Dataset Information

Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.

ABSTRACT: More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.

SUBMITTER: Johnston IG

PROVIDER: S-EPMC6620311 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.

Johnston Iain G IG Hoffmann Till T Greenbury Sam F SF Cominetti Ornella O Jallow Muminatou M Kwiatkowski Dominic D Barahona Mauricio M Jones Nick S NS Casals-Pascual Climent C

NPJ digital medicine 20190710

More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 29 ...[more]

PMID: 31312723

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Project description:IntroductionSevere hyperkalemia, with potassium (K+) levels ? 6.5 mEq/L, is a potentially life-threatening electrolyte imbalance. For prompt and effective treatment, it is important to know its risk factors, clinical manifestations, and predictors of mortality.MethodsAn observational cohort study was performed at 2 medical centers. A total of 923 consecutive Korean patients were analyzed. All were 19 years of age or older and were hospitalized with severe hyperkalemia between August 2007 and July 2010; the diagnosis of severe hyperkalemia was made either at the time of admission to the hospital or during the period of hospitalization. Demographic and baseline clinical characteristics at the time of hyperkalemia diagnosis were assessed, and clinical outcomes such as in-hospital mortality were reviewed, using the institutions' electronic medical record systems.ResultsChronic kidney disease (CKD) was the most common underlying medical condition, and the most common precipitating factor of hyperkalemia was metabolic acidosis. Emergent admission was indicated in 68.6% of patients, 36.7% had electrocardiogram findings typical of hyperkalemia, 24.5% had multi-organ failure (MOF) at the time of hyperkalemia diagnosis, and 20.3% were diagnosed with severe hyperkalemia at the time of cardiac arrest. The in-hospital mortality rate was 30.7%; the rate was strongly correlated with the difference between serum K+ levels at admission and at their highest point, and with severe medical conditions such as malignancy, infection, and bleeding. Furthermore, a higher in-hospital mortality rate was significantly associated with the presence of cardiac arrest and/or MOF at the time of diagnosis, emergent admission, and intensive care unit treatment during hospitalization. More importantly, acute kidney injury (AKI) in patients with normal baseline renal function was a strong predictor of mortality, compared with AKI superimposed on CKD.ConclusionsSevere hyperkalemia occurs in various medical conditions; the precipitating factors are similarly diverse. The mortality rate is especially high in patients with severe underlying disease, coexisting medical conditions, and those with normal baseline renal function.

Project description:BackgroundNext Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the process of sharing and aggregating multisite sequencing datasets are thwarted by issues such as the need to discover relevant data from different sources, built scalable repositories, the automation of data linkage, the volume of the data, efficient querying mechanism, and information rich intuitive visualisation.ResultsWe present an approach to link and query different sequencing datasets (TCGA, COSMIC, REACTOME, KEGG and GO) to indicate risks for four cancer types - Ovarian Serous Cystadenocarcinoma (OV), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) - covering the 16 healthy tissue-specific genes from Illumina Human Body Map 2.0. The differentially expressed genes from Illumina Human Body Map 2.0 are analysed together with the gene expressions reported in COSMIC and TCGA repositories leading to the discover of potential biomarkers for a tissue-specific cancer.ConclusionWe analyse the tissue expression of genes, copy number variation (CNV), somatic mutation, and promoter methylation to identify associated pathways and find novel biomarkers. We discovered twenty (20) mutated genes and three (3) potential pathways causing promoter changes in different gynaecological cancer types. We propose a data-interlinked platform called BIOOPENER that glues together heterogeneous cancer and biomedical repositories. The key approach is to find correspondences (or data links) among genetic, cellular and molecular features across isolated cancer datasets giving insight into cancer progression from normal to diseased tissues. The proposed BIOOPENER platform enriches mutations by filling in missing links from TCGA, COSMIC, REACTOME, KEGG and GO datasets and provides an interlinking mechanism to understand cancer progression from normal to diseased tissues with pathway components, which in turn helped to map mutations, associated phenotypes, pathways, and mechanism.

Project description:BACKGROUND:Severe falciparum malaria is a medical emergency characterised by potentially lethal vital organ dysfunction. Patient fatality rates even with parenteral artesunate treatment remain high. Despite considerable research into adjuvant therapies targeting organ and tissue dysfunction, none have shown efficacy apart from renal replacement therapy. Understanding the causal contributions of clinical and laboratory abnormalities to mortality is essential for the design and evaluation of novel therapeutic interventions. METHODS AND FINDINGS:We used a structural model causal inference approach to investigate causal relationships between epidemiological, laboratory, and clinical variables in patients with severe falciparum malaria enrolled in clinical trials and their in-hospital mortality. Under this causal model, we analysed records from 9,040 hospitalised children (0-12 years, n = 5,635) and adults (n = 3,405, 12-87 years) with severe falciparum malaria from 15 countries in Africa and Asia who were studied prospectively over the past 35 years. On admission, patient covariates associated with increased in-hospital mortality were severity of acidosis (odds ratio [OR] 2.10 for a 7-mEq/L increase in base deficit [95% CI 1.93-2.28]), renal impairment (OR 1.71 for a 2-fold increase in blood urea nitrogen [95% CI 1.58, 1.86]), coma (OR 3.59 [95% CI 3.07-4.21]), seizures (OR 1.40 [95% CI 1.16-1.68]), shock (OR 1.51 [95% CI 1.14-1.99]), and presumed pulmonary oedema (OR 1.58 [95% CI 1.04-2.39]). Lower in-hospital mortality was associated with moderate anaemia (OR 0.87 for a decrease of 10 percentage points in haematocrit [95% CI 0.80-0.95]). Circulating parasite density was not associated with mortality (OR 1.02 for a 6-fold increase [95% CI 0.94-1.11]), so the pathological effects of parasitaemia appear to be mediated entirely by the downstream effects of sequestration. Treatment with an artemisinin derivative decreased mortality compared with quinine (OR 0.64 [95% CI 0.56-0.74]). These estimates were consistent across children and adults (mainly representing African and Asian patients, respectively). Using inverse probability weighting, transfusion was not estimated to be beneficial in children with admission haematocrit values between 15% and 25% (OR 0.99 [95% CI 0.97-1.02]). Except for the effects of artemisinin treatment and transfusion, causal interpretations of these estimates could be biased by unmeasured confounding from severe bacterial sepsis, immunity, and duration of illness. CONCLUSION:These data suggest that moderate anaemia is associated with a reduced risk of death in severe falciparum malaria. This is possibly a direct causal association. The severe anaemia threshold criteria for a definition of severe falciparum malaria should be reconsidered.

Dataset Information

Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.

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Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.

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