Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples

Background Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. Results This study presents two novel statistical methods expectation–maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. Conclusions The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients

Prediction of Long-Term Poor Clinical Outcomes in Cerebral Venous Thrombosis Using Neural Networks Model:The BEAST Study

IntroductionRisk prediction models are commonly performed with logistic regression analysis but are limited by skewed datasets. We utilised neural networks (NNs) model to identify independent predictors of poor outcomes in cerebral venous thrombosis (CVT) due to the limitations of logistic regression (LR) analysis with complex datasets.MethodsWe evaluated 1309 adult CVT patients from the prospective BEAST (Biorepository to Establish the Aetiology of Sinovenous Thrombosis) study. The area under the receiver operating characteristic (AUROC) curve confirmed the goodness-of-fit of prediction models. The normalised importance (NI) of the NNs determines the significance of independent predictors.ResultsThe stepwise logistic regression model found thrombolysis (OR 32.1; 95% CI 3.6–287.0; P=0.002), craniotomy (OR 6.9; 95% CI 1.3–36.8; P=0.02), and cerebral haemorrhage (OR 4.5; 95% CI 1.3–15.4; P=0.01) as predictors of poor clinical outcome with the AUROC of 0.71. Conversely, the NNs model identified major independent predictors of long-term poor clinical outcomes as cerebral haemorrhage (NI 100%) and thrombolysis (NI 98%), as well as trivial predictors of age (NI 2.8%) and altered mental status (NI 3.5%). The accuracy of the NNs model was 95.1% and 94.1% for self-learned randomly selected training and testing samples with an AUROC of 0.82. Positive and negative predictive values for poor outcomes were 13.2% and 97.1% for the LR model, compared with the NNs model of 18.8% and 98.7%, respectively.ConclusionCerebral haemorrhage and thrombolysis was a strong independent predictor, whereas age merely impacts the long-term poor clinical outcome in adult CVT. Integrating unorthodox neural networks risk prediction model can improve decision-making as it outperforms conventional logistic regression with complex datasets

Quality of reporting of outcomes in phase III studies of pulmonary tuberculosis: a systematic review

Abstract Background Despite more than 60 years of clinical trials, tuberculosis (TB) still causes a high global burden of mortality and morbidity. Treatment currently requires multiple drugs in combination, taken over a prolonged period. New drugs are needed to shorten treatment duration, prevent resistance and reduce adverse events. However, to improve on current methodology in drug development, a more complete understanding of the existing clinical evidence base is required. Methods A systematic review was undertaken to summarise outcomes reported in phase III trials of patients with newly diagnosed pulmonary TB. A systematic search of databases (PubMed, MEDLINE, EMBASE, CENTRAL and LILACs) was conducted on 30 November 2017 to retrieve relevant peer-reviewed articles. Reference lists of included studies were also searched. This systematic review considered all reported outcomes. Results Of 248 included studies, 229 considered “on-treatment” outcomes whilst 148 reported “off-treatment” outcomes. There was wide variation and ambiguity in the definition of reported outcomes, including their relationship to treatment and in the time points evaluated. Additional challenges were observed regarding the analysis approach taken (per protocol versus intention to treat) and the varying durations of “intensive” and “continuation” phases of treatment. Bacteriological outcomes were most frequently reported but radiological and clinical data were often included as an implicit or explicit component of the overall definition of outcome. Conclusions Terminology used to define long-term outcomes in phase III trials is inconsistent, reflecting evolving differences in protocols and practices. For successful future cumulative meta-analysis, the findings of this review suggest that greater availability of individual patient data and the development of a core outcome set would be desirable. In the meantime, we propose a simple and logical approach which should facilitate combination of key evidence and inform improvements in the methodology of TB drug development and clinical trials

The relationship between low prolactin and type 2 diabetes

Prolactin (PRL) is secreted throughout life in men and women. At elevated levels, its physiological role in pregnancy and lactation, and pathological effects, are well known. However clinical implications of low circulating PRL are not well established. We conducted a meta-analysis to examine the relationship between low PRL levels and type 2 diabetes. Five papers included cross-sectional studies comprising 8,720 men (mean age range 51.4–60 years) and 3,429 women (49.5–61.6 years), and four papers included cohort studies comprising 2,948 men (52.1–60.0 years) and 3,203 women (49.2–60.1 years). Individuals with pregnancy, lactation and hyperprolactinemia, drugs known to alter circulating PRL levels, or pituitary diseases had been excluded. Although most studies used quartiles to categorize PRL groups for analysis, PRL cut-off values (all measured by chemiluminescence immunoassay) were variably defined between studies: the lowest PRL quartiles ranged from 3.6 ng/ml to 7.2 ng/ml in men and between 4.5 ng/ml to 8 ng/ml in women; and the highest PRL quartiles ranged from 6.9 ng/ml to 13 ng/ml in men and 9.6 ng/ml to 15.8 ng/ml in women. Type 2 diabetes was defined variably using self-reported physician’s diagnosis, fasting blood glucose, oral glucose tolerance test or glycated hemoglobin (HbA1C). In cross-sectional studies, compared to individuals in the highest PRL groups (reference), those in the lowest PRL groups had greater risk of type 2 diabetes both in men: odds ratio (OR) and 95% confidence interval = 1.86 (1.56–2.22) and in women: OR = 2.15 (1.63–2.85). In cohort studies, women showed a significant association between low PRL and type 2 diabetes: OR = 1.52 (1.02–2.28) but not men: OR = 1.44 (0.46–4.57). Relatively low heterogeneity was observed (I2 = 25–38.4%) for cross-sectional studies, but higher for cohort studies (I2 = 52.8–79.7%). In conclusion, low PRL is associated with type 2 diabetes, but discrepancy between men and women in the relationship within cohort studies requires further research

Meta-analysis of changes in the levels of catecholamines and blood pressure with continuous positive airway pressure therapy in obstructive sleep apnea

Stress from obstructive sleep apnea (OSA) stimulates catecholamine release consequently exacerbating hypertension. However, different studies have shown a conflicting impact of continuous positive airway pressure (CPAP) treatment in patients with OSA on catecholamine levels and blood pressure. We aimed to examine changes to catecholamine levels and blood pressure in response to CPAP treatment. We conducted a meta‐analysis of data published up to May 2020. The quality of the studies was evaluated using standard tools for assessing the risk of bias. Meta‐analysis was conducted using RevMan (v5.3) and expressed in standardized mean difference (SMD) for catecholamines and mean difference (MD) for systolic (SBP) and diastolic blood pressure (DBP). A total of 38 studies met our search criteria; they consisted of 14 randomized control trials (RCT) totaling 576 participants and 24 prospective cohort studies (PCS) of 547 participants. Mean age ranged between 41 and 62 year and body mass index between 27.2 and 35.1 kg/m(2). CPAP treatment reduced 24‐hour urinary noradrenaline levels both in RCT (SMD = −1.1; 95% confidence interval (CI): −1.63 to − 0.56) and in PCS (SMD = 0.38 (CI: 0.24 to 0.53). SBP was also reduced by CPAP treatment in RCT (4.8 mmHg; CI: 2.0‐7.7) and in PCS (7.5 mmHg; CI: 3.3‐11.7). DBP was similarly reduced (3.0 mmHg; CI: 1.4‐4.6) and in PCS (5.1 mmHg; CI: 2.3‐8.0). In conclusion, CPAP treatment in patients with OSA reduces catecholamine levels and blood pressure. This suggests that sympathetic activity plays an intermediary role in hypertension associated with OSA‐related stress

COVID-19 outcomes in UK centre within highest health and wealth band: a prospective cohort study.

OBJECTIVES: To describe the characteristics and outcomes of hospitalised patients with COVID-19 from UK in the highest decile of health and gross regional products per capita. DESIGN: Prospective cohort study. SETTING: Recruited all adult inpatients with laboratory-confirmed COVID-19 symptoms admitted to a single Surrey centre between March and April 2020. Extensive demographic details were documented. OUTCOME MEASURE: COVID-19 status of alive/dead and intensive care unit (ICU) status of yes/no. PARTICIPANTS: Patients with COVID-19 from Surrey centre UK (n=429). RESULTS: 429 adult inpatients (mean age 70±18 years; men 56.4%) were included in this study, of whom, 19.1% required admission to ICU and 31.9% died. Adverse outcomes were associated with age (OR with each decade of years: 1.78, 95% CI 1.53 to 2.11, p<0.001 for mortality); male gender (OR=1.08, 95% CI 0.72 to 1.63, p=0.72, present in 70.7%, of admissions to ICU versus 53% of other cases, p=0.004); cardiac disease (OR=3.43, 95% CI 2.10 to 5.63, p<0.001), diabetes mellitus (OR=2.37, 95% CI 1.09 to 5.17, p=0.028) and dementia (OR=5.06, 95% CI 2.79 to 9.44, p<0.001). There was no significant impact of ethnicity or body mass index on disease outcome. CONCLUSIONS: Despite reports of worse outcomes in deprived regions, we show similar complication and mortality rates due to COVID-19 in an affluent and high life expectancy region