Clinical and pathophysiological aspects of severe falciparum malaria

Malaria is a major global public health problem. One-fifth of the world's population is at risk of malaria and drug resistance is spreading. Nearly five times as many cases of malaria were reported in 2000 as tuberculosis, AIDS, measles and leprosy cases combined. In the time it takes to say the word "malaria," ten children will contract the disease and begin fighting for their lives. Since the cinchona alkaloids were introduced as a specific treatment for agues 350 years ago, the treatment of severe malaria has changed little and therapy remains largely empirical. Quinine and quinidine remain the drugs of choice for severe chloroquine-resistant malaria due to Plasmodium falciparum, and with the spread of these resistant parasites, the usage of these drugs is increasing. In 1972 scientists in China discovered the antimalarial properties of a group of sesquiterpene lactone peroxides derived from the qinghao plant (Artemisia annua). The principal component, qinghaosu (artemisinin), and two derivatives - the water-soluble hemisuccinate artesunate and the oil-soluble artemether - are the most rapidly acting and potent of all antimalarial drugs. Although much research effort has been invested in optimizing antimalarial drug regimes, severe malaria remains a major cause of adult mortality in the Asiatic tropics. Despite many clinical trials reducing the mortality from severe malaria has proved difficult. Artemether has been shown to be as good as quinine but not better. This thesis set out to determine whether Artemether or Artesunate was the the better drug, how to manage acute renal failure in severe malaria, to design a severity score for malaria and assess whether there had been any change in the parasite clearance times in Viet Nam over a 18 year period. The results from a series of clinical trials and research from one hospital in Viet Nam are presented. This thesis undertook the following studies: 1. A randomised clinical trial of artesunate vs artemether in the treatment of severe malaria in adults in Viet Nam. 2. A randomized comparison of pumped venovenous haemofiltration and peritoneal dialysis in acute renal failure associated with severe infection. 3. Fluid management in severe malaria. 4. The stage of the development of the falciparum parasites at the time of clinical presentation with severe malaria is important in predicting outcome. 5. Development of a predictive score of outcome in adults with severe falciparum malaria. 6. Assessment of the efficacy of the artemisinin derivatives in the treatment of severe falciparum malaria in Viet Nam 1991-2008

Magnitude and patterns of severe Plasmodium vivax monoinfection in Vietnam: a 4-year single-center retrospective study

IntroductionInfection with Plasmodium vivax is a recognized cause of severe malaria including deaths. The exact burden and patterns of severe P. vivax monoinfections is however still not well quantified, especially in P. vivax endemic regions. We examined the magnitude and patterns of severe malaria caused by monoinfections of P. vivax and associated predictors among patients admitted to a tertiary care center for malaria in Vietnam.MethodsA retrospective cohort study was conducted based on the patients’ medical records at the Hospital for Tropical Diseases from January 2015 to December 2018. Extracted information included demographic, epidemiologic, clinical, laboratory and treatment characteristics.ResultsMonoinfections with P. vivax were found in 153 (34.5, 95% CI 30.3–39.1%) patients of whom, uncomplicated and severe malaria were documented in 89.5% (137/153, 95% CI 83.7–93.5%) and 10.5% (16/153, 95% CI 6.5–16.3%), respectively. Patterns of severe malaria included jaundice (8 cases), hypoglycemia (3 cases), shock (2 cases), anemia (2 cases), and cerebral malaria (1 case). Among 153 patients, 73 (47.7%) had classic malaria paroxysm, 57 (37.3%) had >7 days of illness at the time of admission, and 40 (26.1%) were referred from other hospitals. A misdiagnosis as having other diseases from malaria cases coming from other hospitals was up to 32.5% (13/40). Being admitted to hospital after day 7th of illness (AOR = 6.33, 95% CI 1.14–35.30, p = 0.035) was a predictor of severe malaria. Severe malaria was statistically associated with longer hospital length of stay (p = 0.035). Early and late treatment failures and recrudescence were not recorded. All patients recovered completely.DiscussionThis study confirms the emergence of severe vivax malaria in Vietnam which is associated with delayed hospital admission and increased hospital length of stay. Clinical manifestations of P. vivax infection can be misdiagnosed which results in delayed treatment. To meet the goal of malaria elimination by 2030, it is crucial that the non-tertiary hospitals have the capacity to quickly and correctly diagnose malaria and then provide treatment for malaria including P. vivax infections. More robust studies need to be conducted to fully elucidate the magnitude of severe P. vivax in Vietnam

Fragmented understanding: exploring the practice and meaning of informed consent in clinical trials in Ho Chi Minh City, Vietnam

Background The informed consent process in clinical trials has been extensively studied to inform the development processes which protect research participants and encourage their autonomy. However, ensuring a meaningful informed consent process is still of great concern in many research settings due to its complexity in practice and interwined socio-cultural factors. Objectives This study explored the practices and meaning of the informed consent process in two clinial trials conducted by Oxford University Clinical Research Unit in collaboration with the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam. Methods We used multiple data collection methods including direct observervations, in-depth interviews with study physicians and trial participants, review of informed consent documents from 2009 to 2018, and participant observation with patients’ family members. We recruited seven physicians and twenty-five trial participants into the study, of whom five physicians and thirteen trial participants completed in-depth interviews, and we held twenty-two direct observation sessions. Results We use the concept “fragmented understanding” to describe the nuances of understanding about the consent process and unpack underlying reasons for differing understandings. Conclusions Our findings show how practices of informed consent and different understanding of the trial information are shaped by trial participants’ characteristics and the socio-cultural context in which the trials take place

Automated pupillometry and optic nerve sheath diameter ultrasound to define tuberculous meningitis disease severity and prognosis

Background: Tuberculous meningitis (TBM) causes high mortality and morbidity, in part due to raised intracranial pressure (ICP). Automated pupillometry (NPi) and optic nerve sheath diameter (ONSD) are both low-cost, easy-to-use and non-invasive techniques that correlate with ICP and neurological status. However, it is uncertain how to apply these techniques in the management of TBM. Methods: We conducted a pilot study enrolling 20 adults with TBM in the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Our objective was to investigate the relationships between baseline and serial measurements of NPi and ONSD and disease severity and outcome. Serial NPi and ONSD were performed for 30 days, at discharge, and at 3-months, with measurements correlated with clinical progression and outcomes. Results: ONSD and NPi measurements had an inverse relationship. Higher ONSD and lower NPi values were associated with lower Glasgow coma score. Baseline NPi was a strong predictor 3-month outcome (median NPi 4.55, interquartile range 4.35–4.65 for good outcomes versus 2.60, IQR 0.65–3.95 for poor outcomes, p = 0.002). Pupil inequality (NPi ≥0.7) was also strongly associated with poor 3-month outcomes (p = 0.006). Individual participants' serial NPi and ONSD were variable during initial treatment and correlated with clinical condition and outcome. Conclusion: Pupillometry and ONSD may be used to predict clinical deterioration and outcome from TBM. Future, larger studies are need explore the optimal timing of measurements and to define how they might be used to optimise treatments and improve outcomes from TBM

Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data

Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention

A novel diagnostic model for tuberculous meningitis using Bayesian latent class analysis

Background Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known. Methods We included 659 individuals aged ≥ 16 years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl–Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally. Results Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden’s Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively. Conclusion Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Summary Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research