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

Comorbidities and Complications in Adults with Pyruvate Kinase Deficiency

2019· article· en· W2985736759 sur 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

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueErythrocyte Function and Pathophysiology
Établissements canadiensCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicinePyruvate kinase deficiencyComorbidityPopulationCohortPyruvate kinasePediatricsAnemiaRetrospective cohort studyNatural historyCohort studyBlood transfusionInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,033
Score d'incertitude au seuil0,166

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,230
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2019
Routes d'admission2
Résumé présentoui

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