Case weighted power priors for hybrid control analyses with time-to-event data.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

Estimating treatment effect in randomized trial after control to treatment crossover using external controls.

In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.

Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial.

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different commensurate priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and the potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings, we recommend more simulation studies.

An Evaluation of Gene-Diet Interaction Statistical Methods and Discovery of rs7175421-Whole Grain Interaction in Lung Cancer.

Dietary factors show different effects on genetically diverse populations. Scientific research uses gene-environment interaction models to study the effects of dietary factors on genetically diverse populations for lung cancer risk. However, previous study designs have not investigated the degree of type I error inflation and, in some instances, have not corrected for multiple testing. Using a motivating investigation of diet-gene interaction and lung cancer risk, we propose a training and testing strategy and perform real-world simulations to select the appropriate statistical methods to reduce false-positive discoveries. The simulation results show that the unconstrained maximum likelihood (UML) method controls the type I error better than the constrained maximum likelihood (CML). The empirical Bayesian (EB) method can compete with the UML method in achieving statistical power and controlling type I error. We observed a significant interaction between SNP rs7175421 with dietary whole grain in lung cancer prevention, with an effect size (standard error) of -0.312 (0.112) for EB estimate. SNP rs7175421 may interact with dietary whole grains in modulating lung cancer risk. Evaluating statistical methods for gene-diet interaction analysis can help balance the statistical power and type I error.

Covariate handling approaches in combination with dynamic borrowing for hybrid control studies.

Borrowing data from external control has been an appealing strategy for evidence synthesis when conducting randomized controlled trials (RCTs). Often named hybrid control trials, they leverage existing control data from clinical trials or potentially real-world data (RWD), enable trial designs to allocate more patients to the novel intervention arm, and improve the efficiency or lower the cost of the primary RCT. Several methods have been established and developed to borrow external control data, among which the propensity score methods and Bayesian dynamic borrowing framework play essential roles. Noticing the unique strengths of propensity score methods and Bayesian hierarchical models, we utilize both methods in a complementary manner to analyze hybrid control studies. In this article, we review methods including covariate adjustments, propensity score matching and weighting in combination with dynamic borrowing and compare the performance of these methods through comprehensive simulations. Different degrees of covariate imbalance and confounding are examined. Our findings suggested that the conventional covariate adjustment in combination with the Bayesian commensurate prior model provides the highest power with good type I error control under the investigated settings. It has desired performance especially under scenarios of different degrees of confounding. To estimate efficacy signals in the exploratory setting, the covariate adjustment method in combination with the Bayesian commensurate prior is recommended.

Analysis of Survival Benefits of Living Versus Deceased Donor Liver Transplant in High Model for End-Stage Liver Disease and Hepatorenal Syndrome.

BACKGROUND AND AIMS: Previous recommendations suggested living donor liver transplantation (LDLT) should not be considered for patients with Model for End-Stage Liver Disease (MELD) > 25 and hepatorenal syndrome (HRS). APPROACH AND RESULTS: Patients who were listed with MELD > 25 from 2008 to 2017 were analyzed with intention-to-treat (ITT) basis retrospectively. Patients who had a potential live donor were analyzed as ITT-LDLT, whereas those who had none belonged to ITT-deceased donor liver transplantation (DDLT) group. ITT-overall survival (OS) was analyzed from the time of listing. Three hundred twenty-five patients were listed (ITT-LDLT n = 212, ITT-DDLT n = 113). The risk of delist/death was lower in the ITT-LDLT group (43.4% vs. 19.8%, P < 0.001), whereas the transplant rate was higher in the ITT-LDLT group (78.3% vs. 52.2%, P < 0.001). The 5-year ITT-OS was superior in the ITT-LDLT group (72.6% vs. 49.5%, P < 0.001) for patients with MELD > 25 and patients with both MELD > 25 and HRS (56% vs. 33.8%, P < 0.001). Waitlist mortality was the highest early after listing, and the distinct alteration of slope at survival curve showed that the benefits of ITT-LDLT occurred within the first month after listing. Perioperative outcomes and 5-year patient survival were comparable for patients with MELD > 25 (88% vs. 85.4%, P = 0.279) and patients with both MELD > 25 and HRS (77% vs. 76.4%, P = 0.701) after LDLT and DDLT, respectively. The LDLT group has a higher rate of renal recovery by 1 month (77.4% vs. 59.1%, P = 0.003) and 3 months (86.1% vs, 74.5%, P = 0.029), whereas the long-term estimated glomerular filtration rate (eGFR) was similar between the 2 groups. ITT-LDLT reduced the hazard of mortality (hazard ratio = 0.387-0.552) across all MELD strata. CONCLUSIONS: The ITT-LDLT reduced waitlist mortality and allowed an earlier access to transplant. LDLT in patients with high MELD/HRS was feasible, and they had similar perioperative outcomes and better renal recovery, whereas the long-term survival and eGFR were comparable with DDLT. LDLT should be considered for patients with high MELD/HRS, and the application of LDLT should not be restricted with a MELD cutoff.

Prospective Study of Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma on Waitlist for Liver Transplant.

BACKGROUND AND AIMS: There are no prospective data on stereotactic body radiation therapy (SBRT) as a bridge to liver transplantation for HCC. This study aimed to evaluate the efficacy and safety of SBRT as bridging therapy, with comparison with transarterial chemoembolization (TACE) and high-intensity focused ultrasound (HIFU). APPROACH AND RESULTS: Patients were prospectively enrolled for SBRT under a standardized protocol from July 2015 and compared with a retrospective cohort of patients who underwent TACE or HIFU from 2010. The primary endpoint was tumor control rate at 1 year after bridging therapy. Secondary endpoints included cumulative incidence of dropout, toxicity, and posttransplant survival. During the study period, 150 patients were evaluated (SBRT, n = 40; TACE, n = 59; HIFU, n = 51). The tumor control rate at 1 year was significantly higher after SBRT compared with TACE and HIFU (92.3%, 43.5%, and 33.3%, respectively; P = 0.02). With competing risk analysis, the cumulative incidence of dropout at 1 and 3 years after listing was lower after SBRT (15.1% and 23.3%) compared with TACE (28.9% and 45.8%; P = 0.034) and HIFU (33.3% and 45.1%; P = 0.032). Time-to-progression at 1 and 3 years was also superior after SBRT (10.8%, 18.5% in SBRT, 45%, 54.9% in TACE, and 47.6%, 62.8% in HIFU; P < 0.001). The periprocedural toxicity was similar, without any difference in perioperative complications and patient and recurrence-free survival rates after transplant. Pathological complete response was more frequent after SBRT compared with TACE and HIFU (48.1% vs. 25% vs. 17.9%, respectively; P = 0.037). In multivariable analysis, tumor size <3 cm, listing alpha-fetoprotein <200 ng/mL, Child A, and SBRT significantly reduced the risk of dropout. CONCLUSIONS: SBRT was safe, with a significantly higher tumor control rate, reduced the risk of waitlist dropout, and should be used as an alternative to conventional bridging therapies.

A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity.

BACKGROUND: Amiodarone is one of the most commonly used anti-arrhythmic agents. Amiodarone pulmonary toxicity is a potentially fatal adverse effect associated with amiodarone use. Previous studies on the epidemiology and risk factors for amiodarone pulmonary toxicity showed diverse results. METHODS: A multicenter retrospective cohort study was conducted to identify clinic-epidemiologic markers associated with amiodarone pulmonary toxicity for development of a prediction rule. Patients taking amiodarone who were managed in 3 centres in Hong Kong from 2005 to 2015 were included in this study. Penalized logistic regression was used to model the outcome as it is rare. RESULTS: A total of 34 cases with amiodarone pulmonary toxicity were identified among 1786 patients taking amiodarone for at least 90 days from 2005 to 2015. The incidence of amiodarone pulmonary toxicity was estimated to be 1.9%. The risk factors for amiodarone pulmonary toxicity included advanced age (OR 1.047, 95% CI 1.010-1.085, p = 0.013), ventricular arrhythmia (OR 2.703, 95% CI 1.053-6.935, p = 0.039), underlying lung disease (OR 2.511, 95% CI 1.146-5.501, p = 0.021) and cumulative dose of amiodarone (OR 4.762, 95% CI 1.310-17.309 p = 0.018). CONCLUSIONS: The incidence of amiodarone pulmonary toxicity in Chinese patients in Hong Kong is estimated to be 1.9% in this study. Age, underlying lung disease, ventricular arrhythmia and cumulative dose of amiodarone are associated with the development of amiodarone pulmonary toxicity. A prediction rule was developed to inform the risk of developing amiodarone pulmonary toxicity.

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.

The 12-month prevalence of psychotic experiences and their association with clinical outcomes in Hong Kong: an epidemiological and a 2-year follow up studies.

BACKGROUND: The relationship between the subtypes of psychotic experiences (PEs) and common mental health symptoms remains unclear. The current study aims to establish the 12-month prevalence of PEs in a representative sample of community-dwelling Chinese population in Hong Kong and explore the relationship of types of PEs and common mental health symptoms. METHOD: This is a population-based two-phase household survey of Chinese population in Hong Kong aged 16-75 (N = 5719) conducted between 2010 and 2013 and a 2-year follow-up study of PEs positive subjects (N = 152). PEs were measured with Psychosis Screening Questionnaire (PSQ) and subjects who endorsed any item on the PSQ without a clinical diagnosis of psychotic disorder were considered as PE-positive. Types of PEs were characterized using a number of PEs (single v. multiple) and latent class analysis. All PE-positive subjects were assessed with common mental health symptoms and suicidal ideations at baseline and 2-year follow-up. PE status was also assessed at 2-year follow-up. RESULTS: The 12-month prevalence of PEs in Hong Kong was 2.7% with 21.1% had multiple PEs. Three latent classes of PEs were identified: hallucination, paranoia and mixed. Multiple PEs and hallucination latent class of PEs were associated with higher levels of common mental health symptoms. PE persistent rate at 2-year follow-up was 15.1%. Multiple PEs was associated with poorer mental health at 2-year follow-up. CONCLUSIONS: Results highlighted the transient and heterogeneous nature of PEs, and that multiple PEs and hallucination subtype of PEs may be specific indices of poorer common mental health.

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

Endpoint surrogacy in oncology Phase 3 randomised controlled trials.

Endpoint surrogacy is an important concept in oncology trials. Using a surrogate endpoint like progression-free survival as the primary endpoint-instead of overall survival-would lead to a potential faster drug approval and therefore more cancer patients with an earlier opportunity to receive the newly approved drugs.

Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.

PURPOSE: Biomedical data frequently contain imbalance characteristics which make achieving good predictive performance with data-driven machine learning approaches a challenging task. In this study, we investigated the impact of re-sampling techniques for imbalanced datasets in PET radiomics-based prognostication model in head and neck (HNC) cancer patients. METHODS: Radiomics analysis was performed in two cohorts of patients, including 166 patients newly diagnosed with nasopharyngeal carcinoma (NPC) in our centre and 182 HNC patients from open database. Conventional PET parameters and robust radiomics features were extracted for correlation analysis of the overall survival (OS) and disease progression-free survival (DFS). We investigated a cross-combination of 10 re-sampling methods (oversampling, undersampling, and hybrid sampling) with 4 machine learning classifiers for survival prediction. Diagnostic performance was assessed in hold-out test sets. Statistical differences were analysed using Monte Carlo cross-validations by post hoc Nemenyi analysis. RESULTS: Oversampling techniques like ADASYN and SMOTE could improve prediction performance in terms of G-mean and F-measures in minority class, without significant loss of F-measures in majority class. We identified optimal PET radiomics-based prediction model of OS (AUC of 0.82, G-mean of 0.77) for our NPC cohort. Similar findings that oversampling techniques improved the prediction performance were seen when this was tested on an external dataset indicating generalisability. CONCLUSION: Our study showed a significant positive impact on the prediction performance in imbalanced datasets by applying re-sampling techniques. We have created an open-source solution for automated calculations and comparisons of multiple re-sampling techniques and machine learning classifiers for easy replication in future studies.

Ten-year employment patterns of patients with first-episode schizophrenia-spectrum disorders: comparison of early intervention and standard care services.

BACKGROUND: Little is known about long-term employment outcomes for patients with first-episode schizophrenia-spectrum (FES) disorders who received early intervention services. AIMS: We compared the 10-year employment trajectory of patients with FES who received early intervention services with those who received standard care. Factors differentiating the employment trajectories were explored. METHOD: Patients with FES (N = 145) who received early intervention services in Hong Kong between 1 July 2001 and 30 June 2002 were matched with those who entered standard care 1 year previously. We used hierarchical clustering analysis to explore the 10-year employment clusters for both groups. We used the mixed model test to compare cluster memberships and piecewise regression analysis to compare the employment trajectories of the two groups. RESULTS: There were significantly more patients who received the early intervention service in the good employment cluster (early intervention: N = 98 [67.6%]; standard care: N = 76 [52.4%]; P = 0.009). In the poor employment cluster, there was a significant difference in the longitudinal pattern between early intervention and standard care for years 1-5 (P < 0.0001). The number of relapses during the first 3 years, months of full-time employment during the first year and years of education were significant in differentiating the clusters of the early intervention group. CONCLUSIONS: Results suggest there was an overall long-term benefit of early intervention services on employment. However, the benefit was not sustained for all patients. Personalisation of the duration of the early intervention service with a focus on relapse prevention and early vocational reintegration should be considered for service enhancement.

Longitudinal Changes in Skin Microbiome Associated with Change in Skin Status in Patients with Psoriasis.

The aim of this study was to identify key microbes associated with change in skin status (lesional vs normal). Longitudinal changes in the skin microbiome between patients with psoriasis and healthy family controls living in the same household were studied using whole genome metagenomic shotgun sequencing at 4 time-points. There were significant changes in abundance of the pathogen Campylobacter jejuni and its higher taxonomic levels when the skin status of patients with psoriasis changed. There were significant longitudinal variations in alpha diveristy (p < 0.001) and beta diversity (p < 0.05) of the skin microbiome in patients with psoriasis, but not in the healthy control group, which indicated composition of skin microbiome in patients with psoriasis was different from healthy control and was dynamically less stable. This study will serve as the basis for future temporal studies of the skin microbiome and probiotic therapeutics.

A pooled analysis of individual patient data from National Clinical Trials Network clinical trials of concurrent chemoradiotherapy for limited-stage small cell lung cancer in elderly patients versus younger patients.

BACKGROUND: Platinum and etoposide with thoracic radiation followed by prophylactic cranial irradiation constitute the standard treatment for limited-stage small cell lung cancer (LS-SCLC). Many patients with LS-SCLC are elderly with comorbidities. METHODS: Individual patient data were collected from 11 phase 2 or 3 trials for LS-SCLC conducted by the National Clinical Trials Network and activated from 1990 to 2010. The primary endpoint was overall survival (OS); the secondary endpoints were progression-free survival (PFS), the rate of severe adverse events, and off-treatment reasons. The outcomes were compared for patients 70 years old or older (elderly patients) and patients younger than 70 years (younger patients). RESULTS: Individual patient data from 1049 younger patients (81%) and 254 elderly patients (19%) were analyzed. In the multivariate model, elderly patients, in comparison with younger patients, had worse OS (hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.18-1.63; median OS for elderly patients, 17.8 months; OS for younger patients, 23.5 months) and worse PFS (HR, 1.19; 95% CI, 1.03-1.39; median PFS for elderly patients, 10.6 months; median PFS for younger patients, 12.3 months). Elderly patients, in comparison with younger patients, experienced more grade 5 adverse events (8% vs 3%; P < .01) and more grade 3 or higher dyspnea (11% vs 7%; P = .03) but less grade 3 or higher esophagitis/dysphagia (14% vs 19%; P = .04) and less grade 3 or higher vomiting (11% vs 17%; P = .01). Elderly patients completed treatment less often, discontinued treatment because of adverse events and patient refusal more frequently, and died during treatment more frequently. CONCLUSIONS: Elderly patients with LS-SCLC have worse PFS and OS and more difficulty in tolerating therapy. Future trials should incorporate assessments of elderly patients, novel monitoring of adverse events, and more tolerable radiation and systemic therapies.

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore improving the cervical cancer detection specificity of Onclarity by machine learning. We tested 605 stratified random archived samples of cervical liquid-based cytology samples with both assays. All samples had biopsy diagnosis or repeated negative cytology follow-up. Association rule mining (ARM) was employed to discover coinfection likely to give rise to CIN2/3+. Outcome classifiers interpreting the extended genotyping results of Onclarity were built with different underlying models. The sensitivities (Onclarity, 96.32%; Cobas, 95.71%) and specificities (Onclarity, 46.38%; Cobas, 45.25%) of the high-risk HPV (hrHPV) components of the two tests were not significantly different. When HPV16 and HPV18 were used to further interpret hrHPV-positive cases, Onclarity displayed significantly higher specificity (Onclarity, 87.10%; Cobas, 80.77%). Both hrHPV tests achieved the same sensitivities (Onclarity, 90.91%; Cobas, 90.91%) and similar specificities (Onclarity, 48.46%; Cobas, 51.98%) when used for triaging atypical squamous cells of undetermined significance. Positivity in both HPV16 and HPV33/58 of the Onclarity channels entails the highest probability of developing CIN2/3+. Incorporating other hrHPVs into the outcome classifiers improved the specificity of identifying CIN2/3 to up to 94.32%. The extended genotyping of Onclarity therefore can help to highlight patients having the highest risk of developing CIN2/3+, with the potential to reduce unnecessary colposcopy and negative psychosocial impact on women receiving the reports.

Available evidence of antibiotic resistance from extended-spectrum ß-lactamase-producing Enterobacteriaceae in paediatric patients in 20 countries: a systematic review and meta-analysis.

OBJECTIVE: To make a systematic review of risk factors, outcomes and prevalence of extended-spectrum ß-lactamase-associated infection in children and young adults in South-East Asia and the Western Pacific. METHODS: Up to June 2018 we searched online databases for published studies of infection with extended-spectrum ß-lactamase-producing Enterobacteriaceae in individuals aged 0-21 years. We included case-control, cohort, cross-sectional and observational studies reporting patients positive and negative for these organisms. For the meta-analysis we used random-effects modelling of risk factors and outcomes for infection, and meta-regression for analysis of subgroups. We mapped the prevalence of these infections in 20 countries and areas using available surveillance data. FINDINGS: Of 6665 articles scanned, we included 40 studies from 11 countries and areas in the meta-analysis. The pooled studies included 2411 samples testing positive and 2874 negative. A higher risk of infection with extended-spectrum ß-lactamase-producing bacteria was associated with previous hospital care, notably intensive care unit stays (pooled odds ratio, OR: 6.5; 95% confidence interval, CI: 3.04 to 13.73); antibiotic exposure (OR: 4.8; 95% CI: 2.25 to 10.27); and certain co-existing conditions. Empirical antibiotic therapy was protective against infection (OR: 0.29; 95% CI: 0.11 to 0.79). Infected patients had longer hospital stays (26 days; 95% CI: 12.81 to 38.89) and higher risk of death (OR: 3.2; 95% CI: 1.82 to 5.80). The population prevalence of infection was high in these regions and surveillance data for children were scarce. CONCLUSION: Antibiotic stewardship policies to prevent infection and encourage appropriate treatment are needed in South-East Asia and the Western Pacific.

Next-generation sequencing with a 54-gene panel identified unique mutational profile and prognostic markers in Chinese patients with myelofibrosis.

Current prognostication in myelofibrosis (MF) is based on clinicopathological features and mutations in a limited number of driver genes. The impact of other genetic mutations remains unclear. We evaluated for mutations in a myeloid panel of 54 genes using next-generation sequencing. Multivariate Cox regression analysis was used to determine prognostic factors for overall survival (OS) and leukaemia-free survival (LFS), based on mutations of these genes and relevant clinical and haematological features. One hundred and one patients (primary MF, N = 70; secondary MF, N = 31) with a median follow-up of 49 (1-256) months were studied. For the entire cohort, inferior OS was associated with male gender (P = 0.04), age > 65 years (P = 0.04), haemoglobin < 10 g/dL (P = 0.001), CUX1 mutation (P = 0.003) and TP53 mutation (P = 0.049); and inferior LFS was associated with male gender (P = 0.03), haemoglobin < 10 g/dL (P = 0.04) and SRSF2 mutations (P = 0.008). In primary MF, inferior OS was associated with male gender (P = 0.03), haemoglobin < 10 g/dL (P = 0.002), platelet count < 100 × 109/L (P = 0.02), TET2 mutation (P = 0.01) and CUX1 mutation (P = 0.01); and inferior LFS was associated with haemoglobin < 10 g/dL (P = 0.02), platelet count < 100 × 109/L (P = 0.02), TET2 mutations (P = 0.01) and CUX1 mutations (P = 0.04). These results showed that clinical and haematological features and genetic mutations should be considered in MF prognostication.

Change in moderate alcohol consumption and quality of life: evidence from 2 population-based cohorts.

BACKGROUND: Although the association of moderate alcohol consumption with specific disorders, such as cardiovascular disease and cancers, has been well documented, the evidence of the broader impact of alcohol consumption on health-related quality of life is less clear. Our objective was to examine the association of drinking patterns with changes in physical and mental well-being across populations. METHODS: We conducted a multilevel analysis with multivariate responses in the population-representative FAMILY Cohort in the Hong Kong Special Administrative Region, China, to examine the association between alcohol drinking patterns across 2 waves (2009-2013) (i.e., quitters, initiators, persistent drinkers, persistent former drinkers and lifetime abstainers) and changes in physical and mental well-being (Physical and Mental Component Summary of the 12-Item Short Form Health Survey [SF-12]). Analyses were stratified by sex. We validated findings using a nationally representative cohort in the United States, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, 2001-2005). RESULTS: In the FAMILY Cohort (n = 10 386; median follow-up 2.3 yr), the change in mental well-being was more favourable in female quitters than in lifetime abstainers (ß = 1.44, 95% confidence interval [CI] 0.43 to 2.45; mean score change of +2.0 for quitters and +0.02 for lifetime abstainers). This association was validated in the NESARC (n = 31 079; median follow-up 3.1 yr) (ß = 0.83, 95% CI 0.08 to 1.58; mean score change of -1.1 for quitters and -1.6 for lifetime abstainers). INTERPRETATION: The change in mental well-being was more favourable in female quitters, approaching the level of mental well-being of lifetime abstainers within 4 years of quitting in both Chinese and American populations.