scholarly journals Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
David T. J. Broderick ◽  
David W. Waite ◽  
Robyn L. Marsh ◽  
Carlos A. Camargo ◽  
Paul Cardenas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Respiratory Disease ◽  
Meta Analysis ◽  
Lower Airway ◽  
Rrna Gene ◽  
Anatomical Site ◽  
Individual Participant Data ◽  
Additional Tool ◽  
Individual Participant ◽  
Respiratory Microbiota

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.

scholarly journals Long working hours and alcohol use: systematic review and meta-analysis of published studies and unpublished individual participant data

BMJ ◽  
2015 ◽  
Vol 350 (jan12 13) ◽  
pp. g7772-g7772 ◽  
Author(s):  
M. Virtanen ◽  
M. Jokela ◽  
S. T. Nyberg ◽  
I. E. H. Madsen ◽  
T. Lallukka ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alcohol Use ◽  
Meta Analysis ◽  
Working Hours ◽  
Individual Participant Data ◽  
Long Working Hours ◽  
Individual Participant

scholarly journals Diet and physical activity in pregnancy to prevent gestational diabetes: a protocol for an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e048119
Author(s):  
Dyuti Coomar ◽  
Jonathan M Hazlehurst ◽  
Frances Austin ◽  
Charlie Foster ◽  
Graham A Hitman ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cost Effectiveness ◽  
Gestational Diabetes ◽  
Systematic Reviews ◽  
Meta Analysis ◽  
Individual Participant Data ◽  
Randomised Trials ◽  
Diet And Physical Activity ◽  
Individual Participant ◽  
In Pregnancy

IntroductionMothers with gestational diabetes mellitus (GDM) are at increased risk of pregnancy-related complications and developing type 2 diabetes after delivery. Diet and physical activity-based interventions may prevent GDM, but variations in populations, interventions and outcomes in primary trials have limited the translation of available evidence into practice. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to assess the differential effects and cost-effectiveness of diet and physical activity-based interventions in preventing GDM and its complications.MethodsThe International Weight Management in Pregnancy Collaborative Network database is a living repository of IPD from randomised trials on diet and physical activity in pregnancy identified through a systematic literature search. We shall update our existing search on MEDLINE, Embase, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment Database without language restriction to identify relevant trials until March 2021. Primary researchers will be invited to join the Network and share their IPD. Trials including women with GDM at baseline will be excluded. We shall perform a one and two stage random-effect meta-analysis for each intervention type (all interventions, diet-based, physical activity-based and mixed approach) to obtain summary intervention effects on GDM with 95% CIs and summary treatment–covariate interactions. Heterogeneity will be summarised using I2 and tau2 statistics with 95% prediction intervals. Publication and availability bias will be assessed by examining small study effects. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool, and the Grading of Recommendations, Assessment, Development and Evaluations approach will be used to grade the evidence in the results. A model-based economic analysis will be carried out to assess the cost-effectiveness of interventions to prevent GDM and its complications compared with usual care.Ethics and disseminationEthics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42020212884). Results will be submitted for publication in peer-reviewed journals.

scholarly journals A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuntao Chen ◽  
Adriaan A. Voors ◽  
Tiny Jaarsma ◽  
Chim C. Lang ◽  
Iziah E. Sama ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Decision Making ◽  
Cox Model ◽  
Meta Analysis ◽  
Prognostic Index ◽  
Individual Participant Data ◽  
Mortality Risks ◽  
Baseline Mortality ◽  
Individual Participant ◽  
Stratified Model

Abstract Background Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF. Methods Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration. Results 3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF < 40%), 588 as having HF with midrange EF (HFmrEF; EF 40–49%), and 621 as having HF with preserved EF (HFpEF; EF ≥ 50%). A total of 11 readily available variables built up the prognostic index. For four of these predictor variables, namely systolic blood pressure, serum creatinine, myocardial infarction, and diabetes, the effect differed across the three HF phenotypes. With a weighted IECV-adjusted AUC of 0.79 (0.74–0.83) for HFrEF, 0.74 (0.70–0.79) for HFmrEF, and 0.74 (0.71–0.77) for HFpEF, the model showed excellent discrimination. Moreover, there was a good agreement between the average observed and predicted 1-year mortality risks, especially after recalibration of the baseline mortality risks. Conclusions Our HF phenotype stratified model showed excellent generalizability across four European cohorts and may provide a useful tool in HF phenotype-specific clinical decision-making.

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