Evidence map›Paper›PMID 35547332›Full record

ArticleAfrican journal of laboratory medicine2022

Red cell distribution width as a surrogate marker of haemoglobinopathies in western Kenya.

Benard M Mutua, George Sowayi, Patrick Okoth

Open access · diamondAbstract read
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Article in African journal of laboratory medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Benard M MutuaDepartment of Medical Laboratory Sciences, School of Public Health Biomedical Sciences and Technology, Masinde Muliro University of Science and Technology, Kakamega, Kenya.ORCID https://orcid.org/0000-0001-7141-302X
George SowayiDepartment of Medical Laboratory Sciences, School of Public Health Biomedical Sciences and Technology, Masinde Muliro University of Science and Technology, Kakamega, Kenya.ORCID https://orcid.org/0000-0002-7626-5385
Patrick OkothDepartment of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, Kakamega, Kenya.ORCID https://orcid.org/0000-0002-0241-8799
Masinde Muliro University of Science and Technology · KE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Haemoglobinopathies are inherited haemoglobin disorders that result in anaemia characterised by erythrocyte anisopoikilocytosis. Red cell distribution width (RDW) measures anisopoikiloytosis and is readily reported by haematology analysers as a complete blood count parameter. The utility of RDW as a diagnostic marker of haemoglobinopathies in Kenya remains undetermined and undocumented. Objective: This study aimed to determine the diagnostic efficacy of RDW in discriminating haemoglobinopathy and haemoglobinopathy-free cases in Kenya. Methods: The case-control study used randomly selected haematology analyser outputs for haemoglobinopathy-free (241, 49.4%) and haemoglobinopathy cases (247, 50.1%) aged 1 month to 66 years old tested in the Aga Khan Hospital, Kisumu, and its satellite centres in western Kenya from 01 January 2015 to 31 December 2020. Results were verified using high performance liquid chromatography. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic power of RDW as a biomarker for sickle cell disease (SCD) and sickle cell trait phenotypes and β-thalassaemia. Results: The RDW showed diagnostic significance in SCD phenotypes at 21.1 ROC curve coordinate with 67.7% sensitivity, 90.0% specificity, 0.789 accuracy, 70.5% positive predictive validity, 88.8% negative predictive validity, 6.77 positive likelihood ratio, 0.36 negative likelihood ratio and 18.94 (11.4-31.4) odds ratio. Conclusion: An RDW of 21.1% is potentially a predictor of SCD haemoglobin phenotypes and should be included in the haematology screening algorithm as a critical value, above which suspected cases qualify to be investigated for SCD.

Indexed as

biomarkerhaemoglobinopathiespatientsred cell distribution widthsurrogate markerwestern Kenya

Identifiers

PMID35547332
PMCPMC9082283
OpenAlexW4225327432

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.