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Enregistrement W2738096170 · doi:10.1002/ajh.24871

Kidney function of transfused children with sickle cell anemia: Baseline data from the TWiTCH study with comparison to non‐transfused cohorts

2017· letter· en· W2738096170 sur OpenAlexaboutno aff
Ofelia Álvarez, Kerri Nottage, Lara M. Simpson, John C. Wood, Barry R. Davis, Beng Fuh, Sharada A. Sarnaik, Banu Aygün, Kathleen J. Helton, Russell E. Ware

Notice bibliographique

RevueAmerican Journal of Hematology · 2017
Typeletter
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensnon disponible
Organismes subventionnairesNational Heart, Lung, and Blood Institute
Mots-clésMedicineRenal functionAlbuminuriaCreatinineUrologyInternal medicineKidney diseaseGlomerular hyperfiltrationKidneyDiabetic nephropathy

Résumé

récupéré en direct d'OpenAlex

The phase III multicenter clinical trial TWiTCH (NCT See Appendix for list of investigators) enrolled children with sickle cell anemia (SCA), who had previous abnormal transcranial Doppler ultrasound velocities (TCD V) and were receiving chronic transfusions for primary stroke prevention.1 Because the impact of chronic transfusions on sickle cell nephropathy is unknown in children, we investigated baseline kidney function of this transfused cohort and compared it to the prevalence of albuminuria and glomerular hyperfiltration in two age-matched non-chronically transfused cohorts at St. Jude (HUSTLE, NCT00305175) and University of Miami (UM). Entry renal function evaluation in TWiTCH included serum creatinine and cystatin C; spot urine for albumin/creatinine ratio (ACR) and specific gravity; abdominal ultrasound with measurement of kidney length and volume; and abdominal MRI R2* that measured liver, kidney, and pancreas iron content. Glomerular filtration rate (GFR) was estimated by bedside Schwartz and Schwartz CKiD equations. Glomerular hyperfiltration was defined as estimated GFR > 1 standard deviation above the mean for age. Albuminuria was present when ACR ≥ 30 mg/g creatinine and was reported as microalbuminuria if ACR was 30–300 mg/g creatinine. Macroalbuminuria was defined as ACR > 300 mg/g creatinine. UM and HUSTLE data for aged-matched (4–15 years) children with hemoglobin (Hb) SS or HbS/β0-thalassemia, not receiving chronic erythrocyte transfusions or hydroxyurea, were analyzed for comparable kidney parameters. None of the children had an abnormal TCD when the renal parameters were assessed. Both cohorts have been partially published.2, 3 Descriptive analyses (independent sample t-tests and contingency tables) were performed on baseline demographic, clinical, and kidney function parameters. Predictors of albuminuria were assessed using stepwise multivariate logistic regression. Assessment of associations with CKiD Schwartz was performed using stepwise linear regression. Baseline covariates included age at screening and at start of transfusions, Hb concentration, %HbS level, reticulocyte count, LDH, presence or absence of hyperfiltration (logistic only), months of chelation therapy, serum ferritin, kidney R2* and liver R2* measurements, maximum time-averaged TCD V, magnetic resonance angiography (MRA) vasculopathy staging 1–3,4 and brain MRI evidence of silent infarction or other parenchymal abnormalities. Two-sided t-tests and contingency tables compared age at baseline, estimated GFR and ACR among TWiTCH, UM, and HUSTLE cohorts. P-values ≤.05 were considered statistically significant, with no adjustment for multiple comparisons. There were a total of 121 randomized TWiTCH participants, mean age 9.5 ± 3.0 years, and 73 (60%) were females. Children had a normal body mass index (mean 17.7 ± 3.6) and were not hypertensive (mean systolic and diastolic pressures were 109 ± 10 and 61 ± 8 mm Hg, respectively). TWiTCH participants had started transfusions at a mean age of 5.5 ± 2.0 years, and were transfused for an average of 4.4 years (range 1.0–10.8 years) at enrollment. Study participants had mean baseline Hb = 9.2 ± 0.8 g/dL, mean %HbS = 27 ± 10%, and elevated ferritin = 2895 ± 2275 μg/L. The vast majority (107, 88%) of the children had iron overload and were prescribed chelation for 34 ± 24 months preceding study enrollment. Participants had received iron chelation with deferasirox only (N = 92), deferoxamine only (N = 1), or both (N = 14). TWiTCH participants had an average bedside Schwartz GFR = 140.1 ± 66.7mL/min/1.73 m2 (27.0% with hyperfiltration) and CKiD Schwartz GFR = 122.2 ± 29.8 mL/min/1.73 m2 (13.3% with hyperfiltration). There were 31 (27.9%) and 23 (20.7%) with bilateral and unilateral kidney enlargement, respectively (combined prevalence of 48.7%). Mean renal R2* was elevated (≥35 Hz), indicating increased iron content. Twelve children (10.3%) had microalbuminuria (mean urine ACR 60.2 ± 25.5 mg/g creatinine, range 34.5–121.3 mg/g creatinine) and none had macroalbuminuria. Children with albuminuria had significantly higher estimated GFR by bedside Schwartz (199.80 ± 153.0 vs. 132.94 ± 45.0 mL/min/1.73 m2, P = .001) and CKiD Schwartz (140.58 ± 44.9 vs. 119.96 ± 26.5 mL/min/1.73 m2, P = .025), and lower renal R2* compared to children without albuminuria (39.02 ± 18.3 vs. 74.81 ± 49.7 Hz, P = .035). GFR significantly correlated with renal R2* and absolute reticulocyte count in univariate analysis, but only with renal R2* in multivariate analysis. The correlation between renal R2* and LDH was 0.48 (P < .0001), indicating that renal R2* was associated with endothelial inflammation and/or hemolysis. Albuminuria was significantly associated with MRA cerebral vasculopathy, with an odds ratio of 7.17 (95% CI 1.50–34.23, P = .013), and a 3% reduction in risk of albuminuria was observed for each renal R2* unit increase. The maximum mean TCD V at baseline and iron chelation were not independently associated with albuminuria. Table 1 shows the comparison between TWiTCH and the two non-transfused cohorts. TWiTCH participants had lower prevalence of albuminuria (10%) compared to non-transfused cohorts with 14%-22% prevalence (P = 0.049). The mean GFR in TWiTCH was significantly lower than the mean GFR in HUSTLE, but not significantly different than the UM cohort. This is the first description of kidney function parameters in large age-matched cohorts of school-age children with SCA, analyzed by the presence of chronic transfusions. In TWiTCH, we did not detect associations with albuminuria and hemolytic laboratory parameters, which could reflect treatment effect by chronic transfusions, the relatively small number of children with albuminuria, or lack of a pathophysiological correlation between intravascular hemolysis and albuminuria. Despite the known risk of developing proteinuria with the use of the iron chelator deferasirox, we also did not find an association between iron chelation and albuminuria. Albuminuria was associated with cerebral vasculopathy on MRA, but not with baseline TCD V. This association suggests that sickled-related endothelial damage and dysfunction may contribute to the development of both cerebral vasculopathy and albuminuria. Microalbuminuria was an independent predictor of future stroke (Cox proportional hazard ratio 4.9) in a general population of adults.5 Our finding establishes that such association also exists in children with SCA and mild (stages 1–3) cerebral vasculopathy. We found that the prevalence of baseline albuminuria was lower in TWiTCH than the non-transfused and non-chelated cohorts, and within the range of that encountered (up to 12%) in screening healthy children aged 8–18 years (Third National Health and Nutrition Examination Survey (NHANES III).6 Because albuminuria was associated with cerebral vasculopathy, the fact that fewer children had albuminuria in TWITCH than in the non-transfused cohorts suggests a therapeutic or protective effect of transfusions on renal function. This work was supported by the National Heart Lung and Blood Institute (NHLBI), through grants R01 HL-095647 (REW) and R01 HL-095511 (BRD). Dr. Ofelia Alvarez participated in an advisory board for Novartis. Dr. Kerri Nottage has been employed by Janssen Research & Development, LLC. Dr. John Wood served as a consultant for Vifor, Apopharma, and Ionis. Dr. Sharada Sarnaik is an advisory board member for AstraZeneca. Dr. Russell Ware is a consultant for Global Blood Therapeutics and Nova Laboratories. Drs. Simpson, Davis, Fuh, Aygun, and Helton have nothing to disclose. Ofelia Alvarez http://orcid.org/0000-0003-4811-267X Principal Investigator: Russell E. Ware, MD, PhD, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio. Clinical Site Investigators: Texas Children's Hospital, Houston, Texas: Alex George, MD, PhD, Brigitta U. Mueller, MD, MHCM; Children's Hospital, Boston, Massachusetts: Matthew M. Heeney, MD; Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio: Theodosia A. Kalfa, MD, PhD; Children's Hospitals and Clinics of Minnesota, Minneapolis, Minnesota: Stephen Nelson, MD; Emory/CHOA, Atlanta, Georgia: R. Clark Brown, MD PhD; Co-Investigator: Beatrice Gee, MD; Children's Hospital of Philadelphia, Philadelphia, Pennsylvania: Clinical Investigator: Janet L. Kwiatkowski, MD, MSCE; Co-Investigator: Kim Smith-Whitley, MD; The Hospital for Sick Children, Toronto, Ontario, Canada: Isaac Odame, MB ChB, FRCPath, FRCPC; Children's National Medical Center, Washington, DC: Lori Luchtman Jones MD; Jennifer Webb, MD; Co-Investigators: Brenda Martin, MSN CPNP, and Elizabeth Yang, MD PhD; Columbia University, New York, New York: Margaret T. Lee, MD; Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio: Connie Piccone, MD; University of South Alabama (USA), Mobile, Alabama: Hamayun Imran, MD, MSc; Medical University of South Carolina, Charleston, South Carolina: Sherron M. Jackson, MD; Children's Medical Center of New York, New Hyde Park, New York: Banu Aygun, MD, Sharon Singh, MD; St. Jude Children's Research Hospital, Memphis, Tennessee: Kerri Nottage, MD, MPH, Jane S. Hankins, MD, MS; State University of New York- Downstate Medical Center, Brooklyn, New York: Scott T. Miller, MD; University of Alabama at Birmingham (UAB) Birmingham, Alabama: Lee Hilliard, MD; University of Miami Miller School of Medicine, Miami, Florida: Ofelia Alvarez, MD; University of Mississippi Medical Center (UMMC), Jackson, Mississippi: Melissa Rhodes, MD, Rathi Iyer, MD; UT Southwestern, Dallas, Texas: Zora R. Rogers, MD; Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan: Sharada A. Sarnaik, MD; Anna and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois: Alexis A. Thompson, MD, MPH; Children's Hospital of The King's Daughters, Norfolk, Virginia: William C. Owen, MD; Nemours Children's Clinic, Jacksonville, Florida: Cynthia Gauger, MD; University of South Carolina/Palmetto Health, Columbia, South Carolina: Carla Roberts, MD; Duke University Medical Center, Durham, North Carolina: Jennifer A. Rothman, MD; Brody School of Medicine at East Carolina University, Greenville, North Carolina: Beng Fuh, MD, Charles Daeschner, MD Medical Coordinating Center Cincinnati Children's Hospital, Cincinnati, Ohio- Principal Investigator: Russell E. Ware, MD, PhD; Clinical Coordinator and Medical Monitor: William H. Schultz, MHS, PA-C; Project Manager: Susan Stuber MA, CCRP, RAC Data Coordinating Center UT School of Public Health, Houston, Texas- Principal Investigator: Barry R. Davis, MD, PhD; Co-Investigator: Sara Pressel, MS, Peng Wei, PhD, Seoun Kim, PhD; Project Manager: Cecilia Lara, BS; Safety: Linda Piller, MD, MPH, Lara Simpson, PhD, Aliza Matusevich Neurology and TCD Core MUSC Stroke Center, Charleston, South Carolina- Principal Investigator: Robert J. Adams, MD, PhD Central Laboratory, Georgia Health Sciences University, Augusta, Georgia: Abdullah Kutlar, MD and Niren Patel, MBBS Consultants: Abdominal MRI: John C. Wood, MD PhD; Neuroradiology: Kathleen J. Helton, MD and Donna Roberts, MD; Ultrasound: Jamie Coleman, MD; Neurocognitive: Melanie J. Bonner, PhD; Hematology: Nicole Mortier, MHS, PA-C; Transfusions: Naomi Luban, MD; Iron/Chelation: Alan R. Cohen, MD

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,318
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,003
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 Opus0,014
Tête enseignante GPT0,263
Écart entre enseignants0,250 · 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.

Devis d'étudeSans objet
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

Citations11
Publié2017
Routes d'admission1
Résumé présentoui

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