apopharma: Other: Clinical trial DSMB; Celgene: Consultancy; CRSPR/Vertex: Other: Clinical Trial Steering committee. Despotovic:Novartis: Research Funding; Dova: Honoraria. Grace:Agios Pharmaceuticals, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: 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 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 score0.033
Threshold uncertainty score0.166

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)0.0000.000

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 head0.230
Teacher spread0.218 · 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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Citations3
Published2019
Admission routes2
Has abstractyes

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