Haematological reference intervals for healthy adults in Bamenda, Cameroon

Fondoh, Victor N; Fondoh, Richard M; Awasom, Charles; Edith, Pefoule L; Ntungwen, Winlove A; Roland, Bong; Enow-Tanjong, Rebeca; Njukeng, Patrick Achiangia; Shang, Judith; Egbengu, Egbe P.; Maruta, Talkmore; Etheline, Akazong; Leke, Robert J. I.; Leo, Ayuk; Nsame, Denis

doi:10.4102/ajlm.v9i1.1193

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…Because PV changes are not considered in the [Hb] thresholds established by the WHO to diagnose anemia, this diagnostic model presents substantial limitations (16,17). Numerous studies have soundly measured reference hematological values, including [Hb], in populations with high anemia prevalence according to the WHO website, notably in women of reproductive age and young children (< 5 years old) (39)(40)(41)(42)(43)(44)(45)(46). Such countries are described with a severe anemia prevalence by the WHO with values going from 40% to 69% in either populations (12,47).…”

Section: Discussionmentioning

confidence: 99%

Prediction of plasma volume and total hemoglobin mass with machine learning

Basile

Krumm

Saugy

et al. 2023

Preprint

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Anemia is a widespread disease commonly diagnosed through hemoglobin concentration ([Hb]) thresholds set by the World Health Organization (WHO). However, [Hb] is subject to significant variations mainly due to shifts in plasma volume (PV) which impair the diagnosis of anemia and other medical conditions. The aim of this study was to develop a model able to accurately predict total hemoglobin mass (Hbmass) and PV based on anthropometric and complete blood count (CBC) analyses. 769 CBC coupled to measures of Hbmass and PV using the CO-rebreathing method were used with a machine learning tool in a numeric computing platform (MATLAB regression learner app) to calculate the model. For the predicted values, root mean square error (RMSE) was of 37.9 g and 50.0 g for Hbmass, and 194 ml and 268 ml for PV, in women and men, respectively. Measured and predicted data were significantly correlated (p<0.001) with the coefficient of determination (R2) ranging from 0.73 to 0.81 for Hbmass, and PV, in both women and men. The bland-altman bias between estimated and measured variables was in average of -0.69 for Hbmass and 0.73 for PV. This study proposes a valid model with a high prediction potential for Hbmass and PV, providing relevant complementary data in numerous contexts. This method can notably bring information applicable to the epidemiology of anemia, particularly in countries with high prevalence or in specific population such as high-altitude communities.

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Section: Discussionmentioning

confidence: 99%

Prediction of plasma volume and total hemoglobin mass with machine learning

Basile

Krumm

Saugy

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Because PV changes are not considered in the [Hb] thresholds established by the WHO to diagnose anemia, this diagnostic model presents substantial limitations (Berkow, 2013; Karakochuk et al, 2019). Numerous studies have soundly measured reference hematological values, including [Hb], in populations with high anemia prevalence (Béavogui et al, 2020; Buchanan et al, 2010; Dapper et al, 2009; Fondoh et al, 2020; Humberg et al, 2011; Kone et al, 2017; Schmidt et al, 2018; Umar et al, 2015;WHO, 2023a; WHO, 2023b). The data provided by these studies, despite being crucial regarding anemia epidemiology, can possibly lead to over‐ or underestimation of total prevalence due to PV fluctuations.…”

Section: Discussionmentioning

confidence: 99%

Prediction of plasma volume and total hemoglobin mass with machine learning

Moreillon,

Krumm,

Saugy

et al. 2023

Physiological Reports

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Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed model able to accurately predict total hemoglobin mass (Hbmass) and PV from a single complete blood count (CBC) and anthropometric variables in healthy subject. Seven hundred and sixty‐nine CBC coupled to measures of Hbmass and PV using a CO‐rebreathing method were used with a machine learning tool to calculate an estimation model. The predictive model resulted in a root mean square error of 33.2 g and 35.6 g for Hbmass, and 179 mL and 244 mL for PV, in women and men, respectively. Measured and predicted data were significantly correlated (p < 0.001) with a coefficient of determination (R2) ranging from 0.76 to 0.90 for Hbmass and PV, in both women and men. The Bland–Altman bias was on average 0.23 for Hbmass and 4.15 for PV. We herewith present a model with a robust prediction potential for Hbmass and PV. Such model would be relevant in providing complementary data in contexts such as the epidemiology of anemia or the individual monitoring of [Hb] in anti‐doping.

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“…Since RIs are influenced by age, gender, dietary habits, and race, the Clinical and Laboratory Standard Institute (CLSI) recommends population-specific RIs to situate and contextualize clinical decisions [1]. Previous research in the sub-region have determined that most haematological RIs in sub-Saharan Africa have lower limits lesser than westerncentric RIs [2][3][4][5][6]. For example, in the definition of anaemia in adults, the World Health Organisation (WHO) recommends that haemoglobin cutoff of 12.5 g/dL (females) and 13.5 g/dL (males) be used in Caucasian population compared to 11.5 g/dL (females) and 12.5 g/dL (males) for individuals from sub-Saharan Africa [7].…”

Section: Introductionmentioning

confidence: 99%

Establishing pregnancy-specific haematological reference intervals in Ghana; a three-center cross-sectional study

et al. 2023

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Background Abnormal intra-pregnancy haematological variables are associated with adverse feto-maternal outcomes. However, the reference intervals (RIs) employed in sub-Saharan Africa to inform clinical decisions are generally imported. Since RIs are influenced by age, geographical location, and race, we hypothesized that context specific RIs should be established in Ghana to contextualize intra-pregnancy decision making. Methods This cross-sectional study retrospectively retrieved data of 333 pregnant women with no known clinically determined intra-pregnancy complications; 22 participants in their first trimester (T1; 1–13 weeks), 177 in their T2 (14–27 weeks), and 132 in T3 (28–41 weeks). RIs for haematological parameters were non-parametrically determined at 2.5th and 97.5th percentiles in accordance with CLSI guidance document EP28-A3c. Two-sample comparisons were undertaken using Wilcoxon rank-sum tests whereas more than two-sample comparisons were undertaken using Kruskal-Wallis test. Statistical significance was set at p <0.05 under the two-tailed assumptions. Results In accordance with WHO trimester-specific haemoglobin cutoffs, anaemia prevalence was a moderate (T1: 36.4%; 8/22 & T2: 31.6%; 56/177) to severe (T3:68.0%; 90/132) public health problem. Additionally, 9.3% (31/333) individuals had high gestational haemoglobin levels (Hb >13.0 g/dL). Moreover, haemoglobin (T2: 8.6–14.3 vs T3: 7.5–13.6 g/dL), MCH (T2: 22.5–69.8 vs T3: 21.6–31.9 pg), MCHC (T2: 30.2–51.8 g/L vs T3: 30.5–37.9 g/L), TWBC (T2: 4.0–13.4 vs T3: 4.1–13.0 x 109/L) required trimester specific RIs, compared to RBC (2.8–5.1 x 1012/L), MCV (66.2–100.2 fL), and platelet counts (106.3–388.3 x 109/L) that each required combined reference intervals. Conclusions The intra-pregnancy haematological RIs determined have appreciable lower limits; there is the need to determine context-specific thresholds for haematological variables predictive of positive and/or adverse maternal and infant health outcomes.

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Haematological reference intervals for healthy adults in Bamenda, Cameroon

Cited by 8 publications

References 39 publications

Prediction of plasma volume and total hemoglobin mass with machine learning

Prediction of plasma volume and total hemoglobin mass with machine learning

Prediction of plasma volume and total hemoglobin mass with machine learning

Establishing pregnancy-specific haematological reference intervals in Ghana; a three-center cross-sectional study

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