Evaluation of hematological indices of childhood illnesses in Tamale Metropolis of Ghana

Anabire, Nsoh Godwin; Aryee, Paul Armah; Addo, Francis; Anaba, Frank; Kanwugu, Osman N.; Ankrah, Jacob; Awandare, Gordon A.; Helegbe, Gideon Kofi

doi:10.1002/jcla.22582

Cited by 5 publications

(15 citation statements)

References 18 publications

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“…The first was an observation that platelet counts were significantly reduced in eBL cases with malaria parasitaemia/antigenaemia. This observation was similar to the results in the controls and what has been reported in children without eBL but infected with malaria 17,18,20,21 . The decrease in platelet counts with malaria is attributed to platelet degranulation, which results in the release of PF4 to kill malaria parasites 13 .…”

Section: Discussionsupporting

confidence: 90%

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Mean platelet counts are relatively decreased with malaria but relatively increased with endemic Burkitt Lymphoma in Uganda, Tanzania, and Kenya

et al. 2020

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Summary Platelet counts are decreased in Plasmodium falciparum malaria, which is aetiologically linked with endemic Burkitt lymphoma (eBL). However, the pattern of platelet counts in eBL cases is unknown. We studied platelet counts in 582 eBL cases and 2 248 controls enrolled in a case‐control study in Uganda, Tanzania and Kenya (2010–2016). Mean platelet counts in controls or eBL cases with or without malaria‐infection in controls versus eBLcases were compared using Student’s t‐test. Odds ratios (ORs) and two‐sided 95% confidence intervals (95% CIs) were estimated using multiple logistic regression, controlling for age, sex, haemoglobin and white blood cell counts. Platelets were decreased with malaria infection in the controls [263 vs. 339 × 109 platelets/l, P < 0·0001; adjusted OR (aOR) = 3·42, 95% CI: 2·79–4·18] and eBL cases (314 vs. 367 × 109 platelets/l, P‐value = 0·002; aOR = 2·36, 95% CI: 1·49–3·73). Unexpectedly, platelets were elevated in eBL cases versus controls in overall analyses (mean: 353 vs. 307 × 109 platelets/l, P < 0·0001; aOR = 1·41; 95% CI: 1·12–1·77), and when restricted to malaria‐positive (mean 314 vs. 263 × 109 platelets/l, P < 0·0001; OR = 2·26; 95% CI: 1·56–3·27) or malaria‐negative (mean 367 vs. 339 × 109 platelets/l, P < 0·001; OR = 1·46; 95% CI: 1·17–1·83) subjects. Platelets were decreased with malaria infection in controls and eBL cases but elevated with eBL.

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Section: Discussionsupporting

confidence: 90%

“…Asymptomatic or clinical malaria is associated with decreased platelet counts and the reduction is greatest in children with the most severe form of malaria 17–21 . However, whether malaria infection is associated with decreased platelet count in children with eBL is presently unknown.…”

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confidence: 99%

Mean platelet counts are relatively decreased with malaria but relatively increased with endemic Burkitt Lymphoma in Uganda, Tanzania, and Kenya

et al. 2020

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“…In Africa, 61 studies (75.3%) were conducted; in Asia, 19 studies (23.4%); and in Europe, 1 study. The African studies were conducted in Nigeria (30/61, 49.2%) 36 – 38 , 42 , 47 , 48 , 50 , 51 , 57 , 59 , 61 , 64 , 75 , 77 , 82 – 84 , 89 – 91 , 94 – 101 , 103 , 107 , Cameroon (5/61, 8.2%) 35 , 40 , 73 , 92 , 93 , Ghana (5/61, 8.2%) 41 , 53 , 56 , 76 , 85 , Kenya (5/61, 6.2%) 28 , 29 , 60 , 72 , 80 , Tanzania (4/61, 6.56%) 20 , 39 , 44 , 46 , Malawi 17 , 27 , 30 , Burkina Faso 54 , 86 , Mozambique 79 , Sierra Leone 87 , the Democratic Republic of the Congo 49 , Ethiopia 45 , Gabon 55 , and Gambia 31 , and one study was conducted in Burkina Faso, Ethiopia, Ghana, Guinea-Bissau, Kenya, Madagascar, Senegal, South Africa, Sudan, and Tanzania 62 . The Asian studies were conducted in India (12/19, 63.2%) 43 , 66 – 68 , 71 , 74 , 81 , 88 , 104 –…”

Section: Resultsmentioning

confidence: 99%

Prevalence, probability, and outcomes of typhoidal/non-typhoidal Salmonella and malaria co-infection among febrile patients: a systematic review and meta-analysis

Wilairatana

Mala

Klangbud

et al. 2021

Sci Rep

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The geographical overlaps of malaria parasites and Salmonella spp. can lead to co-infection of these two pathogens, especially in the tropics where malaria is endemic. Moreover, few literatures suggested that malaria infection was associated with Salmonella bacteremia. Therefore, this study quantified pooled prevalence of typhoidal/non-typhoidal Salmonella (NTS) and probability of typhoidal/NTS and malaria co-infection among febrile patients. The systematic review protocol was registered at PROSPERO (CRD42021252322). Studies on co-infection of typhoidal/NTS and malaria were searched in PubMed, Scopus, and Web of Science. The risk of bias of the included studies was assessed using the checklist for analytical cross-sectional studies developed by the Joanna Briggs Institute. Meta-analyses on the following criteria were performed: (1) pooled prevalence of typhoidal/NTS and malaria co-infection among febrile patients, (2) pooled prevalence of typhoidal/NTS among malaria patients, (3) pooled prevalence of malaria infections among patients with Salmonella spp. infection, and (4) probability of typhoidal/NTS and malaria co-infection among febrile patients. Additionally, the case fatality rate and mean difference of malarial parasitemia between typhoidal/NTS and malaria co-infection and Plasmodium monoinfection were also determined. The subgroup analyses of typhoidal/NTS, regions (Africa and Asia), countries, time (publication year), characteristics of participants, and diagnostic tests for identifying Salmonella spp. were also conducted. A sensitivity test was performed to determine the robustness of the study outcomes. Publication bias among the included studies was evaluated using the funnel plot and Egger’s test. All analyses were performed using Stata version 15 (StataCorp LLC, Texas, USA) with a p-value < 0.05 indicating statistical significance. Eighty-one studies that met the eligibility criteria were included in the analyses. Of the 73,775 study participants, 4523 had typhoidal/NTS and malaria co-infections. The pooled prevalence rates of typhoidal/NTS and malaria co-infection among febrile patients were 14% (95% confidence interval [CI], 9–19%; I2, 99.4%; 2971/17,720 cases) and 1% (95% CI 1–1%; I2, 89.9%; 252/29,081 cases) using the Widal test and culture methods for identifying Salmonella spp., respectively. The pooled prevalence rates of typhoidal/NTS infection among patients with malaria were 31% (95% CI 23–39%; I2, 99.5%; 3202/19,208 cases) and 3% (95% CI 2–3%; I2, 86.8%; 407/40,426 cases) using the Widal test and culture methods for identifying Salmonella spp., respectively. The pooled prevalence rates of malaria infection among patients with typhoidal/NTS were 17% (95% CI 6–29%; I2, 33.3%; 13/75 cases) and 43% (95% CI 32–53%; I2, 89.1%; 287/736 cases), respectively. Malaria infection was associated with typhoidal/NTS in children aged < 15 years (p < 0.0001; odds ratio, 0.36; 95% CI 0.23–0.58; I2, 73.9%; 3188/43,212 cases). The case fatality rate in patients with malaria and NTS co-infections was 16% (95% CI 9–24%; I2, 89.1%; 18/103 cases). From the view of the present study, the inappropriate use of the Widal test for Salmonella spp. diagnosis can overestimate the prevalence of typhoidal/NTS and malaria co-infections. Malaria infection associated with typhoidal/NTS in children and the high case fatality rates among few patients with co-infections were highlighted. Future prospective longitudinal studies using the appropriate and confirmatory dsiagnosis for Salmonella spp. infections are highly recommended to ensure the real prevalence of co-infection and highlight the outcome of co-infection for providing adequate treatment in febrile patients who live in areas where malaria is endemic, such as tropical Africa and India.

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“…In addition, haemoglobin (Hb) levels are very important for the classification of SM cases [12]. Indeed, the changes in haematological parameters during clinical malaria have been studied extensively to aid in the understanding of disease pathogenesis [13][14][15][16][17][18][19]. However, the potential and diagnostic value of haematological parameters measured by commonly available automated haematology analysers has not been fully studied using unbiased approaches such as machine learning (ML) techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Morang’a

Amenga–Etego

Bah

et al. 2020

BMC Med

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Background Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. Methods We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. Results The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. Conclusion The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.

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Evaluation of hematological indices of childhood illnesses in Tamale Metropolis of Ghana

Abstract: Hematological indices including Hb, MCV and PLT, or MCH may be useful indices that could incite further diagnostic tests for malaria or SCD among children in Ghana.

Cited by 5 publications

References 18 publications

Mean platelet counts are relatively decreased with malaria but relatively increased with endemic Burkitt Lymphoma in Uganda, Tanzania, and Kenya

Mean platelet counts are relatively decreased with malaria but relatively increased with endemic Burkitt Lymphoma in Uganda, Tanzania, and Kenya

Prevalence, probability, and outcomes of typhoidal/non-typhoidal Salmonella and malaria co-infection among febrile patients: a systematic review and meta-analysis

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

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