Reduction of ventricular arrhythmias with synchronized LV pacing—fact,fiction, or fortuitous

Journal of Interventional Cardiac Electrophysiology -     

Differentiating wide complex tachycardias: A historical perspective

One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base – one that spans several decades.     

Least-cost targets and avoided fossil fuel capacity in India’s pursuit of renewable energy

India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the electricity and carbon mitigation costs to reliably operate India’s grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India’s proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India’s specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India’s 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈ 45% share by energy) could result in electricity costs similar to a fossil fuel-dominated system. As an alternative strategy for meeting peak electricity demand, battery storage can avert the need for new fossil fuel capacity but is cost effective only at low capital costs (≈ USD 150 per kWh).

India emitted 3.2 billion metric tons of CO₂e in 2016, or 6% of annual global greenhouse gas emissions, placing it third only to China and the United States (1). One-third of these emissions were from coal-based electricity. At the same time, both per capita emissions and energy use remain well below global averages, suggesting a massive potential for growth of electricity generation and emissions (1). India’s primary energy demand is expected to double by 2040 compared to 2017 (2). Whether this energy comes from fossil or low-carbon sources will significantly affect the ability to limit average global temperature rise to below 2 °C.India is already pursuing significant technology-specific renewable energy targets—100 GW of solar and 60 GW of wind by 2022—and, in its Nationally Determined Contributions (NDC), committed to a 40% target for installed generation capacity from nonfossil fuel sources by 2030 (3). In 2019, in part to fulfill its NDC commitment, the Indian government proposed to install 440 GW of renewable energy capacity by 2030, with 300 GW of solar and 140 GW of wind capacity (4). Although costs of solar photovoltaic (PV) and wind technologies have declined significantly in recent years (5–7), the low cost of coal and integration costs associated with variable renewable energy (VRE) technologies like wind and solar may hinder India’s cost-effective transition to a decarbonized electricity system. This paper seeks to answer a number of questions that arise in the Indian context. What targets for wind and solar capacity have the lowest associated integration costs? Will these targets significantly offset the need to build fossil fuel generation capacity? What additional measures can we take to mitigate VRE integration costs?Merely comparing the levelized costs of VRE with the costs of conventional generation ignores additional cost drivers, which depend on the timing of VRE production and other conditions in the power system (8, 9). Quantifying these drivers requires models that choose lowest-cost generation capacity portfolios and simulate optimal system operation with detailed spatiotemporal data. Several prior studies address these system-level integration costs in a capacity expansion planning framework (10–16), often making decisions based on a limited sample of representative hours. Other studies explicitly estimate the relationship between long-run economic value (including integration costs) of VRE penetration levels (17, 18) but do not include VRE investment costs in their analysis. Few prior studies explore the impacts of high VRE penetration on India’s electricity system, and those that do either use the capacity expansion framework and do not evaluate the economic value of multiple VRE targets (4, 19, 20) or do not optimize capacity build around proposed VRE targets (21).Here we address this gap by estimating how different VRE targets affect the cost to reliably operate the Indian electricity system. To do so, we work with three interrelated models. First, using a spatially explicit model for VRE site selection, we identify the lowest levelized cost wind and solar sites to meet different VRE capacity targets, and study how the resource quality—and corresponding levelized cost—of selected sites changes with increasing VRE targets.Second, using a capacity investment model that accounts for VRE production patterns and optimal dispatch of hydropower and battery storage, we determine the capacity requirements and investment costs for coal, combined cycle gas turbines (CCGT), and combustion turbine (CT) peaker plants. Due to uncertainties in their future deployment (22), and because their current targets are relatively low (4), we did not consider new nuclear or hydro capacity in the main scenarios but include those in the sensitivity scenarios presented in SI Appendix, section 2. Third, we use a unit commitment and economic dispatch model to simulate hourly operation of the electricity system and estimate annual system operational costs. This model captures important technical constraints, including minimum operating levels, daily unit commitment for coal and natural gas plants, and energy limits on hydropower and battery storage. Rather than cooptimize VRE capacity, we compute the system-level economic value of a range of VRE targets by comparing the sum of the avoided new conventional capacity and energy generation costs to a no-VRE scenario. The net cost for a scenario is then the difference between the levelized cost of the VRE and the system-level economic value. Materials and Methods provides more detail on this process.Our results show that, despite greater levelized cost reduction forecasts for solar PV compared to wind technologies, VRE targets with greater amounts of wind have the lowest projected net carbon mitigation costs. This finding is robust to a range of scenarios, including low-cost solar and storage, and lower minimum generation levels for coal generators.We find that, although VRE production displaces energy production from conventional generators, it does very little to defer the need for capacity from those generators due to low correlation between VRE production and peak demand. Our findings suggest that VRE in India avoids far less conventional capacity than VRE in other regions in the world. These capacity requirements are slightly mitigated if India’s demand patterns evolve to more closely resemble demand in its major cities. Overall, we conclude that the importance of choosing the right VRE mix is significant when measured in terms of carbon mitigation costs: Whereas most solar-majority scenarios we examined lead to costs greater than or equal to estimates of the social cost of carbon (SCC), wind-majority mixes all cost far less than the SCC.     

INCLEN diagnostic tool for autism spectrum disorder (INDT-ASD): Development and validation

Objective

To develop and validate INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD).

Design

Diagnostic test evaluation by cross sectional design

Setting

Four tertiary pediatric neurology centers in Delhi and Thiruvanthapuram, India.

Methods

Children aged 2–9 years were enrolled in the study. INDT-ASD and Childhood Autism Rating Scale (CARS) were administered in a randomly decided sequence by trained psychologist, followed by an expert evaluation by DSM-IV TR diagnostic criteria (gold standard).

Main outcome measures

Psychometric parameters of diagnostic accuracy, validity (construct, criterion and convergent) and internal consistency.

Results

154 children (110 boys, mean age 64.2 mo) were enrolled. The overall diagnostic accuracy (AUC=0.97, 95% CI 0.93, 0.99; P<0.001) and validity (sensitivity 98%, specificity 95%, positive predictive value 91%, negative predictive value 99%) of INDT-ASD for Autism spectrum disorder were high, taking expert diagnosis using DSM-IV-TR as gold standard. The concordance rate between the INDT-ASD and expert diagnosis for’ ASD group’ was 82.52% [Cohen’s κ=0.89; 95% CI (0.82, 0.97); P=0.001]. The internal consistency of INDT-ASD was 0.96. The convergent validity with CARS (r = 0.73, P= 0.001) and divergent validity with Binet-Kamat Test of intelligence (r = ?0.37; P=0.004) were significantly high. INDT-ASD has a 4-factor structure explaining 85.3% of the variance.

Conclusion

INDT-ASD has high diagnostic accuracy, adequate content validity, good internal consistency high criterion validity and high to moderate convergent validity and 4-factor construct validity for diagnosis of Autistm spectrum disorder.