Machine learning approaches classify clinical malaria outcomes based on haematological parameters

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

Impact of intra-partum azithromycin on carriage of group A streptococcus in the Gambia: a posthoc analysis of a double-blind randomized placebo-controlled trial

Background: Group A Streptococcus (GAS) is a major human pathogen and an important cause of maternal and neonatal sepsis. Asymptomatic bacterial colonization is considered a necessary step towards sepsis. Intra-partum azithromycin may reduce GAS carriage. Methods: A posthoc analysis of a double-blind, placebo-controlled randomized-trial was performed to determine the impact of 2 g oral dose of intra-partum azithromycin on maternal and neonatal GAS carriage and antibiotic resistance. Following screening, 829 mothers were randomized who delivered 843 babies. GAS was determined by obtaining samples from the maternal and newborn nasopharynx, maternal vaginal tract and breastmilk. Whole Genome Sequencing (WGS) of GAS isolates was performed using the Illumina Miseq platform. Results: GAS carriage was lower in the nasopharynx of both mothers and babies and breast milk among participants in the azithromycin arm. No differences in GAS carriage were found between groups in the vaginal tract. The occurrence of azithromycin-resistant GAS was similar in both arms, except for a higher prevalence in the vaginal tract among women in the azithromycin arm. WGS revealed all macrolide-resistant vaginal tract isolates from the azithromycin arm were Streptococcus dysgalactiae subspecies equisimilis expressing Lancefield group A carbohydrate (SDSE(A)) harbouring macrolide resistant genes msr(D) and mef(A). Ten of the 45 GAS isolates (22.2%) were SDSE(A). Conclusions: Oral intra-partum azithromycin reduced GAS carriage among Gambian mothers and neonates however carriage in the maternal vaginal tract was not affected by the intervention due to azithromycin resistant SDSE(A). SDSE(A) resistance must be closely monitored to fully assess the public health impact of intrapartum azithromycin on GAS. Trial registration: ClinicalTrials.gov Identifier: NCT0180094