Resuscitation for out-of-hospital cardiac arrest in Ireland 2012-2020: Modelling national temporal developments and survival predictors.

Aim: To explore potential predictors of national out-of-hospital cardiac arrest (OHCA) survival, including health system developments and the COVID pandemic in Ireland. Methods: National level OHCA registry data from 2012 through to 2020, relating to unwitnessed, and bystander witnessed OHCA were interrogated. Logistic regression models were built by including predictors through stepwise variable selection and enhancing the models by adding pairwise interactions that improved fit. Missing data sensitivity analyses were conducted using multiple imputation. Results: The data included 18,177 cases. The final model included seventeen variables. Of these nine variables were involved in pairwise interactions. The COVID-19 period was associated with reduced survival (OR 0.61, 95%CI 0.43, 0.87), as were increasing age in years (OR 0.96, 95% CI 0.96, 0.97) and call response interval in minutes (OR 0.97, 95% CI 0.96, 0.99). Amiodarone administration (OR 3.91, 95% CI 2.80, 5.48), urban location (OR 1.40, 95% CI 1.12, 1.77), and chronological year over time (OR 1.14, 95% CI 1.08, 1.20) were associated with increased survival. Conclusions: National survival from OHCA has significantly increased incrementally over time in Ireland. The COVID-19 pandemic was associated with decreased survival even after accounting for potential disruption to key elements of bystander and EMS care. Further research is needed to understand and address the discrepancy between urban and rural OHCA survival. Information concerning pre-event patient health status and inpatient care process may yield important additional insights in future.

A machine learning approach to predict mortality due to immune-mediated thrombotic thrombocytopenic purpura.

Background: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. Objectives: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index. Methods: We analyzed variables available at the time of initial presentation, including demographics, symptoms, and laboratory findings. We developed our model using gradient boosting machine, a machine learning ensemble method based on classification trees, implemented in the R package gbm. Results: In our cohort (n = 419), the French score predicted mortality with an area under the receiver operating characteristic curve of 0.63 (95% CI: 0.50-0.77), sensitivity of 0.35, and specificity of 0.84. Our gradient boosting machine model selected 8 variables to predict acute mortality with a cross-validated area under the receiver operating characteristic curve of 0.77 (95% CI: 0.71-0.82). The 2 cutoffs corresponded to sensitivities of 0.64 and 0.50 and specificities of 0.76 and 0.87, respectively. Conclusion: The USTMA Mortality Index was acceptable for predicting mortality due to acute iTTP in the USTMA registry, but not sensitive enough to rule out death. Identifying patients at high risk of iTTP-related mortality may help individualize care and ultimately improve iTTP survival outcomes. Further studies are needed to provide external validation. Our model is one of many recent examples where machine learning models may show promise in clinical prediction tools in healthcare.

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature.

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

Challenges and Concerns in the Utilization of Meta-Analysis Software: Navigating the Landscape of Scientific Synthesis.

Meta-analysis has emerged as a pivotal tool for synthesizing evidence in scientific research, facilitated by the advent of meta-analysis software. While these tools have significantly streamlined the synthesis process, challenges and concerns persist, impacting the reliability and validity of meta-analytic findings. This editorial addresses key issues in the use of meta-analysis software, including heterogeneity, publication bias, data quality, model dependence, and user competence. As the scientific community increasingly relies on meta-analytic approaches, collaborative efforts are needed to establish standardized reporting guidelines, enhance data quality, and improve transparency. This study highlights the importance of addressing these challenges to ensure the continued evolution of meta-analysis as a robust and informative method for evidence synthesis in scientific research.

Performance of the IMPACT and CRASH prognostic models for traumatic brain injury in a contemporary multicenter cohort: a TRACK-TBI study.

OBJECTIVE: The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) prognostic models for mortality and outcome after traumatic brain injury (TBI) were developed using data from 1984 to 2004. This study examined IMPACT and CRASH model performances in a contemporary cohort of US patients. METHODS: The prospective 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years 2014-2018) enrolled subjects aged ≥ 17 years who presented to level I trauma centers and received head CT within 24 hours of TBI. Data were extracted from the subjects who met the model criteria (for IMPACT, Glasgow Coma Scale [GCS] score 3-12 with 6-month Glasgow Outcome Scale-Extended [GOSE] data [n = 441]; for CRASH, GCS score 3-14 with 2-week mortality data and 6-month GOSE data [n = 831]). Analyses were conducted in the overall cohort and stratified on the basis of TBI severity (severe/moderate/mild TBI defined as GCS score 3-8/9-12/13-14), age (17-64 years or ≥ 65 years), and the 5 top enrolling sites. Unfavorable outcome was defined as GOSE score 1-4. Original IMPACT and CRASH model coefficients were applied, and model performances were assessed by calibration (intercept [< 0 indicated overprediction; > 0 indicated underprediction] and slope) and discrimination (c-statistic). RESULTS: Overall, the IMPACT models overpredicted mortality (intercept -0.79 [95% CI -1.05 to -0.53], slope 1.37 [1.05-1.69]) and acceptably predicted unfavorable outcome (intercept 0.07 [-0.14 to 0.29], slope 1.19 [0.96-1.42]), with good discrimination (c-statistics 0.84 and 0.83, respectively). The CRASH models overpredicted mortality (intercept -1.06 [-1.36 to -0.75], slope 0.96 [0.79-1.14]) and unfavorable outcome (intercept -0.60 [-0.78 to -0.41], slope 1.20 [1.03-1.37]), with good discrimination (c-statistics 0.92 and 0.88, respectively). IMPACT overpredicted mortality and acceptably predicted unfavorable outcome in the severe and moderate TBI subgroups, with good discrimination (c-statistic ≥ 0.81). CRASH overpredicted mortality in the severe and moderate TBI subgroups and acceptably predicted mortality in the mild TBI subgroup, with good discrimination (c-statistic ≥ 0.86); unfavorable outcome was overpredicted in the severe and mild TBI subgroups with adequate discrimination (c-statistic ≥ 0.78), whereas calibration was nonlinear in the moderate TBI subgroup. In subjects ≥ 65 years of age, the models performed variably (IMPACT-mortality, intercept 0.28, slope 0.68, and c-statistic 0.68; CRASH-unfavorable outcome, intercept -0.97, slope 1.32, and c-statistic 0.88; nonlinear calibration for IMPACT-unfavorable outcome and CRASH-mortality). Model performance differences were observed across the top enrolling sites for mortality and unfavorable outcome. CONCLUSIONS: The IMPACT and CRASH models adequately discriminated mortality and unfavorable outcome. Observed overestimations of mortality and unfavorable outcome underscore the need to update prognostic models to incorporate contemporary changes in TBI management and case-mix. Investigations to elucidate the relationships between increased survival, outcome, treatment intensity, and site-specific practices will be relevant to improve models in specific TBI subpopulations (e.g., older adults), which may benefit from the inclusion of blood-based biomarkers, neuroimaging features, and treatment data.

Estimating the probability of occurrence of African dust outbreaks over regions of the western Mediterranean basin from thermodynamic atmospheric parameters.

Desert dust is currently recognized as a health risk factor. Therefore, the World Health Organization (WHO) is actively promoting the establishment of early warning systems for sand and dust storms. This study introduces a methodology to estimate the probability of African dust outbreaks occurring in eight different regions of the Iberian Peninsula and the Balearic Islands. In each region, a multilinear regression model was developed to calculate daily probabilities of dust events using three thermodynamic variables (geopotential thickness in the 1000-500 hPa layer, mean potential temperature between 925 and 700 hPa, and temperature anomalies at 850 hPa) as assessment parameters. All days with African dust transport over each study region were identified in the period 2001-2021 using a proven procedure. This information was then utilized to establish a functional relationship between the values of the thermodynamic parameters and the probability of African dust outbreaks occurring. The validation of this methodology involved comparing the daily probabilities of dust events generated by the models in 2001-2021 with the daily African dust contributions to PM10 regional background levels in each region. On average, daily dust contributions increased proportionally with the increase in daily probabilities, reaching zero for days with low probabilities. Furthermore, a well-defined seasonal evolution of probability values was observed in all regions, with the highest values in the summer months and the lowest in the winter period, ensuring the physical relevance of the models' results. Finally, upward trends were observed in all regions for the three thermodynamic parameters over 1940-2021. Thus, the probability of dust events development also increased in this period. It demonstrates that the aggravation of warm conditions in southern Europe in the last decades, have modified the frequency of North-African dust outbreaks over the western Mediterranean basin.

Improving Reporting of Exercise Capacity Across Age Ranges Using Novel Workload Reference Equations.

Exercise capacity (EC) is an important predictor of survival in the general population and in subjects with cardiopulmonary disease. Despite its relevance, considering the percent-predicted workload (%pWL) given by current equations may overestimate EC in older adults. Therefore, to improve the reporting of EC in clinical practice, our main objective was to develop workload reference equations (pWL) that better reflect the relation between workload and age. Using the Fitness Registry and the Importance of Exercise National Database (FRIEND), we analyzed a reference group of 6,966 apparently healthy participants and 1,060 participants with heart failure who underwent graded treadmill cardiopulmonary exercise testing. For the first group, the mean age was 44 years (18 to 79); 56.5% of participants were males and 15.4% had obesity. Peak oxygen consumption was 11.6 ± 3.0 METs in males and 8.5 ± 2.4 METs in females. After partition analysis, we first developed sex-specific pWL equations to allow comparisons to a healthy weight reference. For males, pWL (METs) = 14.1-0.9×10-3×age2 and 11.5-0.87×10-3×age2 for females. We used those equations as denominators of %pWL, and based on their distribution, we determined thresholds for EC classification, with average EC defined by the range corresponding to 85% to 115%pWL. Compared with %pWL using current equations, the new equations yielded better-calibrated %pWL across different age ranges. We also derived body mass index-adjusted pWL equations that better assessed EC in subjects with heart failure. In conclusion, the novel pWL equations have the potential to impact the report of EC in practice.

Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.

Spatio-temporal dynamics of visceral leishmaniasis in Brazil: A nonlinear regression analysis.

AIMS: This study describes the spatio-temporal dynamics of new visceral leishmaniasis (VL) cases notified in Brazil between 2001 and 2020. METHODS AND RESULTS: Data on the occurrence of the disease were obtained by means of the Notifiable Diseases Information System of the Brazilian Ministry of Health. Joinpoint, temporal generalized additive models and conditional autoregressive (CAR) models were used to analyse the temporal evolution of the rates in Brazil, states and regions. Spatio-temporal generalized additive and CAR models were used to identify the distribution of annual risks of VL occurrence in the Brazilian territory in relation to variation in the spatial average. There were 63,966 VL cases in the target period (3.198 cases/year), corresponding to a mean incidence rate of 1.68 cases/100,000 inhabitants. Of these, 4451 resulted in deaths, which gives a mean mortality rate of 0.12 deaths/100,000 inhabitants and a case fatality of 6.96%. The highest incidence rate was found in the North region, followed closely by the Northeast region, which presented the second and first highest mortality rates, respectively. For all of Brazil, and in the Northeast region, there were stability in the incidence rates, while the other regions showed an increasing trend in different time segments in the period: Central-West up to 2011, North up to 2008, Southeast up to 2004, and South up to 2010. On the other hand, all regions experienced a reduction in incidence rate during the last years of the series. The Northeast region had the highest number of municipalities with statistically significant elevated relative risks. The spatio-temporal analysis showed the highest risk area predominantly in the Northeast region in the beginning of the time series. From 2002 to 2018, this area expanded to the interior of the country. CONCLUSIONS: The present study has shown that VL has expanded in Brazil. However, the North and Northeast regions continue to have the highest incidence, and the risk of infection has decreased in recent years.

Isotemporal substitution analysis of time between sleep, sedentary behavior, and physical activity on depressive symptoms in older adults: a cross-sectional study

ABSTRACT BACKGROUND: Compared to young individuals, older adults participate more in sedentary behavior (SB) and less in physical activity (PA). These behaviors are associated with numerous adverse health factors. OBJECTIVE: The purpose of the study was to examine the hypothetical effects of substituting time spent sleeping, performing SB, and performing moderate-to-vigorous physical activity (MVPA) on depressive symptomatology in older adults. DESIGN AND SETTING: An analytical cross-sectional study employing exploratory survey methods was conducted in the city of Alcobaça in the state of Bahia, Brazil METHODS: The study included 473 older adults who answered a structured questionnaire during an interview. Exposure time to SB and PA level were assessed using the International Physical Activity Questionnaire, and depressive symptoms were analyzed using the short version of the Geriatric Depression Scale. An isotemporal replacement model was used to evaluate the effects of different SB sessions on depressive symptomatology. RESULTS: An increase in the risk of depressive symptoms was observed when MVPA and sleep time were substituted for the same SB time at all times tested, with maximum values of 40% and 20%, respectively. Opposite substitution of MVPA and sleep time increments reduced the risk of depressive symptomatology by 28% and 17%, respectively. CONCLUSIONS: The results of the present study indicate that replacing SB with the same amount of sleep or MVPA may reduce depressive symptoms. The longer the reallocation time, the greater are the benefits.

Multistep estimators of the between-study covariance matrix under the multivariate random-effects model for meta-analysis.

A wide variety of methods are available to estimate the between-study variance under the univariate random-effects model for meta-analysis. Some, but not all, of these estimators have been extended so that they can be used in the multivariate setting. We begin by extending the univariate generalised method of moments, which immediately provides a wider class of multivariate methods than was previously available. However, our main proposal is to use this new type of estimator to derive multivariate multistep estimators of the between-study covariance matrix. We then use the connection between the univariate multistep and Paule-Mandel estimators to motivate taking the limit, where the number of steps tends toward infinity. We illustrate our methodology using two contrasting examples and investigate its properties in a simulation study. We conclude that the proposed methodology is a fully viable alternative to existing estimation methods, is well suited to sensitivity analyses that explore the use of alternative estimators, and should be used instead of the existing DerSimonian and Laird-type moments based estimator in application areas where data are expected to be heterogeneous. However, multistep estimators do not seem to outperform the existing estimators when the data are more homogeneous. Advantages of the new multivariate multistep estimator include its semi-parametric nature and that it is computationally feasible in high dimensions. Our proposed estimation methods are also applicable for multivariate random-effects meta-regression, where study-level covariates are included in the model.

Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies.

BACKGROUND: Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. METHODS: Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. RESULTS: The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. CONCLUSION: This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.

Los determinantes de la estancia hospitalaria en un hospital universitario de alta complejidad

Objetivo: Establecer y cuantificar los determinantes de la estancia hospitalaria en un hospital universitario de Medellín de alta complejidad de Medellín, entre 2013 y 2018, valorar su importancia y modelar la estancia esperada. Metodología: Estudio observacional analítico retrospectivo de datos agregados. Siguiendo el método paso a paso, se corrieron siete modelos con estancia hospitalaria media como variable dependiente y las respectivas variables independientes: complejidad, oportunidad de apoyos diagnósticos, disponibilidad de insumos, casos de estancia prolongada y capacidad financiera. Se seleccionó el mejor modelo usando los criterios de ajuste Akaike e información Bayesiana, junto con las medidas de significancia global y significancia individual de los coeficientes. Se realizaron pruebas estadísticas de validez del modelo y se calcularon los coeficientes estandarizados. Resultados: Los valores medios de las variables más relevantes y su desviación estándar (de) fueron: estancia hospitalaria media, 8,09 días (de = 0,40); complejidad por consumo de recursos, 1,28 unidades (de = 0,07); apoyos diagnósticos, 90,74 mil estudios (de = 10,05); casos de estancia extrema, 4,36 % (de = 0,70), y complejidad por casuística, 1 (de = 0,03). Significancia global F = 55,2, p< 0,001. Significancia de los coeficientes: complejidad por consumo de recursos, p< 0,01; apoyos diagnósticos y casos de estancia extrema, p< 0,001; complejidad por casuística, p< 0,05. Coeficientes estandarizados: complejidad por consumo de recursos, 0,35; apoyos diagnósticos, 0,35; casos de estancia extrema, 0,26, y complejidad por casuística, 0,24. R2 ajustado 0,82. Conclusión: Los determinantes de la estancia hospitalaria en orden de importancia son: complejidad por consumo de recursos, apoyos diagnósticos, casos de estancia extrema, complejidad por casuística, inventario disponible y ganancias brutas.

Objective: To establish and quantify the determinants of hospital stay in a high complexity university hospital in Medellin between 2013 and 2018, assess their importance, and model the expected length of stay. Methodology: Retrospective analytical observational study of aggregate data. While following the method step by step, seven models were used, where mean hospital stay was the dependent variable and the respective independent variables were complexity, timeliness of diagnostic procedures, availability of supplies, cases of prolonged stay and financial capacity. The best model was selected using the Akaike and Bayesian information criterion, along with measures of both overall significance and individual significance of the coefficients. Statistical tests of model validity were performed and standardized coefficients were calculated. Results: The mean values of the most relevant variables and their standard deviation (SD) were: mean hospital stay, 8.09 days (SD = 0.40); complexity by resource consumption, 1.28 units (SD = 0.07); diagnostic procedures, 90.74 thousand studies (SD = 10.05); cases of extremely prolonged stay, 4.36% (SD = 0.70), and complexity by casuistry, 1 (SD = 0.03). Overall significance: F = 55.2, p < 0.001. Significance of coefficients: complexity by resource consumption, p < 0.01; diagnostic procedures and cases of extremely prolonged stay, p < 0.001; complexity by casuistry, p < 0.05. Standardized coefficients: complexity by resource consumption, 0.35; diagnostic procedures, 0.35; cases of extremely prolonged stay, 0.26; and complexity by casuistry, 0.24. Adjusted R2 0.82. Conclusion: In order of importance, the determinants of hospital stay are complexity by resource consumption, diagnostic procedures, extremely prolonged stay, complexity by casuistry, available inventory and gross profit.

Objetivo: Estabelecer e quantificar os determinantes da permanência hospitalar em um hospital universitário de alta complexidade de Medellín, entre 2013 e 2018, valorar sua importância e fazer a modelação da permanência esperada. Metodologia: Estudo observacional analítico retrospectivo de dados agregados. Seguindo o método passo a passo, foram aplicados sete modelos com permanência hospitalar média como variável dependente e as respectivas variáveis independentes: complexidade, oportunidade de apoios diagnósticos, disponibilidade de insumos, casos de permanência prolongada e capacidade financeira. Selecionou-se o melhor modelo usando os critérios de ajuste Akaike e informação Bayesiana, junto com as medidas de significância individual dos coeficientes. Realizaram-se provas estatísticas de validade do modelo e calcularam-se os coeficientes padronizados. Resultados: Os valores médios das variáveis mais relevantes e seu desvio-padrão (DP) foram: permanência hospitalar média, 8.09 dias (DP = 0,40); complexidade por consumo de recursos, 1,28 unidades (DP = 0,07); apoios diagnósticos, 90,74 mil estudos (DP = 10,05); casos de permanência extrema, 4,36 % (DP = 0,70), e complexidade por casuística, 1 (DP = 0,03). Significância global F = 55,2, p < 0,001. Significância dos coeficientes: complexidade por consumo de recursos, p < 0,01; apoios diagnósticos e casos de permanência extrema p < 0,001; complexidade por casuística, p < 0,05. Coeficientes padronizados: complexidade por consumo de recursos, 0,35; apoios diagnósticos, 0,35; casos de permanência extrema, 0,26 e complexidade por casuística, 0,24. R2 ajustado 0,82. Conclusão: Os determinantes da permanência hospitalar em ordem de importância são: complexidade por consumo de recursos, apoios diagnósticos, casos de permanência extrema, complexidade por casuística, inventário disponível e lucros brutos.

Natural history of Duchenne muscular dystrophy in the United Kingdom: A descriptive study using the Clinical Practice Research Datalink.

BACKGROUND: Duchenne muscular dystrophy (DMD) is a rare, muscle-degenerative disease predominantly affecting males. Natural history models capture the full disease pathway under current care and combine with estimates of new interventions' effects to assess cost-effectiveness by health technology decision-makers. These models require mortality estimates throughout a patient's lifetime, but rare disease datasets typically contain relatively few patients with short follow-ups. Alternative (published) sources of mortality data may therefore be required. METHODS: The Clinical Practice Research Datalink (CPRD) was evaluated as a source of mortality and natural history data for future economic evaluations of health technologies for DMD and rare diseases in general in the UK population. This retrospective longitudinal cohort study provides flexible parametric estimates of mortality rates and survival probabilities in the current UK DMD population through primary/secondary records in the CPRD since 1990. It also investigates clinically significant milestones such as corticosteroid use, spinal surgery, and cardiomyopathy in these patients. RESULTS: A total of 1121 male patients were included in the study, observed from 0.7 to 48.9 years. Median life expectancy was 25.64 years (95% confidence interval 24.73, 26.47), consistent with previous global estimates. This has improved to 26.47 (25.16, 27.89) years in patients born after 1990. The median ages at corticosteroid initiation, spinal surgery, ventilation, and cardiomyopathy diagnosis were 6.06 years (5.77, 6.29), 14.79 years (14.29, 15.09), 16.97 years (16.50, 18.31), and 15.26 years (14.22, 16.70), respectively. CONCLUSIONS: Estimates of mortality in UK-based DMD patients are age-specific in a uniquely large and nationally representative sample from the CPRD.

Investigation of the Cementing Efficiency of Fly Ash Activated by Microsilica in Low-Cement Concrete.

This paper presents experimental results on the influence of concrete composition factors on the criterion characterizing the ratio between the compressive strength of activated low-cement concrete and clinker consumption. The investigation was carried out using mathematical planning of the experiments. Experimental and statistical models describing the influence of the fly ash, activating additive (microsilica), consumption of cement and aggregates, as well as the superplasticizer on the strength of low-cement concrete under normal hardening conditions and after steaming were obtained. The values of the clinker efficiency criterion and the mineral additive cementing efficiency coefficient were calculated, and models of these parameters were obtained for the investigated concrete compositions. It was shown that the activating effect of microsilica yields an increase in ash cementing efficiency and clinker efficiency criterion in concrete. Using the obtained models, an example for calculating the ash cementing efficiency coefficient is given.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

Association of systolic blood pressure with incident chronic kidney disease estimated by marginal structural model: a nationwide population-based study.

Background: We aimed to investigate the association between systolic blood pressure (SBP) and risk of incident chronic kidney disease (CKD) using marginal structural model (MSM) to reflect mutual effects of exposure and confounders on the outcome. Methods: A total of 195,970 adults with an estimated glomerular filtration rate (eGFR) of >60 mL/min/1.73 m2 and no proteinuria were included from a nationally representative sample cohort of Korean population. SBPs were measured through national health examinations. Primary outcome was incident CKD, defined as a composite of events of a decrease in eGFR to <60 mL/min/1.73 m2 or a newly developed proteinuria for at least two consecutive measurements. The association between SBP and risk of CKD was examined using Cox model, time-dependent Cox model, and MSM. Results: During a follow-up of 5 years, CKD occurred in 3,355 participants (1.7%). With SBP treated as a continuous variable, each 10-mmHg increment was associated with higher risk for incident CKD, regardless of analytical models used. Compared to SBP group of 120-129 mmHg, hazard ratios (95% confidence intervals) for incident CKD for SBP groups of <110, 110-119, 130-139, and ≥140 mmHg in MSM were 0.70 (0.62-0.80), 0.85 (0.77-0.95), 1.16 (1.05-1.27), and 1.63 (1.47-1.80), respectively. Conclusion: In this nationwide study, we found a significant relationship between higher SBP and higher risk of incident CKD. Further studies are warranted to verify the potential significance of high SBP as a preventable risk factor for the development of CKD in those with preserved renal function.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

Circular and unified analysis in network neuroscience.

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.