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Background Observational studies are increasingly used to inform health decision-making when randomised trials are not feasible, ethical or timely. The target trial approach provides a framework to help minimise common biases in observational studies that aim to estimate the causal effect of interventions. Incomplete reporting of studies using the target trial framework limits the ability for clinicians, researchers, patients and other decision-makers to appraise, synthesise and interpret findings to inform clinical and public health practice and policy. This paper describes the methods that we will use to develop the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) reporting guideline.Methods/design The TARGET reporting guideline will be developed in five stages following recommended guidance. The first stage will identify target trial reporting practices by systematically reviewing published studies that explicitly emulated a target trial. The second stage will identify and refine items to be considered for inclusion in the TARGET guideline by consulting content experts using sequential online surveys. The third stage will prioritise and consolidate key items to be included in the TARGET guideline at an in-person consensus meeting of TARGET investigators. The fourth stage will produce and pilot-test both the TARGET guideline and explanation and elaboration document with relevant stakeholders. The fifth stage will disseminate the TARGET guideline and resources via journals, conferences and courses.Ethics and dissemination Ethical approval for the survey has been attained (HC220536). The TARGET guideline will be disseminated widely in partnership with stakeholders to maximise adoption and improve reporting of these studies.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

BackgroundUS policymakers are debating whether to expand the Medicare program by lowering the age of eligibility. The goal of this study was to determine the association of Medicare eligibility and enrollment with healthcare access, affordability, and financial strain from medical bills in a contemporary population of low- and higher-income adults in the US.Methods and findingsWe used cross-sectional data from the National Health Interview Survey (2019) to examine the association of Medicare eligibility and enrollment with outcomes by income status using a local randomization-based regression discontinuity approach. After weighting to account for survey sampling, the low-income group consisted of 1,660,188 adults age 64 years and 1,488,875 adults age 66 years, with similar baseline characteristics, including distribution of sex (59.2% versus 59.7% female) and education (10.8% versus 12.5% with bachelor's degree or higher). The higher-income group consisted of 2,110,995 adults age 64 years and 2,167,676 adults age 66 years, with similar distribution of baseline characteristics, including sex (40.0% versus 49.4% female) and education (41.0% versus 41.6%). The share of adults age 64 versus 66 years enrolled in Medicare differed within low-income (27.6% versus 87.8%, p < 0.001) and higher-income groups (8.0% versus 85.9%, p < 0.001). Medicare eligibility at 65 years was associated with a decreases in the percentage of low-income adults who delayed (14.7% to 6.2%; -8.5% [95% CI, -14.7%, -2.4%], P = 0.007) or avoided medical care (15.5% to 5.9%; -9.6% [-15.9%, -3.2%], P = 0.003) due to costs, and a larger decrease in the percentage who were worried about (66.5% to 51.1%; -15.4% [-25.4%, -5.4%], P = 0.003) or had problems (33.9% to 20.6%; -13.3% [-23.0%, -3.6%], P = 0.007) paying medical bills. In contrast, there were no significant associations between Medicare eligibility and measures of cost-related barriers to medication use. For higher-income adults, there was a large decrease in worrying about paying medical bills (40.5% to 27.5%; -13.0% [-21.4%, -4.5%], P = 0.003), a more modest decrease in avoiding medical care due to cost (3.5% to 0.6%; -2.9% [-5.3%, -0.5%], P = 0.02), and no significant association between eligibility and other measures of healthcare access and affordability. All estimates were stronger when examining the association of Medicare enrollment with outcomes for low and higher-income adults. Additional analyses that adjusted for clinical comorbidities and employment status were largely consistent with the main findings, as were analyses stratified by levels of educational attainment. Study limitations include the assumption adults age 64 and 66 would have similar outcomes if both groups were eligible for Medicare or if eligibility were withheld from both.ConclusionsMedicare eligibility and enrollment at age 65 years were associated with improvements in healthcare access, affordability, and financial strain in low-income adults and, to a lesser extent, in higher-income adults. Our findings provide evidence that lowering the age of eligibility for Medicare may improve health inequities in the US.

Background The AHA Registry (American Heart Association COVID‐19 Cardiovascular Disease Registry) captures detailed information on hospitalized patients with COVID‐19. The registry, however, does not capture information on social determinants of health or long‐term outcomes. Here we describe the linkage of the AHA Registry with external data sources, including fee‐for‐service (FFS) Medicare claims, to fill these gaps and assess the representativeness of linked registry patients to the broader Medicare FFS population hospitalized with COVID‐19. Methods and Results We linked AHA Registry records of adults ≥65 years from March 2020 to September 2021 with Medicare FFS claims using a deterministic linkage algorithm and with the American Hospital Association Annual Survey, Rural Urban Commuting Area codes, and the Social Vulnerability Index using hospital and geographic identifiers. We compared linked individuals with unlinked FFS beneficiaries hospitalized with COVID‐19 to assess the representativeness of the AHA Registry. A total of 10 010 (47.0%) records in the AHA Registry were successfully linked to FFS Medicare claims. Linked and unlinked FFS beneficiaries were similar with respect to mean age (78.1 versus 77.9, absolute standardized difference [ASD] 0.03); female sex (48.3% versus 50.2%, ASD 0.04); Black race (15.1% versus 12.0%, ASD 0.09); dual‐eligibility status (26.1% versus 23.2%, ASD 0.07); and comorbidity burden. Linked patients were more likely to live in the northeastern United States (35.7% versus 18.2%, ASD 0.40) and urban/metropolitan areas (83.9% versus 76.8%, ASD 0.18). There were also differences in hospital‐level characteristics between cohorts. However, in‐hospital outcomes were similar (mortality, 23.3% versus 20.1%, ASD 0.08; home discharge, 45.5% versus 50.7%, ASD 0.10; skilled nursing facility discharge, 24.4% versus 22.2%, ASD 0.05). Conclusions Linkage of the AHA Registry with external data sources such as Medicare FFS claims creates a unique and generalizable resource to evaluate long‐term health outcomes after COVID‐19 hospitalization.

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

Background To understand when results from observational studies and randomized trials are comparable, we performed an observational emulation of a target trial designed to ask similar questions as the VALIDATE (Bivalirudin Versus Heparin in ST‐Segment and Non–ST‐Segment Elevation Myocardial Infarction in Patients on Modern Antiplatelet Therapy) randomized trial. The VALIDATE trial compared the effect of bivalirudin and heparin during percutaneous coronary intervention on the risk of death, myocardial infarction, and bleeding across Sweden. Methods and Results We specified the protocol of a target trial similar to the VALIDATE trial, then emulated the target trial in the period before the VALIDATE trial took place using data from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence‐Based Care in Heart Disease Evaluated According to Recommended Therapies) registry—the same registry in which the trial was undertaken. The target trial emulation and the VALIDATE trial both estimated little or no effect of bivalirudin versus heparin on the risk of death or myocardial infarction by 180 days (target trial emulation risk ratio for death, 1.21 [95% CI, 0.88 – 1.54]; VALIDATE trial hazard ratio for death, 1.05 [95% CI, 0.78 – 1.41]). The observational data, however, could not capture less severe cases of bleeding, resulting in an inability to define a bleeding outcome like the trial, and could not accurately estimate the comparative risk of death by 14 days, which may be the result of intractable confounding early in follow‐up or the inability to precisely emulate the trial’s eligibility criteria. Conclusions Using real‐world data to emulate a target trial can deliver accurate effect estimates. Yet, even with rich observational data, it is not always possible to estimate the short‐term effect of interventions or the effect on outcomes for which data are not routinely collected.

When examining the evidence on therapeutic interventions to answer a comparative effectiveness research question, one should consider all studies that are informative on the interventions' causal effects. "Single group studies" evaluate outcomes longitudinally in cohorts of subjects who are managed with a single treatment strategy. Because these studies are "missing" a direct, concurrent comparison group, they are typically deemed non-informative on comparative effectiveness. However, in principle, single group studies can provide information on causal treatment effects by extrapolating expected outcomes in the "missing" untreated arm. Single group studies rely on before-after, implicit, or historical comparisons as a proxy for an ideal comparison group. The validity of these comparisons must be carefully examined on a case-by-case basis. While in many cases, researchers will disagree on whether such extrapolations are reasonable; circumstances exist where such studies are generally acceptable as a source of evidence. This article provides an overview of issues related to the interpretation of single group studies with a focus on the assumptions required to support their consideration in comparative effectiveness reviews. We discuss the various settings in which single group studies are employed, common research designs that systematic reviewers need to interpret, and challenges associated with using these designs to inform comparative effectiveness questions.

We performed a survey of meta-analyses of test performance to describe the evolution in their methods and reporting. Studies were identified through MEDLINE (1966-2009), reference lists, and relevant reviews. We extracted information on clinical topics, literature review methods, quality assessment, and statistical analyses. We reviewed 760 publications reporting meta-analyses of test performance, published between 1987 and 2009. Eligible reviews included a median of 18 primary studies that were used in quantitative analyses. Most common clinical areas were cardiovascular disease (21%) and oncology (25%); most common test categories were imaging (44%) and biomarker tests (28%). Assessment of verification and spectrum bias, blinding, prospective study design, and consecutive patient recruitment became more common over time (p < 0.001 comparing reviews published through 2004 vs 2005 onwards). These changes coincided with the increasing use of checklists to guide assessment of methodological quality. Heterogeneity tests were used in 58% of meta-analyses; subgroup or regression analyses were used in 57%. Random effects models were employed in 57% of meta-analyses (38% through 2004 vs 72% 2004-onwards; p < 0.001). Use of bivariate models of sensitivity and specificity increased in recent years (21% in 2008-2009 vs 7% in earlier years; p < 0.001). Methods employed in meta-analyses of test performance have improved with the introduction of quality assessment checklists and the development of more sophisticated statistical methods.

The R environment provides a natural platform for developing new statistical methods due to the mathematical expressiveness of the language, the large number of existing libraries, and the active developer community. One drawback to R, however, is the learning curve; programming is a deterrent to non-technical users, who typically prefer graphical user interfaces (GUIs) to command line environments. Thus, while statisticians develop new methods in R, practitioners are often behind in terms of the statistical techniques they use as they rely on GUI applications. Meta-analysis is an instructive example; cutting-edge meta-analysis methods are often ignored by the overwhelming majority of practitioners, in part because they have no easy way of applying them. This paper proposes a strategy to close the gap between the statistical state-of-the-science and what is applied in practice. We present open-source meta-analysis software that uses R as the underlying statistical engine, and Python for the GUI. We present a framework that allows methodologists to implement new methods in R that are then automatically integrated into the GUI for use by end-users, so long as the programmer conforms to our interface. Such an approach allows an intuitive interface for non-technical users while leveraging the latest advanced statistical methods implemented by methodologists.

OBJECTIVE: Smoking during pregnancy has been linked to a variety of negative embryonic and neonatal consequences. Nicotine is the major constituent of tobacco smoke, which was associated with adverse impacts on histological and functional features of the placenta. This study aims to investigate the potential influence of nicotine exposure on the rat placenta and fetus. MATERIALS AND METHODS: Nicotine was administrated through the drinking water of female pregnant rats. The placental size, as well as the fetal body weight and size, were measured at E20. The mRNA expression of the Bax gene (pro-apoptotic), the Bcl-2 gene (anti-apoptotic) and the angiogenic genes VEGF, Flt-1, and HIF1 were measured in placental tissue. Furthermore, Immunohistochemistry (IHC) using Bax, caspase 9 and VEGF antibodies were performed on placental sections. RESULTS: The results of the current study showed a significant reduction in the size of the placenta along with fetal body weight in nicotine treated group compared to the control group. Apoptosis was observed to be boosted in the placentas of the nicotine-treated group. This was associated with upregulation of Bax expression combined with no change in the expression of Bcl-2 in the treated group. On the other hand, there was no difference in the expression of angiogenic factors VEGF, Flt-1, or HIF1 between tested groups. CONCLUSIONS: In utero nicotine exposure in pregnant rats showed deleterious impacts on fetus growth and weight, as well as placental size. These were accompanied by increased apoptosis within the placenta, as revealed by Bax gene upregulation. [ABSTRACT FROM AUTHOR]

When studying the effect of a prenatal treatment on events in the offspring, failure to produce a live birth is a competing event for events in the offspring. A common approach to handle this competing event is reporting both the treatment-specific probabilities of live births and of the event of interest among live births. However, when the treatment affects the competing event, the latter probability cannot be interpreted as the causal effect among live births. Here we provide guidance for researchers interested in the effects of prenatal treatments on events in the offspring in the presence of the competing event "no live birth." We review the total effect of treatment on a composite event and the total effect of treatment on the event of interest. These causal effects are helpful for decision making but are agnostic about the pathways through which treatment affects the event of interest. Therefore, based on recent work, we also review three causal effects that explicitly consider the pathways through which treatment may affect the event of interest in the presence of competing events: the direct effect of treatment on the event of interest under an intervention to eliminate the competing event, the separable direct and indirect effects of treatment on the event of interest, and the effect of treatment in the principal stratum of those who would have had a live birth irrespective of treatment choice. As an illustrative example, we use a randomized trial of fertility treatments and risk of neonatal complications. [ABSTRACT FROM AUTHOR]

We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that the assumptions that allow inferences to be transported from all trials in the collection to the same target population have implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis Trial. [ABSTRACT FROM AUTHOR]

When generalizing inferences from a randomized trial to a target population, two classes of estimators are used: g-formula estimators that depend on modeling the conditional outcome mean among trial participants and inverse probability (IP) weighting estimators that depend on modeling the probability of participation in the trial. In this article, we take a closer look at the relation between these two classes of estimators. We propose IP weighting estimators that combine models for the probability of trial participation and the probability of treatment among trial participants. We show that, when all models are estimated using nonparametric frequency methods, these estimators are finite-sample equivalent to the g-formula estimator. We argue for the use of augmented IP weighting (doubly robust) generalizability estimators when nonparametric estimation is infeasible due to the curse of dimensionality, and examine the finite-sample behavior of different estimators using parametric models in a simulation study. [ABSTRACT FROM AUTHOR]

Apical periodontitis is an inflammatory disorder that results from the host immune response to microbial infection through the dental pulp, leading to alveolar bone destruction. The nod-like receptor 12 (NLRP12) is an atypical intracellular sensor of the NLR family that is involved in the negative regulation of several inflammatory conditions and also osteoclastogenesis. However, the role of NLRP12 in the regulation of immune response and bone loss induced by bacterial infection remains unclear. Here we investigated the development of apical periodontitis in wild-type (WT) and NLRP12 knockout (NLRP12-/-) mice by using micro-computed tomography together with histological, immunohistochemical, and molecular analyses. We found that NLRP12-/- mice are highly susceptible to apical periodontitis induced by bacterial infection, which is associated with an elevated infiltration of neutrophils and macrophages, periapical lesion extension, and alveolar bone destruction. Furthermore, NLRP12-/- mice showed a high expression of inflammatory cytokines ( Il1b, Il6, and Tnfa) and the osteoclastogenic markers ( Rankl and Acp5) in the periapical tissues. Consistent with this observation, NLRP12-/- mice showed an increased number of tartrate-resistant acid phosphatase-positive cells lining the apical periodontitis site, which was associated with augmented expression of the osteoclast effector genes, Ctsk and Mmp9. Mechanistically, NLRP12-deficient preosteoclasts showed elevated IκB-α degradation and p65 phosphorylation when stimulated with receptor activator of nuclear factor (NF)-κB ligand (RANKL). Similarly, increased IκB-α degradation was observed in the periapical tissue of NLRP12-/- mice. Furthermore, our in vitro study showed that preosteoclasts from NLRP12-/- mice exhibited higher RANKL-induced osteoclastogenesis, which was synergistically amplified by interleukin-1β and tumor necrosis factor α (mimicking an inflammatory periapical milieu). In conclusion, our data show that NLRP12 exhibits a protective role in the periapical bone destruction by attenuating inflammation and osteoclastogenesis through negative regulation of the NF-κB pathway. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the effects of high‐impact physical exercise as a prophylactic and therapeutic means in osteopenic bones of rats submitted to ovariectomy and protein diet intake. A total of 64 Wistar rats were divided into eight groups (n = 8 each), being: OVX, ovx, standard diet and sedentary; OVXE, ovx, standard diet and jump; OVXP, ovx, high‐protein diet and sedentary; and OVXEP, ovx, high‐protein diet and jump; SH, sham, standard diet and sedentary; SHE, sham, standard diet and jump; SHP, sham, high‐protein diet and sedentary; and SHEP, sham, high‐protein diet and jump. OVX surgery consists of ovariectomy, and sham was the control surgery. The jumping protocol consisted of 20 jumps/day, 5 days/week. The bone structure was evaluated by densitometry, mechanical tests, histomorphometric, and immunohistochemical analyses. A high‐protein diet resulted in increased bone mineral density (P = .049), but decreased maximal load (P = .026) and bone volume fraction (P = .023). The benefits of physical exercise were demonstrated by higher values of the maximal load in the trained groups compared to the sedentary groups (P < .001). The sham groups had decreased immunostaining of osteocalcin (P = .004) and osteopontin (P = .010) compared to ovx groups. However, the high‐protein diet (P = .005) and jump exercise (P = .017) resulted in lower immunostaining of osteopontin compared to the standard diet and sedentary groups, respectively. In this experimental model, it was concluded that ovariectomy and a high‐fat diet can negatively affect bone tissue and the high‐impact exercise was not enough to suppress the deleterious effects caused by the protein diet and ovariectomy. [ABSTRACT FROM AUTHOR]

This work presents an exact and general model order reduction (MOR) technique for a fast finite element resolution of elastohydrodynamic lubrication (EHL) problems. The reduction technique is based on the static condensation principle. As such, it is exact and it preserves the generality of the solution scheme while reducing the size of its corresponding model and, consequently, the associated computational overhead. The technique is complemented with a splitting algorithm to alleviate the hurdle of solving an arising semidense matrix system. The proposed reduced model offers computational time speed-ups compared to the full model ranging between a factor of at least three and at best 15 depending on operating conditions. The results also reveal the robustness of the proposed methodology which allows the resolution of very highly loaded contacts with Hertzian pressures reaching several GPa. Such cases are known to be a numerical challenge in the EHL literature. [ABSTRACT FROM AUTHOR]

Heart failure in adults is characterized by a reduction in beta oxidation of long-chain fatty acids in favor of carbohydrate metabolism. Thus, the reduction of beta oxidation leads to a decrease in energy production and an accumulation of toxic derivatives such as LC acylcarnitines. This lipidomic disturbance has been shown to impair cardiac function, and is called cardiolipotoxicity. No data are available concerning lipid homeostasis modification in heart failure in children. We compared acylcarnitines profiles performed in our centre from 2015 to 2021 in children explored for a non-metabolic dilated cardiomyopathy (DCM) and in control children. Sixteen children aged from 3 months to 15 years old referred to our centre for DCM were included. Eight of them had clinical symptoms of heart failure and elevated NT pro BNP (DCM-HF), while the 8 others had no symptoms of HF and normal NT pro BNP (DCM-N). Sums of total acylcarnitines, as well as sum of long chain, medium chain or short chain acylcarnitines were enhanced in DCM-HF children compared to the control population, and also compared to DCM-N children (Figure 1). C16OH-Car/C16-Car, and C14:1-Car/C12:1-Car ratio were significantly higher in DCM-HF group than in DCM-N group or control group, suggesting a downregulation of specific enzymes of long chain fatty acids beta oxidation. Our results support a reduction of fatty acid oxidation in children with DCM-HF. This metabolic modification needs to be further investigated in a larger scale study. Such lipidomic alteration could worsen heart function and may suggest considering a metabolic treatment of heart failure in children. [ABSTRACT FROM AUTHOR]

Summary: The KRAS oncogene has been extensively studied for more than three decades, however, it is only recently that it attained a central role in the clinical decision-making process for the practicing oncologist. Recently, based on retrospective analyses of large randomized clinical trials, the use of anti-epidermal growth factor (EGFR) monoclonal antibodies, cetuximab and panitumumab, was restricted to patients with metastatic colorectal cancer that carry the “wild-type”KRAS genotype. Challenges remain in the laboratory implementation of KRAS mutational testing and the clinical application of the test for treatment planning. This review attempts to offer a global view of KRAS biology, its functional role in cell signaling, mechanisms of resistance to anti-EGFR agents and its predictive potential in metastatic colorectal cancer. We also survey the growing list of candidate biomarkers that may shortly supplement KRAS in routine clinical patient stratification. Finally, we discuss practical aspects of KRAS testing that may be useful for those involved in mutational screening in their centers. This general overview of KRAS for clinical oncology practice aims to assist in data interpretation and offer insight into potential pitfalls of mutational testing. KRAS is a prime example of how translational research can fulfill the promises of personalized medicine for tailoring treatment to match the underlying tumor biology. [ABSTRACT FROM AUTHOR]

The article discusses a research study on heterogeneous coronary artery disease (CAD) phenotypic definition and how it can affect studies on generic associations. Meta-analyses of genetic associations were reviewed to extract demographics, phenotypic definitions and genotypic data for the assessment of the magnitude of genetic effects in CAD genotypes. Results indicated the existence of substantial phenotypic heterogeneity in CAD genetic associations and the small contribution to heterogeneity of differences in phenotype definition.