Authors' Reply: Response to Ian Clark

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The spread of antimalarial drug resistance: A mathematical model with practical implications for ACT drug policies

Most malaria-endemic countries are implementing a change in antimalarial drug policy to artemisinin combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. A comprehensive model was constructed incorporating important epidemiological and biological factors and used to illustrate the spread of resistance in low and high transmission settings. The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and that in low transmission areas ACTs slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. A major obstacle to achieving the benefits of high coverage is the current cost of the drugs. This argues strongly for a global subsidy to make ACTs generally available and affordable in endemic areas

Identifying risk factors for the development of sepsis during adult severe malaria.

BACKGROUND: Severe falciparum malaria can be compounded by bacterial sepsis, necessitating antibiotics in addition to anti-malarial treatment. The objective of this analysis was to develop a prognostic model to identify patients admitted with severe malaria at higher risk of developing bacterial sepsis. METHODS: A retrospective data analysis using trial data from the South East Asian Quinine Artesunate Malaria Trial. Variables correlating with development of clinically defined sepsis were identified by univariable analysis, and subsequently included into a multivariable logistic regression model. Internal validation was performed by bootstrapping. Discrimination and goodness-of-fit were assessed using the area under the curve (AUC) and a calibration plot, respectively. RESULTS: Of the 1187 adults with severe malaria, 86 (7.3%) developed clinical sepsis during admission. Predictors for developing sepsis were: female sex, high blood urea nitrogen, high plasma anion gap, respiratory distress, shock on admission, high parasitaemia, coma and jaundice. The AUC of the model was 0.789, signifying modest differentiation for identifying patients developing sepsis. The model was well-calibrated (Hosmer-Lemeshow Chi squared = 1.02). The 25th percentile of the distribution of risk scores among those who developed sepsis could identify a high-risk group with a sensitivity and specificity of 70.0 and 69.4%, respectively. CONCLUSIONS: The proposed model identifies patients with severe malaria at risk of developing clinical sepsis, potentially benefiting from antibiotic treatment in addition to anti-malarials. The model will need further evaluation with more strictly defined bacterial sepsis as outcome measure

A Competing-Risk Approach for Modeling Length of Stay in Severe Malaria Patients in South-East Asia and the Implications for Planning of Hospital Services.

Background: Management of severe malaria with limited resources requires comprehensive planning. Expected length of stay (LOS) and the factors influencing it are useful in the planning and optimisation of service delivery. Methods: A secondary, competing-risk approach to survival analysis was performed for 1217 adult severe malaria patients from the South-East Asia Quinine Artesunate Malaria Trial. Results: Twenty percent of patients died; 95.4% within 7 days compared to 70.3% of those who were discharged. Median time to discharge was 6 days. Compared to quinine, artesunate increased discharge incidence (subdistribution-Hazard ratio, 1.24; [95% confidence interval 1.09-1.40]; P = .001) and decreased incidence of death (0.60; [0.46-0.80]; P < .001). Low Glasgow coma scale (discharge, 1.08 [1.06-1.11], P < .001; death, 0.85 [0.82-0.89], P < .001), high blood urea-nitrogen (discharge, 0.99 [0.99-0.995], P < .001; death, 1.00 [1.00-1.01], P = .012), acidotic base-excess (discharge, 1.05 [1.03-1.06], P < .001; death, 0.90 [0.88-0.93], P < .001), and development of shock (discharge, 0.25 [0.13-0.47], P < .001; death, 2.14 [1.46-3.12], P < .001), or coma (discharge, 0.46 [0.32-0.65], P < .001; death, 2.30 [1.58-3.36], P < .001) decreased cumulative incidence of discharge and increased incidence of death. Conventional Kaplan-Meier survival analysis overestimated cumulative incidence compared to competing-risk model. Conclusions: Clinical factors on admission and during hospitalisation influence LOS in severe malaria, presenting targets to improve health and service efficiency. Artesunate has the potential to increase LOS, which should be accounted for when planning services. In-hospital death is a competing risk for discharge; an important consideration in LOS models to reduce overestimation of risk and misrepresentation of associations

Infectivity of Chronic Malaria Infections and Its Consequences for Control and Elimination

Assessing the importance of targeting the chronic Plasmodium falciparum malaria reservoir is pivotal as the world moves toward malaria eradication. Through the lens of a mathematical model, we show how, for a given malaria prevalence, the relative infectivity of chronic individuals determines what intervention tools are predicted be the most effective. Crucially, in a large part of the parameter space where elimination is theoretically possible, it can be achieved solely through improved case management. However, there are a significant number of settings where malaria elimination requires not only good vector control but also a mass drug administration campaign. Quantifying the relative infectiousness of chronic malaria across a range of epidemiological settings would provide essential information for the design of effective malaria elimination strategies. Given the difficulties obtaining this information, we also provide a set of epidemiological metrics that can be used to guide policy in the absence of such data

Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies

BACKGROUND: Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. METHODS: A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. RESULTS: The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with mismatched half-lives, together with reducing malaria transmission through vector control measures. CONCLUSION: This paper has demonstrated the use of a comprehensive mathematical model to describe malaria transmission and the spread of drug resistance. The model is strongly linked to the empirical evidence obtained from extensive data available from various sources. This model can be a useful tool to inform the design of treatment policies, particularly at a time when ACT has been endorsed by WHO as first-line treatment for falciparum malaria worldwide

Intestinal injury and the gut microbiota in patients with Plasmodium falciparum malaria

The pathophysiology of severe falciparum malaria involves a complex interaction between the host, parasite, and gut microbes. In this review, we focus on understanding parasite-induced intestinal injury and changes in the human intestinal microbiota composition in patients with Plasmodium falciparum malaria. During the blood stage of P. falciparum infection, infected red blood cells adhere to the vascular endothelium, leading to widespread microcirculatory obstruction in critical tissues, including the splanchnic vasculature. This process may cause intestinal injury and gut leakage. Epidemiological studies indicate higher rates of concurrent bacteraemia in severe malaria cases. Furthermore, severe malaria patients exhibit alterations in the composition and diversity of the intestinal microbiota, although the exact contribution to pathophysiology remains unclear. Mouse studies have demonstrated that the gut microbiota composition can impact susceptibility to Plasmodium infections. In patients with severe malaria, the microbiota shows an enrichment of pathobionts, including pathogens that are known to cause concomitant bloodstream infections. Microbial metabolites have also been detected in the plasma of severe malaria patients, potentially contributing to metabolic acidosis and other clinical complications. However, establishing causal relationships requires intervention studies targeting the gut microbiota

An artesunate pharmacometric model to explain therapeutic responses in falciparum malaria.

Background: The artemisinins are potent and widely used antimalarial drugs that are eliminated rapidly. A simple concentration–effect pharmacometric model does not explain why dosing more frequently than once daily fails to augment parasite clearance and improve therapeutic responses in vivo. Artemisinins can induce a temporary non-replicative or ‘dormant’ drug refractory state in Plasmodium falciparum malaria parasites which may explain recrudescences observed in clinical trials despite full drug susceptibility, but whether it explains the dosing–response relationship is uncertain. Objectives: To propose a revised model of antimalarial pharmacodynamics that incorporates reversible asexual parasite injury and temporary drug refractoriness in order to explain the failure of frequent dosing to augment therapeutic efficacy in falciparum malaria. Methods: The model was fitted using a Bayesian Markov Chain Monte Carlo approach with the parasite clearance data from 39 patients with uncomplicated falciparum malaria treated with artesunate from western Cambodia and 40 patients from northwestern Thailand reported previously. Results: The revised model captured the dynamics of parasite clearance data. Its predictions are consistent with observed therapeutic responses. Conclusions: A within-host pharmacometric model is proposed in which it is hypothesized that some malaria parasites enter a temporary drug refractory state after exposure to artemisinin antimalarials, which is followed by delayed parasite death or reactivation. The model fitted the observed sequential parasite density data from patients with acute P. falciparum malaria, and it supported reduced ring stage activity in artemisinin-resistant infections