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Record W2984904736 · doi:10.1182/blood-2019-124381

Prevalence of Red Cell Pyruvate Kinase Deficiency: A Systematic Literature Review

2019· article· en· W2984904736 on OpenAlexaboutno aff
Mike Storm, Matthew H. Secrest, Courtney Carrington, Keely Gilroy, Leanne Pladson, Audra Boscoe, Deborah Casso

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsnot available
Fundersnot available
KeywordsPyruvate kinase deficiencyMedicinePyruvate kinasePopulationIncidence (geometry)PediatricsMEDLINEEpidemiologyInternal medicineBiologyEnvironmental healthGlycolysis

Abstract

fetched live from OpenAlex

Introduction: Red cell pyruvate kinase deficiency (PKD) is a rare congenital disorder caused by compound heterozygosity or homozygosity for >300 mutations in the PKLR gene. The resulting glycolytic defect can lead to lifelong chronic hemolysis and associated symptoms, including anemia, jaundice, and iron overload. Reports of PKD prevalence vary, likely due to several factors, including PKD rarity, differences in measurement approaches, diagnostic challenges, and variable clinical expression. To understand the prevalence of PKD, we conducted a systematic literature review to identify and evaluate analyses of PKD epidemiology or PKLR mutant allele frequency (MAF). Methods: We queried Embase and Medline, screened conference abstracts, and considered relevant articles encountered incidentally to identify peer-reviewed references published before January 23, 2019 reporting PKD prevalence or incidence, PKLR MAF among the general population, or crude results from which prevalence, incidence or MAF could be derived. Two independent reviewers screened for eligibility and extracted data from eligible studies using a custom tool. Results: Of 1390 references screened, 1296 were excluded after title/abstract review (Figure). Of the remaining 94 references, 34 met eligibility criteria on full text review. In 30/34 eligible studies, potential sources of bias in PKD prevalence were identified, such as a non-generalizable study population (e.g., malaria-endemic areas; n=17), use of diagnostic assays of questionable accuracy (n=9), consideration of mutations with incomplete penetrance (n=3), and consideration of mutations with unclear clinical significance (n=1). The remaining 4 studies were considered high-quality for the estimation of PKD prevalence and were further assessed. Among these 4 studies, an important distinction was made between studies reporting diagnosed prevalence (n=3) and overall disease prevalence (diagnosed and undiagnosed PKD; n=1). Two studies estimated diagnosed PKD prevalence as 3.2 per million[i] and 8.5 per million[ii] by identifying diagnosed PKD cases from source populations of known size. We estimated the prevalence of diagnosed PKD in a general population to be 6.5 per million using data from another high-quality study[iii] that screened newborns for bilirubin and tested jaundiced newborns for PKD. These 3 studies are likely underestimates because they only consider diagnosed cases, and in one study only diagnosed, jaundice cases. In the final study,[iv] the authors sought to limit biases related to the Hardy-Weinberg equilibrium (HWE) assumption, as HWE incorrectly assumes the penetrance of each mutation is 100%. To address this, the authors identified a mutation known to have high penetrance (c.1529G>A), referred to as the 'index mutation'. They then assumed that the frequency of the index mutation relative to other PKD-causing mutations is the same between the general population and PKD cases, which led to a prevalence estimate (diagnosed and undiagnosed) of 51 per million (standard error: 32.5 per million). Conclusions: We consider the likely range of diagnosed PKD prevalence in general Western populations to be 3.2 to 8.5 per million. However, diagnosed and undiagnosed PKD prevalence may be as high as 51 per million. Future studies are needed to understand the clinical significance of various mutant alleles, which may inform more accurate, clinically-relevant PKD prevalence estimates, identify the degree of and reasons for underdiagnosis, and elucidate PKD heterogeneity between populations. References: [i] Carey PJ, Chandler J, Hendrick A, Reid MM, Saunders PW, Tinegate H, Taylor PR, West N. Prevalence of pyruvate kinase deficiency in a northern European population in the north of England. Blood. 2000 Dec 1;96(12):4005-6. [ii] de Medicis E, Ross P, Friedman R, Hume H, Marceau D, Milot M, Lyonnais J, de Braekeleer M. Hereditary nonspherocytic hemolytic anemia due to pyruvate kinase deficiency: a prevalence study in Quebec (Canada). Human heredity. 1992;42(3):179-83. [iii] Christensen RD, Eggert LD, Baer VL, Smith KN. Pyruvate kinase deficiency as a cause of extreme hyperbilirubinemia in neonates from a polygamist community. Journal of Perinatology. 2010 Mar;30(3):233. [iv] Beutler E, Gelbart T. Estimating the prevalence of pyruvate kinase deficiency from the gene frequency in the general white population. Blood. 2000 Jun 1;95(11):3585-8. Figure Disclosures Storm: Agios: Employment. Secrest:Agios: Other: Received funds as part of contracted research; IQVIA: Employment. Carrington:Agios: Other: Received funds as part of contracted research; IQVIA: Employment. Gilroy:Agios: Employment. Pladson:Agios: Employment. Boscoe:Agios Pharmaceuticals, Inc.: Employment, Equity Ownership. Casso:IQVIA: Employment; Agios: Other: Received funds as part of contracted research.

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)0.010
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.048
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0260.026
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations4
Published2019
Admission routes1
Has abstractyes

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