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A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

Background: Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. “CKD Patch” is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. Methods: Utilizing data from 4,143,535 adults from 35 datasets, we developed several “CKD Patches” incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. Findings: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018–0.036] and 0.010 [0.007–0.013] and categorical net reclassification improvement 0.080 [0.032–0.127] and 0.056 [0.044–0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89–3.40) in very high-risk CKD (e.g., eGFR 30–44 ml/min/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48–2.44) in high-risk CKD (e.g., eGFR 45–59 ml/min/1.73m2 with albuminuria 30–299 mg/g), and 1.37 (1.14–1.69) in moderate risk CKD (e.g., eGFR 60–89 ml/min/1.73m2 with albuminuria 30–299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37–1.81), 1.24 (1.10–1.54), and 1.21 (0.98–1.46). Interpretation: The “CKD Patch” can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. Funding: US National Kidney Foundation and the NIDDK.

ImportanceEarly identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.ObjectiveTo develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).Design, setting, and participantsIndividual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).ExposuresDemographic and clinical factors.Main outcomes and measuresIncident eGFR of less than 60 mL/min/1.73 m2.ResultsAmong 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.Conclusions and relevanceEquations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

A single determination of eGFR associates with subsequent mortality risk. Prior decline in eGFR indicates loss of kidney function, but the relationship to mortality risk is uncertain. We conducted an individual-level meta-analysis of the risk of mortality associated with antecedent eGFR slope, adjusting for established risk factors, including last eGFR, among 1.2 million subjects from 12 CKD and 22 other cohorts within the CKD Prognosis Consortium. Over a 3-year antecedent period, 12% of participants in the CKD cohorts and 11% in the other cohorts had an eGFR slope 5 ml/min per 1.73 m(2) per year, respectively. Compared with a slope of 0 ml/min per 1.73 m(2) per year, a slope of -6 ml/min per 1.73 m(2) per year associated with adjusted hazard ratios for all-cause mortality of 1.25 (95% confidence interval [95% CI], 1.09 to 1.44) among CKD cohorts and 1.15 (95% CI, 1.01 to 1.31) among other cohorts during a follow-up of 3.2 years. A slope of +6 ml/min per 1.73 m(2) per year also associated with higher all-cause mortality risk, with adjusted hazard ratios of 1.58 (95% CI, 1.29 to 1.95) among CKD cohorts and 1.43 (95% CI, 1.11 to 1.84) among other cohorts. Results were similar for cardiovascular and noncardiovascular causes of death and stronger for longer antecedent periods (3 versus

eGFR is a robust predictor of ESRD risk. However, the prognostic information gained from the past trajectory (slope) beyond that of the current eGFR is unclear. We examined 22 cohorts to determine the association of past slopes and current eGFR level with subsequent ESRD. We modeled hazard ratios as a spline function of slopes, adjusting for demographic variables, eGFR, and comorbidities. We used random effects meta-analyses to combine results across studies stratified by cohort type. We calculated the absolute risk of ESRD at 5 years after the last eGFR using the weighted average baseline risk. Overall, 1,080,223 participants experienced 5163 ESRD events during a mean follow-up of 2.0 years. In CKD cohorts, a slope of -6 versus 0 ml/min per 1.73 m(2) per year over the previous 3 years (a decline of 18 ml/min per 1.73 m(2) versus no decline) associated with an adjusted hazard ratio of ESRD of 2.28 (95% confidence interval, 1.88 to 2.76). In contrast, a current eGFR of 30 versus 50 ml/min per 1.73 m(2) (a difference of 20 ml/min per 1.73 m(2)) associated with an adjusted hazard ratio of 19.9 (95% confidence interval, 13.6 to 29.1). Past decline contributed more to the absolute risk of ESRD at lower than higher levels of current eGFR. In conclusion, during a follow-up of 2 years, current eGFR associates more strongly with future ESRD risk than the magnitude of past eGFR decline, but both contribute substantially to the risk of ESRD, especially at eGFR

Do leaders persuade? Social scientists have long studied the relationship between elite behavior and mass opinion. However, there is surprisingly little evidence regarding direct persuasion by leaders. Here we show that political leaders can persuade their constituents directly on three dimensions: substantive attitudes regarding policy issues, attributions regarding the leaders' qualities, and subsequent voting behavior. We ran two randomized controlled field experiments testing the causal effects of directly interacting with a sitting politician. Our experiments consist of 20 online town hall meetings with members of Congress conducted in 2006 and 2008. Study 1 examined 19 small meetings with members of the House of Representatives (average 20 participants per town hall). Study 2 examined a large (175 participants) town hall with a senator. In both experiments we find that participating has significant and substantively important causal effects on all three dimensions of persuasion but no such effects on issues that were not discussed extensively in the sessions. Further, persuasion was not driven solely by changes in copartisans' attitudes; the effects were consistent across groups.

Why and how do groups share information in politics? Most studies of information exchange in politics focus on individual-level attributes and implicitly assume that communication between any two policy actors is independent of the larger communication network in which they are embedded. We develop a theory stating that the decision of any lobbyist to inform another lobbyist is heavily conditioned upon their mutual relationships to third par-ties. We analyze over 40,000 dyadic relationships among lobbyists, government agencies, and congressional staff using sociometric data gathered in the 1970s health and energy policy domains. The results cohere with recent findings that lobbyists disproportionately inform those with similar preferences and show in addition that political communication is transitive: holding constant the degree of preference similarity, a lobbyist is more likely to communicate with another lobbyist if their relationship is brokered by a third party.

Abstract Greenhouse gas (GHG) accounting of emissions from land use, land-use change, and forestry necessarily involves consideration of landscape fire. This is of particular importance for Australia given that natural and human fire is a common occurrence, and many ecosystems are adapted to fire, and require periodic burning for plant regeneration and ecological health. Landscape fire takes many forms, can be started by humans or by lightning, and can be managed or uncontrolled. We briefly review the underlying logic of greenhouse gas accounting involving landscape fire in the 2020 Australian Government GHG inventory report. The treatment of wildfire that Australia chooses to enact under the internationally agreed guidelines is based on two core assumptions (a) that effects of natural and anthropogenic fire in Australian vegetation carbon stocks are transient and they return to the pre-fire level relatively quickly, and (b) that historically and geographically anomalous wildfires in forests should be excluded from national anthropogenic emission estimates because they are beyond human control. It is now widely accepted that anthropogenic climate change is contributing to increased frequency and severity of forest fires in Australia, therefore challenging assumptions about the human agency in fire-related GHG emissions and carbon balance. Currently, the national inventory focuses on forest fires; we suggest national greenhouse gas accounting needs to provide a more detailed reporting of vegetation fires including: (a) more detailed mapping of fire severity patterns; (b) more comprehensive emission factors; (c) better growth and recovery models from different vegetation types; (d) improved understanding how fires of different severities affect carbon stocks; and (e) improved analysis of the human agency behind the causes of emissions, including ignition types and fire-weather conditions. This more comprehensive accounting of carbon emissions would provide greater incentives to improve fire management practices that reduce the frequency, severity, and extent of uncontrolled landscape fires.

Critics of deliberative democracy have worried that deliberation may mirror (or even exacerbate) inequalities in participation across categories such as gender, race, and age. Accordingly, we investigate the potential for technology and design to ameliorate these concerns, looking at the extent to which online deliberative sessions facilitate inclusive participation. In a large study of online deliberation (over 1600 participants nested in hundreds of online sessions), we examine differences in the amount and nature of participation across demographic categories, as well as the effect of forum characteristics on such differences. Though our results are mixed, we read them with cautious optimism: the online format is not immune to inequalities in participation and satisfaction, but we do not observe differences across some demographics, and most observed differences are substantively minor. Moreover, features of online deliberation environments show promise for addressing some of the problems plaguing in-person designs. [ABSTRACT FROM AUTHOR]

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change, and play important roles in the global carbon cycle, through maintenance of a large carbon pool in tropical forests, and exchange in savannas through extensive landscape fires. Reliable mapping of tropical forest and savanna is essential to understand how the current distribution of these vegetation types is controlled by climate land clearing and fire. Using Google Maps satellite imagery, we manually classified 24 239 random points as forest, savanna, or anthropogenic landscapes within the tropics and applied this novel dataset to defining the climatic zone where forest and savanna exist as alternative states. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modeling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We also identified areas where tropical forest and savanna both co-occur, but these were relatively uncommon. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modeling provides global mapping that can be used track further changes to distribution of tropical forests.

N6-methyladenine is the most widespread mRNA modification. A subset of human box C/D sno RNA species have target GAC sequences that lead to formation of N6-methyladenine at a key trans Hoogsteen-sugar A·G base pair, of which half are methylated in vivo. The GAC target is conserved only in those that are methylated. Methylation prevents binding of the 15.5-kDa protein and the induced folding of the RNA. Thus, the assembly of the box C/D sno RNP could in principle be regulated by RNA methylation at its critical first stage. Crystallography reveals that N6-methylation of adenine prevents the formation of trans Hoogsteen-sugar A·G base pairs, explaining why the box C/D RNA cannot adopt its kinked conformation. More generally, our data indicate that sheared A·G base pairs (but not Watson-Crick base pairs) are more susceptible to disruption by N6mA methylation and are therefore possible regulatory sites. The human signal recognition particle RNA and many related Alu retrotransposon RNA species are also methylated at N6 of an adenine that forms a sheared base pair with guanine and mediates a key tertiary interaction. [ABSTRACT FROM AUTHOR]