Platelet-Neutrophil Ratio as a Potential Biomarker for Stroke Risk Stratification in Children and Young Adults with Sickle Cell Anaemia in Resource Poor Settings

Platelet-Neutrophil Ratio as a Potential Biomarker for Stroke Risk Stratification in Children and Young Adults with Sickle Cell Anaemia in Resource Poor Settings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Platelet-Neutrophil Ratio as a Potential Biomarker for Stroke Risk Stratification in Children and Young Adults with Sickle Cell Anaemia in Resource Poor Settings Chisom Adaobi Nri-Ezedi, Chilota Chibuife Efobi, Habib Darbari Deepika, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7557167/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted 7 You are reading this latest preprint version Abstract Background Sickle cell anaemia (SCA) is associated with a significantly increased risk of stroke, primarily assessed using transcranial Doppler ultrasound (TCD). However, access to TCD and haemoglobin F (HbF) testing remains limited in resource-constrained settings. Our preliminary findings identified the platelet-neutrophil ratio (PNR) as a promising biomarker for stroke risk stratification. This updated study evaluates the predictive value of PNR while adjusting for HbF levels and transfusion history. Methods We conducted a retrospective study of 248 HbSS children and young adults at Children’s National Hospital, Washington DC. Demographic, clinical, and hematologic parameters—including PNR, HbF levels, and transfusion status—were analysed in relation to TCD results. Logistic regression and ROC curve analyses were performed. Results Subjects with abnormal TCD values had significantly lower PNR (53.05 vs. 87.65, p < 0.001) and HbF (7.48% vs. 16.35%, p < 0.001) compared to those with normal TCD results. Transfused children were more likely to have abnormal TCD findings (OR = 5.56, 95% CI: 2.30–13.47, p < 0.001). Multivariate analysis confirmed PNR as a significant independent predictor of abnormal TCD (OR = 0.925, p < 0.001). ROC analysis showed superior predictive performance for PNR (AUC = 0.82) compared to HbF (AUC = 0.73) and ANC (AUC = 0.76). Conclusion PNR remains a cost-effective and accessible biomarker for stroke risk stratification, retaining its predictive strength even after adjusting for HbF and transfusion status. Sickle Cell Disease Platelet-Neutrophil Ratio Transcranial Doppler HbF Stroke Figures Figure 1 Figure 2 Introduction Sickle cell disease (SCD) is a hereditary disorder characterized by the production of abnormal haemoglobin, leading to the deformation of red blood cells into a sickle shape. It is one of the most common genetic disorders globally, disproportionately affecting individuals of African, Mediterranean, and Middle Eastern descent [ 1 , 2 ]. SCD is notorious for inducing vaso-occlusive events, which can result in severe complications, including stroke [ 3 ]. Stroke is a particularly alarming complication in SCD, often leading to long-term disability or death [ 4 ]. Remarkably, children with SCD face a stroke risk over 200 times higher than their non-SCD counterparts, making early identification and preventive measures crucial [ 5 ]. Transcranial Doppler ultrasound (TCD), remains the gold standard for stroke risk stratification, as it can identify children with elevated cerebral blood flow velocities who are at risk for primary stroke events [ 6 ]. However, the accessibility of TCD is limited in many parts of the world due to the high cost of equipment, maintenance, and the scarcity of skilled neurosonologists [ 7 ].The cost and infrastructure challenges underscore the urgent need for accessible, scalable biomarkers that can augment or substitute TCD in resource-limited environments. The platelet-neutrophil ratio (PNR), derived from complete blood counts, has emerged as a promising biomarker [ 8 ] has garnered attention as a potential marker for inflammatory and thrombotic states in various conditions. In our preliminary analysis, PNR showed promise as a biomarker for stroke risk in children with SCA. However, that initial study was limited by the unavailability of haemoglobin F (HbF) levels and transfusion status—two clinically relevant variables that have been shown to influence disease severity and cerebral vasculopathy. HbF inhibits HbS polymerisation and reduces disease complications, while chronic transfusions alter haematological profiles and influence stroke risk. In this revised analysis, we re-evaluated the utility of PNR in stroke risk stratification by incorporating HbF levels and transfusion status into the analysis. Our goal was to assess whether PNR retains its predictive strength in the presence of these key modifiers, thus validating its potential as a cost-effective, accessible tool for early identification of children at high risk of stroke in resource-poor settings. Methods Study Design This was a retrospective observational study conducted using data from the electronic medical records of Children's National Hospital, Washington DC. The data spans from September 1, 2013, to September 14, 2023, and includes consenting patients with Sickle Cell Disease (SCD) who participated in the IRB-approved Natural History Study of Sickle Cell Disease at Children's National Hospital. Study Population We included patients aged 5 to 25 years with a confirmed diagnosis of sickle cell anaemia (HbSS genotype) who had undergone transcranial Doppler (TCD) ultrasonography and routine haematological testing within the study period. Patients were excluded if they had a history of prior stroke, splenectomy, bone marrow transplantation, or incomplete clinical records. Children under 5 years were excluded due to physiological variability in haematological indices, especially neutrophil counts, which could confound PNR calculations. Data Collection Demographic data included age and sex. Clinical and laboratory parameters included haemoglobin concentration, reticulocyte count, white blood cell (WBC) count, absolute neutrophil count (ANC), platelet count, and haemoglobin F (HbF) percentage. Additional variables included hydroxyurea intake and transfusion history, determined by clinical documentation and the presence of HbA on haemoglobin electrophoresis. Transcranial Doppler Ultrasound Classification was based on the time-averaged mean of the maximum (TAMM) velocity in the middle cerebral or internal carotid artery: normal (≤ 170 cm/s), conditional (170–199 cm/s), and abnormal (≥ 200 cm/s). PNR (Platelet-Neutrophil Ratio) was calculated by dividing the absolute platelet count by the absolute neutrophil count (ANC). Data Analysis All statistical analyses were conducted using Python 3.10.0. Continuous variables were summarised as medians and interquartile ranges (IQRs) or means with standard deviations, as appropriate. Categorical variables were summarised using frequencies and percentages. Comparisons between TCD categories and numerical variables were made using the Kruskal-Wallis test or ANOVA. Categorical variables were compared using the Chi-square or Fisher’s exact test as applicable. Univariate logistic regression analyses were performed to assess the association between each predictor and the likelihood of abnormal TCD findings. Multivariate logistic regression was then conducted to identify independent predictors of abnormal TCD results, adjusting for confounders such as age, gender, HbF, transfusion status, hydroxyurea use, and haemoglobin concentration. Receiver Operating Characteristic (ROC) curve analysis was employed to evaluate the discriminative ability of PNR, HbF, and neutrophil count (ANC) in predicting abnormal TCD results. The area under the curve (AUC) was compared across predictors to assess diagnostic performance. A p-value < 0.05 was considered statistically significant. Results Demographic and Clinical Overview A total of 248 children and young adults aged 5 to 25 years (median age: 12.0 years, IQR: 8.83–16.25) with HbSS genotype were included. Males constituted 48.8% (n = 121), and females 51.2% (n = 127). The median body mass index (BMI) was 17.35 kg/m² (IQR: 15.57–20.25). The median platelet count was 366.76 × 10⁹/L (IQR: 290.75–483.00), median absolute neutrophil count (ANC) was 4.59 × 10⁹/L (IQR: 3.29–6.32), and the median PNR was 80.90 (IQR: 55.77–123.38). HbF ranged from 0.00% to 43.80% with a median of 15.41% (IQR: 6.91–22.65). Eighty-nine percent of participants (n = 220) were on hydroxyurea, while 18.1% (n = 45) had a history of transfusion (Table 1 ). Table 1 Demographic and Clinical Characteristics of Children and Young Adults Variable Median (IQR)/Frequency Range/Percentage Age (years) 12.00 (8.83–16.25) 5.00–25.00 Age Category − 5 to 10 111 44.8 − 11 to 15 67 27.0 - Above 15 70 28.2 Gender - Male 121 48.8 - Female 127 51.2 Height (m) 142.07 (125.72-162.64) 95.00-190.00 Weight (kg) 34.90 (24.49–53.20) 13.80–95.50 BMI (kg/m 2 ) 17.35 (15.57–20.25) 13.10–36.70 WBC (x10⁹/L) 10.16 (7.71–12.42) 3.03–29.87 Haemoglobin (g/dL) 8.45 (7.70–9.30) 5.70–14.80 Retic Count (x10⁹/L) 253.50 (186.03-362.83) 6.77–791.30 Platelets (x10⁹/L) 366.76 (290.75–483.00) 34.00-1212.00 Neutrophils (ANC x10⁹/L) 4.59 (3.29–6.32) 1.14–22.10 HbF (%) 15.41 (6.91–22.65) 0.00-43.80 PNR (Platelet-Neutrophil Ratio) 80.90 (55.77-123.38) 7.90-301.90 Genotype - SS 248 100.0 Transfusion Status - Not transfused 203 81.9 - Transfused 45 18.1 Hydroxyurea Intake - Yes 220 88.7 - No 28 11.3 TCD Results - Normal 210 84.7 - Abnormal 24 9.7 - Conditional 14 5.6 TCD Classification and Associations Based on TCD results, 84.7% (n = 210) had normal velocities, 5.6% (n = 14) were conditional, and 9.7% (n = 24) were classified as abnormal. Children with abnormal TCD results were significantly younger (median age: 8.79 years), more likely to be in the 5–10 age category (100%), and predominantly female (70.8%) (p < 0.01 for all). Participants with abnormal TCD had significantly lower HbF levels (7.48% vs. 16.35%, p < 0.0001) and PNR (53.05 vs. 87.65, p < 0.0001) (Fig. 1 ), and higher ANC values (6.3 vs. 4.12, p < 0.0001). Transfusion history was significantly associated with abnormal TCD (50% vs. 15.2%, p = 0.0001). Hydroxyurea use was 100% among those with abnormal TCD (Table 2 ). Table 2 Distribution of Demographic and Haematologic Parameters Across Transcranial Doppler Velocity Categories Variable Normal Conditional Abnormal p-Value Age (years) 13.0 (9.0–17.0) 8.71 (6.92–12.37) 8.79 (7.69–9.33) < 0.0001 Age Category − 5 to 10 78 (37.1) 9 (64.3) 24 (100.0) − 11 to 15 64 (30.5) 3 (21.4) 0 (0.0) - Above 15 68 (32.4) 2 (14.3) 0 (0.0) 0.0001 Gender - Female 99 (47.1) 11 (78.6) 17 (70.8) - Male 111 (52.9) 3 (21.4) 7 (29.2) 0.01 BMI 17.55 (15.5–20.8) 17.1 (15.15–17.9) 17.1 (15.6-18.45) 0.302 WBC (x10⁹/L) 9.88 (7.28–12.18) 12.36 (10.39–13.92) 10.79 (10.24–12.8) 0.001 Haemoglobin (g/dL) 8.7 ± 1.3 7.9 ± 0.87 8.09 ± 0.42 0.001 Retic Count (x10⁹/L) 246.25 (174.92-343.15) 348.31 (272.57–426.1) 250.81 (212.1-406.71) 0.006 Platelets (x10⁹/L) 378.5 (283.25-497.25) 426.28 (276.09-474.93) 308.53 (304.22-349.04) 0.155 Neutrophils (ANC x10⁹/L) 4.12 (3.07–6.05) 4.96 (4.47–5.47) 6.3 (6.23–6.81) < 0.0001 HbF (%) 16.35 (8.1-23.87) 6.63 (6.02–12.94) 7.48 (3.7-15.29) < 0.0001 PNR (Platelet-Neutrophil Ratio) 87.65 (59.58–126.6) 82.35 (48.97-120.49) 53.05 (48.11–62.01) < 0.0001 Transfusion Status - Not transfused 178 (84.8) 13 (92.9) 12 (50.0) - Transfused 32 (15.2) 1 (7.1) 12 (50.0) 0.0001 Hydroxyurea Intake - No 24 (11.4) 4 (28.6) 0 (0.0) - Yes 186 (88.6) 10 (71.4) 24 (100.0) 0.027 Univariate and Multivariate Logistic Regression Univariate logistic regression showed that increasing age (OR = 0.759, 95% CI: 0.662–0.871, p < 0.001), male gender (OR = 0.367, 95% CI: 0.146–0.922, p = 0.033), higher haemoglobin (OR = 0.633, p = 0.023), higher platelet count (OR = 0.997, p = 0.035), higher HbF (OR = 0.906, p = 0.001), and higher PNR (OR = 0.961, p < 0.001) were all protective against abnormal TCD. Transfusion history (OR = 5.563, p < 0.001) and higher ANC (OR = 1.214, p = 0.010) increased the odds of abnormal TCD (Table 3 ). Table 3 Univariate Logistic Regression of TCD Categories (Normal: 0, Abnormal: 1) Variable Odds Ratio (OR) 95% Confidence Interval (CI) p-Value Age (years) 0.759 0.662–0.871 < 0.001** Gender - Male 0.367 0.146–0.922 0.033** BMI 0.887 0.770–1.021 0.095 WBC (x10⁹/L) 1.09 0.991–1.198 0.077 Haemoglobin (Hgb) 0.633 0.427–0.939 0.023** Platelets (PLT) 0.997 0.993–1.000 0.035** Reticulocytes 1.002 0.999–1.005 0.192 Neutrophils (ANC) 1.214 1.047–1.408 0.010** Transfusion Status - Transfused 5.563 2.297–13.468 < 0.001** HbF 0.906 0.855–0.959 0.001** PNR 0.961 0.942–0.981 < 0.001** **p-value < 0.05 In multivariate analysis adjusting for relevant variables, PNR remained a strong independent predictor (OR = 0.925, 95% CI: 0.885–0.967, p < 0.001), along with HbF (OR = 0.809, p = 0.0024). Age and gender also retained significance (p < 0.05), while transfusion status and haemoglobin were no longer statistically significant in the adjusted model (Table 4 ). Table 4 Predictive Value of PNR in Multivariate TCD Category Analysis (Normal: 0, Abnormal:1) Variable Odds Ratio (OR) 95% Confidence Interval (CI) p-Value Age (years) 0.561 0.424–0.742 < 0.001** Gender - Male 0.218 0.055–0.860 0.0296** BMI 1.011 0.975–1.049 0.5467 Transfusion Status - Transfused 0.452 0.066–3.104 0.4194 Haemoglobin (Hgb) 1.312 0.534–3.223 0.5532 HbF 0.809 0.706–0.928 0.0024** PNR 0.925 0.885–0.967 < 0.001** ROC Analysis PNR demonstrated superior predictive performance for abnormal TCD results with an AUC of 0.82, compared to HbF (AUC = 0.73) and ANC (AUC = 0.76), confirming its robustness as a screening biomarker (Fig. 2 ). Discussion This study evaluates the diagnostic utility of the Platelet-Neutrophil Ratio (PNR) in stratifying stroke risk in paediatric and young adult patients with sickle cell anaemia (SCA), using transcranial Doppler (TCD) as the reference standard. With the incorporation of haemoglobin F (HbF) levels and transfusion status, this updated analysis strengthens the predictive value of PNR, even after adjusting for these key modifiers. PNR retained its independent association with abnormal TCD outcomes in multivariate regression analysis (OR = 0.925, p < 0.001), even after adjusting for age, gender, HbF, haemoglobin concentration, hydroxyurea use, and transfusion status. In contrast, transfusion status and haemoglobin concentration lost statistical significance after adjustment. This finding reinforces the robustness of PNR as a predictive biomarker that is relatively unaffected by prior transfusion or baseline haemoglobin values. ROC analysis demonstrated that PNR had the highest diagnostic performance (AUC = 0.82), surpassing HbF (AUC = 0.73) and neutrophil count (AUC = 0.76). These results suggest that PNR not only reflects the underlying inflammatory state but may integrate the cumulative vascular risk more effectively than traditional haematologic indices. The biological plausibility for the association between low PNR and increased stroke risk in SCA lies in the interplay between inflammation, thrombosis, and endothelial dysfunction. Neutrophils play a crucial role in this inflammatory process by adhering to the endothelium and contributing to the formation of vaso-occlusive events.[ 9 – 11 ]Activated neutrophils release reactive oxygen species and proteolytic enzymes, exacerbating tissue damage and promoting further inflammation.[ 11 ] Similarly, platelets are known to interact with sickled red blood cells and endothelial cells, contributing to thrombus formation and vaso-occlusion.[ 12 ] Activated platelets release pro-inflammatory cytokines and chemokines, which further recruit neutrophils and amplify the inflammatory response.[ 13 , 14 ] The interaction between neutrophils and platelets can thus create a vicious cycle of inflammation and coagulation, increasing the risk of ischemic events such as stroke.[ 15 ] The ratio between these two components, as reflected by the PNR, may provide an indirect but dynamic measure of the pro-inflammatory/pro-thrombotic state. Our earlier study by Efobi et al. (2023) [ 8 ] reported an association between PNR and general disease severity in SCA. This study builds on that foundation and is the first, to our knowledge, to evaluate PNR in the specific context of stroke risk. The consistent significance of PNR across both univariate and multivariate models, and its superior AUC in ROC analysis, support its potential use as a screening tool, especially in resource-limited settings where access to TCD is restricted. The significant association between PNR and stroke risk underscores the potential of PNR as a practical and accessible tool for stroke risk assessment in SCD patients. TCD, while effective, is often inaccessible in many low- and middle-income countries due to cost and technical expertise requirements.[ 16 , 17 ] PNR, derived from routine blood tests, offers a feasible alternative that could be easily implemented in these settings, facilitating early identification and intervention for at-risk children and potentially reducing stroke incidence and associated morbidity. With the inclusion of new variables such as haemoglobin F (HbF) and transfusion history, our study has further validated PNR as a robust and independent predictor of stroke risk. Children with abnormal TCD values not only had significantly lower PNR but also lower HbF levels and higher likelihood of previous transfusion. These findings align with the pathophysiological understanding that low HbF and transfusion history are indicative of more severe disease.[ 18 , 19 ] Even after adjusting for these, PNR retained a strong inverse association with stroke risk (OR = 0.925, p < 0.001). The ROC curve analysis reaffirmed the superior performance of PNR (AUC = 0.82) compared to HbF (AUC = 0.73) and neutrophils (AUC = 0.76). This implies that PNR not only reflects inflammatory status but integrates risk better than traditional markers like HbF. This makes it an even more attractive option for resource-limited settings where access to sophisticated tests is scarce. To the best of our knowledge, this is the first study to evaluate the utility of PNR as a predictor of stroke risk in paediatric SCD patients. The novelty of this study lies in identifying a simple, cost-effective biomarker that can be integrated into routine clinical practice, particularly in resource-constrained settings. If validated through further research, PNR could serve as an evaluative tool for stroke risk assessment, aiding in the timely initiation of preventive measures such as hydroxyurea therapy or chronic blood transfusions, thereby improving patient outcomes. This study has several limitations. The retrospective design limits causal inferences, and the single-institution study population may affect generalizability. Future prospective, multi-centre studies are needed to validate these findings and explore the combined use of PNR with other markers. Conclusion This study demonstrates that the platelet-neutrophil ratio (PNR) remains a significant and independent predictor of abnormal transcranial Doppler (TCD) velocities in children and young adults with sickle cell anaemia. PNR outperformed traditional haematologic markers, including HbF and neutrophil count, in predicting abnormal TCD findings, as evidenced by its superior area under the curve (AUC) in receiver operating characteristic (ROC) analysis. The clinical implications of these findings are particularly relevant in resource-constrained settings where access to TCD and HbF testing may be limited. As PNR can be readily derived from routine complete blood counts, it represents a practical and cost-effective tool for early stroke risk stratification in this vulnerable population. Prospective, multi-centre studies are warranted to validate these findings further and explore the integration of PNR into standardised screening protocols for stroke prevention in sickle cell disease. Abbreviations SCA Sickle Cell Anaemia HbF Haemoglobin F PNR Platelet-to-Neutrophil Ratio TCD Transcranial Doppler ANC Absolute Neutrophil Count HbSS Haemoglobin SS genotype BMI Body Mass Index AUC Area Under the Curve OR Odds Ratio CI Confidence Interval Declarations Human Ethics and Consent to Participate Ethical approval for the study was obtained from the Institutional Review Board (IRB) of Children’s National Hospital, Washington, D.C., USA (IRB number: Pro00010744). As this was a retrospective chart review using de-identified data, individual patient consent was waived by the IRB in accordance with ethical regulations and the principles outlined in the Declaration of Helsinki. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. Ethical approval Ethical approval for the study was obtained from the institutional review board (IRB) at Children's National Hospital, Washington DC, United States of America. IRB number: Pro00010744 Consent to participate As this was a retrospective chart review using de-identified data, individual patient consent was waived by the IRB in accordance with ethical regulations and the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research received no external funding. All expenses related to data analysis and manuscript preparation were personally covered by the authors. Author Contribution C.A.N. conceived the study, conducted the literature review, performed the statistical analyses, and led manuscript preparation. C.C.E. contributed to methodology design and co-wrote the manuscript. D.D.H., A.N.B., and A.C. facilitated access to institutional data, provided clinical insights, and critically reviewed the manuscript. All authors read and approved the final version of the manuscript for submission. Acknowledgements The authors would like to express their gratitude to the staff of Children’s National Hospital for their invaluable support during this study. We also thank the patients and their families for their participation. Availability of Data and Materials The dataset analysed during this study is not publicly available due to institutional policies on data sharing, but it may be made available from the corresponding author on reasonable request and with permission from Children’s National Hospital. Data availability The dataset analysed during this study is not publicly available due to institutional policies on data sharing, but it may be made available from the corresponding author on reasonable request and with permission from Children’s National Hospital. References Thomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V et al Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000–2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Haematol [Internet] 2023 [cited 2023 Jun 20];0. Available from: http://www.thelancet.com/article/S2352302623001187/fulltext Makani J, Williams TN, Marsh K Sickle cell disease in Africa: burden and research priorities. Ann Trop Med Parasitol [Internet] 2007 [cited 2023 Jul 13];101:3–14. Available from: https://pubmed.ncbi.nlm.nih.gov/17244405/ Powars D, Wilson B, Imbus C, Pegelow C, Allen J The natural history of stroke in sickle cell disease. Am J Med [Internet] 1978 [cited 2023 May 18];65:461–71. Available from: https://pubmed.ncbi.nlm.nih.gov/717414/ Mburu J, Odame I Sickle cell disease: Reducing the global disease burden. Int J Lab Hematol [Internet] 2019 [cited 2023 Jun 20];41 Suppl 1:82–8. Available from: https://pubmed.ncbi.nlm.nih.gov/31069977/ Ohene-Frempong K, Weiner SJ, Sleeper LA, Miller ST, Embury S, Moohr JW et al (1998) Cerebrovascular accidents in sickle cell disease: Rates and risk factors. Blood 91:288–294 Adams R, McKie V, Nichols F, Carl E, Zhang DL, McKie K et al The use of transcranial ultrasonography to predict stroke in sickle cell disease. N Engl J Med [Internet] 1992 [cited 2024 Jan 23];326:605–10. Available from: https://pubmed.ncbi.nlm.nih.gov/1734251/ Modebe E, Nonyelu C, Duru A, Ezenwosu O, Chukwu B, Madu A et al Cerebral artery conditional blood velocity in sickle cell disease: a multicentre study and evidence for active treatment. Arch Dis Child [Internet] 2023 [cited 2024 Jan 23];108:440–4. Available from: https://pubmed.ncbi.nlm.nih.gov/36737235/ Efobi CC, Nri-Ezedi CA, Madu CS, Ikediashi CC, Ejiofor O, Ofiaeli CI, Neutrophil-Lymphocyte Platelet-Neutrophil, and PlateletLymphocyte Ratios as Indicators of Sickle Cell Anaemia Severity. Ethiop J Health Sci [Internet] 2023 [cited 2024 Jan 23];33:821–30. Available from: https://www.ajol.info/index.php/ejhs/article/view/255812 Chen G, Zhang D, Fuchs TA, Manwani D, Wagner DD, Frenette PS (2014) Heme-induced neutrophil extracellular traps contribute to the pathogenesis of sickle cell disease. Blood [Internet]. [cited 2024 May 25];123:3818–27. Available from: https://pubmed.ncbi.nlm.nih.gov/24620350/ Lum AFH, Wun T, Staunton D, Simon SI Inflammatory potential of neutrophils detected in sickle cell disease. Am J Hematol [Internet] 2004 [cited 2023 May 22];76:126–33. Available from: https://pubmed.ncbi.nlm.nih.gov/15164377/ Zhang D, Xu C, Manwani D, Frenette PS (2016) Neutrophils, platelets, and inflammatory pathways at the nexus of sickle cell disease pathophysiology. Blood [Internet]. [cited 2024 May 25];127:801–9. Available from: https://pubmed.ncbi.nlm.nih.gov/26758915/ Westerman MP, Green D, Gilman-Sachs A, Beaman K, Freels S, Boggio L et al Antiphospholipid antibodies, proteins C and S, and coagulation changes in sickle cell disease. J Lab Clin Med [Internet] 1999 [cited 2024 May 25];134:352–62. Available from: https://pubmed.ncbi.nlm.nih.gov/10521081/ Proenca̧-Ferreira R, Brugnerotto AF, Garrido VT, Dominical VM, Vital DM, Ribeiro MFR et al Endothelial Activation by Platelets from Sickle Cell Anemia Patients. PLoS One [Internet] 2014 [cited 2024 May 25];9:e89012. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089012 Dominical VM, Samse L, Nichols JS, Costa FF, McCoy JP, Conran N et al (2014) Prominent role of platelets in the formation of circulating neutrophil-red cell heterocellular aggregates in sickle cell anemia. Haematologica 99:e214–e217 Polanowska-Grabowska R, Wallace K, Field JJ, Chen L, Marshall MA, Figler R et al (2010) P-selectin-mediated platelet-neutrophil aggregate formation activates neutrophils in mouse and human sickle cell disease. Arterioscler Thromb Vasc Biol 30:2392–2399 Grosse SD, Odame I, Atrash HK, Amendah DD, Piel FB, Williams TN (2011) Sickle cell disease in Africa: A neglected cause of early childhood mortality. Am J Prev Med;41 Osei MA, McGann PT Sickle cell disease: time to act on the most neglected global health problem. Lancet Haematol [Internet] 2023 [cited 2023 Jun 20];0. Available from: http://www.thelancet.com/article/S2352302623001692/fulltext Ware RE, Helms RW Stroke With Transfusions Changing to Hydroxyurea (SWiTCH). Blood [Internet] 2012 [cited 2023 May 18];119:3925–32. Available from: https://pubmed.ncbi.nlm.nih.gov/22318199/ Adams RJ (2000) Lessons from the stroke prevention trial in sickle cell anemia (STOP) study. J Child Neurol 15:344–349 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Annals of Hematology → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7557167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515422060,"identity":"79ba0ed6-bada-4f41-809d-dfe9c0c37a43","order_by":0,"name":"Chisom Adaobi Nri-Ezedi","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Chisom","middleName":"Adaobi","lastName":"Nri-Ezedi","suffix":""},{"id":515422062,"identity":"23b310ed-ba06-4eb2-8df7-90a9fd144359","order_by":1,"name":"Chilota Chibuife Efobi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3QsQrCMBCA4SsBXSJZI4rPcFBw9VUiBacqHTsWBLuIsz6Gb3Chax+gg4MgODnoVsTBq5tgS90E8w/JLV+bBMDl+tViIKl4J0DDm2lBcib95FsCSK+xBVEpeUeKD0O/mFtbRjNQ3RBFdK8nOjcCKT/LcbEwpDGE/vqCYrepJ6ihM7itMiYhEmIMyIPorRtJt7RM/C0Tw2TSgnSgIqiZEB+sGoQsm+4yXerqLjq/oE1wxsM5ynpJPVFpZq/8YhOVhv6tfAQjlQb7k3zUEwDv/YOyWshbNZHPNf7F5XK5/qwn6aNUIrdAIl0AAAAASUVORK5CYII=","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":true,"prefix":"","firstName":"Chilota","middleName":"Chibuife","lastName":"Efobi","suffix":""},{"id":515422063,"identity":"11eb8ece-68a7-4b9c-9758-70d8130db459","order_by":2,"name":"Habib Darbari Deepika","email":"","orcid":"","institution":"Children’s National Hospital, George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Habib","middleName":"Darbari","lastName":"Deepika","suffix":""},{"id":515422064,"identity":"1ea4fe31-968f-4306-bef5-0fa46411b947","order_by":3,"name":"Nana-Bilkisu Aduni","email":"","orcid":"","institution":"Children’s National Hospital, George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Nana-Bilkisu","middleName":"","lastName":"Aduni","suffix":""},{"id":515422065,"identity":"912bf74e-38da-4019-85c0-c6835a4e790b","order_by":4,"name":"Andrew Drew Campbell","email":"","orcid":"","institution":"Children’s National Hospital, George Washington University Washington DC","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"Drew","lastName":"Campbell","suffix":""}],"badges":[],"createdAt":"2025-09-07 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06:52:12","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92421,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7557167/v1/a6164f8930ee1c4a85c446fb.html"},{"id":91953658,"identity":"d99f6d62-1fc0-4eb9-a5a8-fd35a79a4bb1","added_by":"auto","created_at":"2025-09-23 07:00:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125173,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Platelet-Neutrophil Ratio Across Transcranial Doppler Velocity Categories in Children and Young Adults with Sickle Cell Anaemia\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7557167/v1/af846ed88ec8abb01ef9d744.png"},{"id":91952558,"identity":"be3af9bb-97e3-49dc-aa9b-e3b9e71dbc73","added_by":"auto","created_at":"2025-09-23 06:52:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32973,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic Curves Comparing the Predictive Performance of PNR, Haemoglobin F, and Absolute Neutrophil Count for Abnormal Transcranial Doppler Velocities\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7557167/v1/b16fa294c45fd323c660858d.png"},{"id":102234313,"identity":"69a1f586-3aa6-4a7e-9322-8c76a108fcbe","added_by":"auto","created_at":"2026-02-09 16:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1068150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7557167/v1/24929506-aecb-4ee4-8aeb-3d2ef70ee0bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Platelet-Neutrophil Ratio as a Potential Biomarker for Stroke Risk Stratification in Children and Young Adults with Sickle Cell Anaemia in Resource Poor Settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSickle cell disease (SCD) is a hereditary disorder characterized by the production of abnormal haemoglobin, leading to the deformation of red blood cells into a sickle shape. It is one of the most common genetic disorders globally, disproportionately affecting individuals of African, Mediterranean, and Middle Eastern descent [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. SCD is notorious for inducing vaso-occlusive events, which can result in severe complications, including stroke [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Stroke is a particularly alarming complication in SCD, often leading to long-term disability or death [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Remarkably, children with SCD face a stroke risk over 200 times higher than their non-SCD counterparts, making early identification and preventive measures crucial [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTranscranial Doppler ultrasound (TCD), remains the gold standard for stroke risk stratification, as it can identify children with elevated cerebral blood flow velocities who are at risk for primary stroke events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the accessibility of TCD is limited in many parts of the world due to the high cost of equipment, maintenance, and the scarcity of skilled neurosonologists [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].The cost and infrastructure challenges underscore the urgent need for accessible, scalable biomarkers that can augment or substitute TCD in resource-limited environments.\u003c/p\u003e\u003cp\u003eThe platelet-neutrophil ratio (PNR), derived from complete blood counts, has emerged as a promising biomarker [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] has garnered attention as a potential marker for inflammatory and thrombotic states in various conditions. In our preliminary analysis, PNR showed promise as a biomarker for stroke risk in children with SCA. However, that initial study was limited by the unavailability of haemoglobin F (HbF) levels and transfusion status\u0026mdash;two clinically relevant variables that have been shown to influence disease severity and cerebral vasculopathy. HbF inhibits HbS polymerisation and reduces disease complications, while chronic transfusions alter haematological profiles and influence stroke risk.\u003c/p\u003e\u003cp\u003eIn this revised analysis, we re-evaluated the utility of PNR in stroke risk stratification by incorporating HbF levels and transfusion status into the analysis. Our goal was to assess whether PNR retains its predictive strength in the presence of these key modifiers, thus validating its potential as a cost-effective, accessible tool for early identification of children at high risk of stroke in resource-poor settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis was a retrospective observational study conducted using data from the electronic medical records of Children's National Hospital, Washington DC. The data spans from September 1, 2013, to September 14, 2023, and includes consenting patients with Sickle Cell Disease (SCD) who participated in the IRB-approved Natural History Study of Sickle Cell Disease at Children's National Hospital.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eWe included patients aged 5 to 25 years with a confirmed diagnosis of sickle cell anaemia (HbSS genotype) who had undergone transcranial Doppler (TCD) ultrasonography and routine haematological testing within the study period.\u003c/p\u003e\u003cp\u003ePatients were excluded if they had a history of prior stroke, splenectomy, bone marrow transplantation, or incomplete clinical records. Children under 5 years were excluded due to physiological variability in haematological indices, especially neutrophil counts, which could confound PNR calculations.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic data included age and sex. Clinical and laboratory parameters included haemoglobin concentration, reticulocyte count, white blood cell (WBC) count, absolute neutrophil count (ANC), platelet count, and haemoglobin F (HbF) percentage. Additional variables included hydroxyurea intake and transfusion history, determined by clinical documentation and the presence of HbA on haemoglobin electrophoresis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscranial Doppler Ultrasound Classification\u003c/b\u003e was based on the time-averaged mean of the maximum (TAMM) velocity in the middle cerebral or internal carotid artery: normal (\u0026le;\u0026thinsp;170 cm/s), conditional (170\u0026ndash;199 cm/s), and abnormal (\u0026ge;\u0026thinsp;200 cm/s).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePNR (Platelet-Neutrophil Ratio)\u003c/b\u003e was calculated by dividing the absolute platelet count by the absolute neutrophil count (ANC).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using Python 3.10.0. Continuous variables were summarised as medians and interquartile ranges (IQRs) or means with standard deviations, as appropriate. Categorical variables were summarised using frequencies and percentages. Comparisons between TCD categories and numerical variables were made using the Kruskal-Wallis test or ANOVA. Categorical variables were compared using the Chi-square or Fisher\u0026rsquo;s exact test as applicable. Univariate logistic regression analyses were performed to assess the association between each predictor and the likelihood of abnormal TCD findings. Multivariate logistic regression was then conducted to identify independent predictors of abnormal TCD results, adjusting for confounders such as age, gender, HbF, transfusion status, hydroxyurea use, and haemoglobin concentration. Receiver Operating Characteristic (ROC) curve analysis was employed to evaluate the discriminative ability of PNR, HbF, and neutrophil count (ANC) in predicting abnormal TCD results. The area under the curve (AUC) was compared across predictors to assess diagnostic performance. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Clinical Overview\u003c/h2\u003e\u003cp\u003eA total of 248 children and young adults aged 5 to 25 years (median age: 12.0 years, IQR: 8.83\u0026ndash;16.25) with HbSS genotype were included. Males constituted 48.8% (n\u0026thinsp;=\u0026thinsp;121), and females 51.2% (n\u0026thinsp;=\u0026thinsp;127). The median body mass index (BMI) was 17.35 kg/m\u0026sup2; (IQR: 15.57\u0026ndash;20.25).\u003c/p\u003e\u003cp\u003eThe median platelet count was 366.76 \u0026times; 10⁹/L (IQR: 290.75\u0026ndash;483.00), median absolute neutrophil count (ANC) was 4.59 \u0026times; 10⁹/L (IQR: 3.29\u0026ndash;6.32), and the median PNR was 80.90 (IQR: 55.77\u0026ndash;123.38). HbF ranged from 0.00% to 43.80% with a median of 15.41% (IQR: 6.91\u0026ndash;22.65).\u003c/p\u003e\u003cp\u003eEighty-nine percent of participants (n\u0026thinsp;=\u0026thinsp;220) were on hydroxyurea, while 18.1% (n\u0026thinsp;=\u0026thinsp;45) had a history of transfusion (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Clinical Characteristics of Children and Young Adults\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)/Frequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange/Percentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.00 (8.83\u0026ndash;16.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.00\u0026ndash;25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;5 to 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;11 to 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Above 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142.07 (125.72-162.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.00-190.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.90 (24.49\u0026ndash;53.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.80\u0026ndash;95.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.35 (15.57\u0026ndash;20.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.10\u0026ndash;36.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.16 (7.71\u0026ndash;12.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.03\u0026ndash;29.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.45 (7.70\u0026ndash;9.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.70\u0026ndash;14.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetic Count (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253.50 (186.03-362.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.77\u0026ndash;791.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e366.76 (290.75\u0026ndash;483.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.00-1212.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (ANC x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.59 (3.29\u0026ndash;6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14\u0026ndash;22.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.41 (6.91\u0026ndash;22.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00-43.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNR (Platelet-Neutrophil Ratio)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.90 (55.77-123.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.90-301.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- SS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Not transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydroxyurea Intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTCD Results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Abnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Conditional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTCD Classification and Associations\u003c/h3\u003e\n\u003cp\u003eBased on TCD results, 84.7% (n\u0026thinsp;=\u0026thinsp;210) had normal velocities, 5.6% (n\u0026thinsp;=\u0026thinsp;14) were conditional, and 9.7% (n\u0026thinsp;=\u0026thinsp;24) were classified as abnormal. Children with abnormal TCD results were significantly younger (median age: 8.79 years), more likely to be in the 5\u0026ndash;10 age category (100%), and predominantly female (70.8%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all).\u003c/p\u003e\u003cp\u003eParticipants with abnormal TCD had significantly lower HbF levels (7.48% vs. 16.35%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and PNR (53.05 vs. 87.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and higher ANC values (6.3 vs. 4.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Transfusion history was significantly associated with abnormal TCD (50% vs. 15.2%, p\u0026thinsp;=\u0026thinsp;0.0001). Hydroxyurea use was 100% among those with abnormal TCD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Demographic and Haematologic Parameters Across Transcranial Doppler Velocity Categories\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConditional\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.0 (9.0\u0026ndash;17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.71 (6.92\u0026ndash;12.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.79 (7.69\u0026ndash;9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;5 to 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;11 to 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Above 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99 (47.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17 (70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.55 (15.5\u0026ndash;20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.1 (15.15\u0026ndash;17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.1 (15.6-18.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.88 (7.28\u0026ndash;12.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.36 (10.39\u0026ndash;13.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.79 (10.24\u0026ndash;12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetic Count (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e246.25 (174.92-343.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e348.31 (272.57\u0026ndash;426.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e250.81 (212.1-406.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e378.5 (283.25-497.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426.28 (276.09-474.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e308.53 (304.22-349.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (ANC x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.12 (3.07\u0026ndash;6.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.96 (4.47\u0026ndash;5.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.3 (6.23\u0026ndash;6.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.35 (8.1-23.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.63 (6.02\u0026ndash;12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.48 (3.7-15.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNR (Platelet-Neutrophil Ratio)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87.65 (59.58\u0026ndash;126.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.35 (48.97-120.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.05 (48.11\u0026ndash;62.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Not transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e178 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydroxyurea Intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e186 (88.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eUnivariate and Multivariate Logistic Regression\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression showed that increasing age (OR\u0026thinsp;=\u0026thinsp;0.759, 95% CI: 0.662\u0026ndash;0.871, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male gender (OR\u0026thinsp;=\u0026thinsp;0.367, 95% CI: 0.146\u0026ndash;0.922, p\u0026thinsp;=\u0026thinsp;0.033), higher haemoglobin (OR\u0026thinsp;=\u0026thinsp;0.633, p\u0026thinsp;=\u0026thinsp;0.023), higher platelet count (OR\u0026thinsp;=\u0026thinsp;0.997, p\u0026thinsp;=\u0026thinsp;0.035), higher HbF (OR\u0026thinsp;=\u0026thinsp;0.906, p\u0026thinsp;=\u0026thinsp;0.001), and higher PNR (OR\u0026thinsp;=\u0026thinsp;0.961, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all protective against abnormal TCD. Transfusion history (OR\u0026thinsp;=\u0026thinsp;5.563, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher ANC (OR\u0026thinsp;=\u0026thinsp;1.214, p\u0026thinsp;=\u0026thinsp;0.010) increased the odds of abnormal TCD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate Logistic Regression of TCD Categories (Normal: 0, Abnormal: 1)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.662\u0026ndash;0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.146\u0026ndash;0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.770\u0026ndash;1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (x10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.991\u0026ndash;1.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaemoglobin (Hgb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.427\u0026ndash;0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (PLT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.993\u0026ndash;1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReticulocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u0026ndash;1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (ANC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.047\u0026ndash;1.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.297\u0026ndash;13.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.855\u0026ndash;0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.942\u0026ndash;0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e**p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn multivariate analysis adjusting for relevant variables, PNR remained a strong independent predictor (OR\u0026thinsp;=\u0026thinsp;0.925, 95% CI: 0.885\u0026ndash;0.967, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with HbF (OR\u0026thinsp;=\u0026thinsp;0.809, p\u0026thinsp;=\u0026thinsp;0.0024). Age and gender also retained significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while transfusion status and haemoglobin were no longer statistically significant in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictive Value of PNR in Multivariate TCD Category Analysis (Normal: 0, Abnormal:1)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.424\u0026ndash;0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u0026ndash;0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0296**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.975\u0026ndash;1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Transfused\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u0026ndash;3.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaemoglobin (Hgb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.534\u0026ndash;3.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.706\u0026ndash;0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0024**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.885\u0026ndash;0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eROC Analysis\u003c/h2\u003e\u003cp\u003ePNR demonstrated superior predictive performance for abnormal TCD results with an AUC of 0.82, compared to HbF (AUC\u0026thinsp;=\u0026thinsp;0.73) and ANC (AUC\u0026thinsp;=\u0026thinsp;0.76), confirming its robustness as a screening biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluates the diagnostic utility of the Platelet-Neutrophil Ratio (PNR) in stratifying stroke risk in paediatric and young adult patients with sickle cell anaemia (SCA), using transcranial Doppler (TCD) as the reference standard. With the incorporation of haemoglobin F (HbF) levels and transfusion status, this updated analysis strengthens the predictive value of PNR, even after adjusting for these key modifiers.\u003c/p\u003e\u003cp\u003ePNR retained its independent association with abnormal TCD outcomes in multivariate regression analysis (OR\u0026thinsp;=\u0026thinsp;0.925, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), even after adjusting for age, gender, HbF, haemoglobin concentration, hydroxyurea use, and transfusion status. In contrast, transfusion status and haemoglobin concentration lost statistical significance after adjustment. This finding reinforces the robustness of PNR as a predictive biomarker that is relatively unaffected by prior transfusion or baseline haemoglobin values.\u003c/p\u003e\u003cp\u003eROC analysis demonstrated that PNR had the highest diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.82), surpassing HbF (AUC\u0026thinsp;=\u0026thinsp;0.73) and neutrophil count (AUC\u0026thinsp;=\u0026thinsp;0.76). These results suggest that PNR not only reflects the underlying inflammatory state but may integrate the cumulative vascular risk more effectively than traditional haematologic indices.\u003c/p\u003e\u003cp\u003eThe biological plausibility for the association between low PNR and increased stroke risk in SCA lies in the interplay between inflammation, thrombosis, and endothelial dysfunction. Neutrophils play a crucial role in this inflammatory process by adhering to the endothelium and contributing to the formation of vaso-occlusive events.[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]Activated neutrophils release reactive oxygen species and proteolytic enzymes, exacerbating tissue damage and promoting further inflammation.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSimilarly, platelets are known to interact with sickled red blood cells and endothelial cells, contributing to thrombus formation and vaso-occlusion.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Activated platelets release pro-inflammatory cytokines and chemokines, which further recruit neutrophils and amplify the inflammatory response.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] The interaction between neutrophils and platelets can thus create a vicious cycle of inflammation and coagulation, increasing the risk of ischemic events such as stroke.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The ratio between these two components, as reflected by the PNR, may provide an indirect but dynamic measure of the pro-inflammatory/pro-thrombotic state.\u003c/p\u003e\u003cp\u003eOur earlier study by Efobi et al. (2023) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported an association between PNR and general disease severity in SCA. This study builds on that foundation and is the first, to our knowledge, to evaluate PNR in the specific context of stroke risk. The consistent significance of PNR across both univariate and multivariate models, and its superior AUC in ROC analysis, support its potential use as a screening tool, especially in resource-limited settings where access to TCD is restricted.\u003c/p\u003e\u003cp\u003eThe significant association between PNR and stroke risk underscores the potential of PNR as a practical and accessible tool for stroke risk assessment in SCD patients. TCD, while effective, is often inaccessible in many low- and middle-income countries due to cost and technical expertise requirements.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] PNR, derived from routine blood tests, offers a feasible alternative that could be easily implemented in these settings, facilitating early identification and intervention for at-risk children and potentially reducing stroke incidence and associated morbidity.\u003c/p\u003e\u003cp\u003eWith the inclusion of new variables such as haemoglobin F (HbF) and transfusion history, our study has further validated PNR as a robust and independent predictor of stroke risk. Children with abnormal TCD values not only had significantly lower PNR but also lower HbF levels and higher likelihood of previous transfusion. These findings align with the pathophysiological understanding that low HbF and transfusion history are indicative of more severe disease.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Even after adjusting for these, PNR retained a strong inverse association with stroke risk (OR\u0026thinsp;=\u0026thinsp;0.925, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ROC curve analysis reaffirmed the superior performance of PNR (AUC\u0026thinsp;=\u0026thinsp;0.82) compared to HbF (AUC\u0026thinsp;=\u0026thinsp;0.73) and neutrophils (AUC\u0026thinsp;=\u0026thinsp;0.76). This implies that PNR not only reflects inflammatory status but integrates risk better than traditional markers like HbF. This makes it an even more attractive option for resource-limited settings where access to sophisticated tests is scarce.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, this is the first study to evaluate the utility of PNR as a predictor of stroke risk in paediatric SCD patients. The novelty of this study lies in identifying a simple, cost-effective biomarker that can be integrated into routine clinical practice, particularly in resource-constrained settings. If validated through further research, PNR could serve as an evaluative tool for stroke risk assessment, aiding in the timely initiation of preventive measures such as hydroxyurea therapy or chronic blood transfusions, thereby improving patient outcomes.\u003c/p\u003e\u003cp\u003eThis study has several limitations. The retrospective design limits causal inferences, and the single-institution study population may affect generalizability. Future prospective, multi-centre studies are needed to validate these findings and explore the combined use of PNR with other markers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that the platelet-neutrophil ratio (PNR) remains a significant and independent predictor of abnormal transcranial Doppler (TCD) velocities in children and young adults with sickle cell anaemia. PNR outperformed traditional haematologic markers, including HbF and neutrophil count, in predicting abnormal TCD findings, as evidenced by its superior area under the curve (AUC) in receiver operating characteristic (ROC) analysis.\u003c/p\u003e\u003cp\u003eThe clinical implications of these findings are particularly relevant in resource-constrained settings where access to TCD and HbF testing may be limited. As PNR can be readily derived from routine complete blood counts, it represents a practical and cost-effective tool for early stroke risk stratification in this vulnerable population.\u003c/p\u003e\u003cp\u003eProspective, multi-centre studies are warranted to validate these findings further and explore the integration of PNR into standardised screening protocols for stroke prevention in sickle cell disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSickle Cell Anaemia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHbF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHaemoglobin F\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePNR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-to-Neutrophil Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscranial Doppler\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbsolute Neutrophil Count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHbSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHaemoglobin SS genotype\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eHuman Ethics and Consent to Participate\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eEthical approval for the study was obtained from the Institutional Review Board (IRB) of Children\u0026rsquo;s National Hospital, Washington, D.C., USA (IRB number: Pro00010744). As this was a retrospective chart review using de-identified data, individual patient consent was waived by the IRB in accordance with ethical regulations and the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003e Ethical approval for the study was obtained from the institutional review board (IRB) at Children's National Hospital, Washington DC, United States of America. IRB number: Pro00010744\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003eAs this was a retrospective chart review using de-identified data, individual patient consent was waived by the IRB in accordance with ethical regulations and the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no external funding. All expenses related to data analysis and manuscript preparation were personally covered by the authors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.A.N. conceived the study, conducted the literature review, performed the statistical analyses, and led manuscript preparation. C.C.E. contributed to methodology design and co-wrote the manuscript. D.D.H., A.N.B., and A.C. facilitated access to institutional data, provided clinical insights, and critically reviewed the manuscript. All authors read and approved the final version of the manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to express their gratitude to the staff of Children\u0026rsquo;s National Hospital for their invaluable support during this study. We also thank the patients and their families for their participation.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\u003cp\u003eThe dataset analysed during this study is not publicly available due to institutional policies on data sharing, but it may be made available from the corresponding author on reasonable request and with permission from Children\u0026rsquo;s National Hospital.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe dataset analysed during this study is not publicly available due to institutional policies on data sharing, but it may be made available from the corresponding author on reasonable request and with permission from Children\u0026rsquo;s National Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V et al Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000\u0026ndash;2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Haematol [Internet] 2023 [cited 2023 Jun 20];0. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thelancet.com/article/S2352302623001187/fulltext\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com/article/S2352302623001187/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMakani J, Williams TN, Marsh K Sickle cell disease in Africa: burden and research priorities. Ann Trop Med Parasitol [Internet] 2007 [cited 2023 Jul 13];101:3\u0026ndash;14. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/17244405/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/17244405/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowars D, Wilson B, Imbus C, Pegelow C, Allen J The natural history of stroke in sickle cell disease. Am J Med [Internet] 1978 [cited 2023 May 18];65:461\u0026ndash;71. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/717414/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/717414/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMburu J, Odame I Sickle cell disease: Reducing the global disease burden. Int J Lab Hematol [Internet] 2019 [cited 2023 Jun 20];41 Suppl 1:82\u0026ndash;8. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/31069977/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/31069977/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhene-Frempong K, Weiner SJ, Sleeper LA, Miller ST, Embury S, Moohr JW et al (1998) Cerebrovascular accidents in sickle cell disease: Rates and risk factors. Blood 91:288\u0026ndash;294\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams R, McKie V, Nichols F, Carl E, Zhang DL, McKie K et al The use of transcranial ultrasonography to predict stroke in sickle cell disease. N Engl J Med [Internet] 1992 [cited 2024 Jan 23];326:605\u0026ndash;10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/1734251/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/1734251/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eModebe E, Nonyelu C, Duru A, Ezenwosu O, Chukwu B, Madu A et al Cerebral artery conditional blood velocity in sickle cell disease: a multicentre study and evidence for active treatment. Arch Dis Child [Internet] 2023 [cited 2024 Jan 23];108:440\u0026ndash;4. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/36737235/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/36737235/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEfobi CC, Nri-Ezedi CA, Madu CS, Ikediashi CC, Ejiofor O, Ofiaeli CI, Neutrophil-Lymphocyte Platelet-Neutrophil, and PlateletLymphocyte Ratios as Indicators of Sickle Cell Anaemia Severity. Ethiop J Health Sci [Internet] 2023 [cited 2024 Jan 23];33:821\u0026ndash;30. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ajol.info/index.php/ejhs/article/view/255812\u003c/span\u003e\u003cspan address=\"https://www.ajol.info/index.php/ejhs/article/view/255812\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen G, Zhang D, Fuchs TA, Manwani D, Wagner DD, Frenette PS (2014) Heme-induced neutrophil extracellular traps contribute to the pathogenesis of sickle cell disease. Blood [Internet]. [cited 2024 May 25];123:3818\u0026ndash;27. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24620350/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/24620350/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLum AFH, Wun T, Staunton D, Simon SI Inflammatory potential of neutrophils detected in sickle cell disease. Am J Hematol [Internet] 2004 [cited 2023 May 22];76:126\u0026ndash;33. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/15164377/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/15164377/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang D, Xu C, Manwani D, Frenette PS (2016) Neutrophils, platelets, and inflammatory pathways at the nexus of sickle cell disease pathophysiology. Blood [Internet]. [cited 2024 May 25];127:801\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/26758915/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/26758915/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWesterman MP, Green D, Gilman-Sachs A, Beaman K, Freels S, Boggio L et al Antiphospholipid antibodies, proteins C and S, and coagulation changes in sickle cell disease. J Lab Clin Med [Internet] 1999 [cited 2024 May 25];134:352\u0026ndash;62. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/10521081/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/10521081/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eProenca̧-Ferreira R, Brugnerotto AF, Garrido VT, Dominical VM, Vital DM, Ribeiro MFR et al Endothelial Activation by Platelets from Sickle Cell Anemia Patients. PLoS One [Internet] 2014 [cited 2024 May 25];9:e89012. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089012\u003c/span\u003e\u003cspan address=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089012\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDominical VM, Samse L, Nichols JS, Costa FF, McCoy JP, Conran N et al (2014) Prominent role of platelets in the formation of circulating neutrophil-red cell heterocellular aggregates in sickle cell anemia. Haematologica 99:e214\u0026ndash;e217\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolanowska-Grabowska R, Wallace K, Field JJ, Chen L, Marshall MA, Figler R et al (2010) P-selectin-mediated platelet-neutrophil aggregate formation activates neutrophils in mouse and human sickle cell disease. Arterioscler Thromb Vasc Biol 30:2392\u0026ndash;2399\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrosse SD, Odame I, Atrash HK, Amendah DD, Piel FB, Williams TN (2011) Sickle cell disease in Africa: A neglected cause of early childhood mortality. Am J Prev Med;41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsei MA, McGann PT Sickle cell disease: time to act on the most neglected global health problem. Lancet Haematol [Internet] 2023 [cited 2023 Jun 20];0. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thelancet.com/article/S2352302623001692/fulltext\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com/article/S2352302623001692/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWare RE, Helms RW Stroke With Transfusions Changing to Hydroxyurea (SWiTCH). Blood [Internet] 2012 [cited 2023 May 18];119:3925\u0026ndash;32. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/22318199/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/22318199/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams RJ (2000) Lessons from the stroke prevention trial in sickle cell anemia (STOP) study. J Child Neurol 15:344\u0026ndash;349\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sickle Cell Disease, Platelet-Neutrophil Ratio, Transcranial Doppler, HbF, Stroke","lastPublishedDoi":"10.21203/rs.3.rs-7557167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7557167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSickle cell anaemia (SCA) is associated with a significantly increased risk of stroke, primarily assessed using transcranial Doppler ultrasound (TCD). However, access to TCD and haemoglobin F (HbF) testing remains limited in resource-constrained settings. Our preliminary findings identified the platelet-neutrophil ratio (PNR) as a promising biomarker for stroke risk stratification. This updated study evaluates the predictive value of PNR while adjusting for HbF levels and transfusion history.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective study of 248 HbSS children and young adults at Children\u0026rsquo;s National Hospital, Washington DC. Demographic, clinical, and hematologic parameters\u0026mdash;including PNR, HbF levels, and transfusion status\u0026mdash;were analysed in relation to TCD results. Logistic regression and ROC curve analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSubjects with abnormal TCD values had significantly lower PNR (53.05 vs. 87.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HbF (7.48% vs. 16.35%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with normal TCD results. Transfused children were more likely to have abnormal TCD findings (OR\u0026thinsp;=\u0026thinsp;5.56, 95% CI: 2.30\u0026ndash;13.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis confirmed PNR as a significant independent predictor of abnormal TCD (OR\u0026thinsp;=\u0026thinsp;0.925, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis showed superior predictive performance for PNR (AUC\u0026thinsp;=\u0026thinsp;0.82) compared to HbF (AUC\u0026thinsp;=\u0026thinsp;0.73) and ANC (AUC\u0026thinsp;=\u0026thinsp;0.76).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePNR remains a cost-effective and accessible biomarker for stroke risk stratification, retaining its predictive strength even after adjusting for HbF and transfusion status.\u003c/p\u003e","manuscriptTitle":"Platelet-Neutrophil Ratio as a Potential Biomarker for Stroke Risk Stratification in Children and Young Adults with Sickle Cell Anaemia in Resource Poor Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 06:52:07","doi":"10.21203/rs.3.rs-7557167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T16:54:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T16:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306856650006702270136171757151297235671","date":"2025-11-03T15:08:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T14:47:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T08:19:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T08:19:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2025-09-07T14:59:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"93a2610e-eb41-460a-802a-201855504505","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-7557167","link":"https://doi.org/10.1007/s00277-026-06859-8","journal":{"identity":"annals-of-hematology","isVorOnly":false,"title":"Annals of Hematology"},"publishedOn":"2026-02-06 15:59:42","publishedOnDateReadable":"February 6th, 2026"},"versionCreatedAt":"2025-09-23 06:52:07","video":"","vorDoi":"10.1007/s00277-026-06859-8","vorDoiUrl":"https://doi.org/10.1007/s00277-026-06859-8","workflowStages":[]},"version":"v1","identity":"rs-7557167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7557167","identity":"rs-7557167","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}