Improving the diagnosis of severe malaria in African children using platelet counts and plasma Pf HRP2 concentrations

Abstract

Severe malaria caused by Plasmodium falciparum is difficult to diagnose accurately in children in high-transmission settings. Using data from 2649 pediatric and adult patients enrolled in four studies of severe illness in three countries (Bangladesh, Kenya, and Uganda), we fitted Bayesian latent class models using two diagnostic markers: the platelet count and the plasma concentration of P. falciparum histidine-rich protein 2 (PfHRP2). In severely ill patients with clinical features consistent with severe malaria, the combination of a platelet count of ≤150,000/μl and a plasma PfHRP2 concentration of ≥1000 ng/ml had an estimated sensitivity of 74% and specificity of 93% in identifying severe falciparum malaria. Compared with misdiagnosed children, pediatric patients with true severe malaria had higher parasite densities, lower hematocrits, lower rates of invasive bacterial disease, and a lower prevalence of both sickle cell trait and sickle cell anemia. We estimate that one-third of the children enrolled into clinical studies of severe malaria in high-transmission settings in Africa had another cause of their severe illness.

Fig. 1. The diagnostic value of platelet counts (pink) and plasma PfHRP2 concentrations (green) estimated using data from 2063 patients diagnosed with severe falciparum malaria in three studies.

(A) and (B) show the mean estimated sensitivity (dotted lines) and specificity (dashed lines) functions for platelet count and plasma PfHRP2, respectively. For the platelet count, thresholds correspond to upper limits, whereas for the PfHRP2 concentration, the thresholds correspond to lower limits. Shaded areas show 95% CI. (C) shows the ROC curves for each marker.

Fig. 2. Probabilistic model of severe falciparum malaria using platelet counts and plasma PfHRP2 concentrations in 2622 severely ill patients based on a Bayesian parametric latent class model with three latent classes (a severe malaria class and two not severe malaria classes).

(A) to (D) show the individual data for each study [(A) Kilifi cohort, (B) Kampala cohort, (C) FEAST trial, and (D) Bangladesh cohort]. The colors correspond to the probability of severe malaria under the model (dark blue: high probability; dark red: low probability). Triangles show the individuals with HbAS; crosses show the individuals with HbSS. To show data points with nonmeasurable plasma PfHRP2, nonmeasurable concentrations were set to 1 ng/ml ± random jitter on the log₁₀ scale (about half the lower limit of quantification of the assay). P(SM), Probability(Severe Malaria).

Fig. 3. Mortality as a function of the probability of having severe malaria under the Bayesian latent class model (based on platelet counts and PfHRP2 concentrations).

The lines (shaded areas) show mean (95% CIs) mortality estimates from logistic regression fits. (A) to (D) show the individual data for each study [(A) Kilifi cohort, (B) Kampala cohort, (C) FEAST trial, and (D) Bangladesh cohort].

Fig. 4. Admission parasite densities as a function of the probability of severe malaria under the Bayesian latent class model.

Data from the FEAST trial include only the patients with a positive malaria rapid diagnostic test. The thick lines show the additive linear model fit (spline-based). (A) to (D) show the individual data for each study [(A) Kilifi cohort, (B) Kampala cohort, (C) FEAST trial, and (D) Bangladesh cohort].