Evidence map›Paper›PMID 40411598›Full record

ArticleAnnals of hematology2025

Platelet-to-Neutrophil ratio as a predictor of risk of complications in sickle cell disease: a valuable insight for resource-limited settings.

Chilota Efobi, Nnanna Ukpai, Onyinye Ezinne Eze, Bruno Basil

Abstract read
In one paragraph

Article in Annals of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

Chilota EfobiDepartment of Haematology and Immunology, Nnamdi Azikiwe University, Awka, Nigeria.
Nnanna UkpaiDepartment of Otorhinolaryngology, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria.
Onyinye Ezinne EzeDepartment of Haematology and Immunology, Enugu State University of Science and Technology, Enugu, Nigeria.
Bruno BasilInternational Institute of Pathology and Forensic Science Research, David Umahi Federal University of Health Sciences, Uburu, Nigeria. basilb@dufuhs.edu.ng.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sickle cell disease (SCD) presents significant clinical challenges, particularly in resource-limited settings where early identification of high-risk patients remains difficult, necessitating the use of simple, cost-effective biomarkers to improve risk stratification and guide timely interventions. This study investigates the potential of platelet-to-neutrophil ratio (PNR) to serve as a predictor for risk of specific SCD complications in a population of Nigerian patients. This hospital-based cross-sectional analytical study was conducted over 7 months and included a total of 73 adult patients with haemoglobin SS genotype in a steady state. Data on socio-demographics, medical history, clinical characteristics, and laboratory parameters, including platelet-to-neutrophil ratio (PNR) were obtained. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 25. Comparative statistical analyses, binary logistic regression, and receiver operating characteristic (ROC) curve evaluations were performed to determine the predictive value of PNR for risk of SCD complications. Patients with avascular necrosis (AVN) had significantly lower median PNR values compared to those without the condition (151.0 IQR: 311.2 vs. 77.5 IQR: 61.0, p = 0.004). Multivariate logistic regression analysis identified PNR as an independent predictor of AVN (p = 0.034, OR = 1.003, 95% CI: 1.000-1.006), with an ROC-derived optimal cutoff of 96.6 yielding a sensitivity of 69.6% and specificity of 76.5% (AUC = 0.733, p = 0.004). This study reveals that RNR demonstrates reasonable potential as a predictor of AVN in Nigerian SCD patients and could serve as an easily accessible biomarker for SCD risk stratification, particularly in resource-limited settings. Further studies are needed for validation of its clinical utility and possible integration into SCD management protocols.

Indexed as

Anemia, Sickle CellBlood PlateletsNeutrophilsAdolescentAdultBiomarkersCross-Sectional StudiesFemaleHumansMaleMiddle AgedNigeriaPlatelet CountPredictive Value of TestsResource-Limited SettingsRisk FactorsBiomarkers

Identifiers

PMID40411598
PMCPMC12141157

What OpenQuestion holds

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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.