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Blood Glucose Levels Combined with Triage Revised Trauma Score Improve the Outcome Prediction in Adults and in Elderly Patients with Trauma
- Marcello Covino, Raffaella Zaccaria, Maria Grazia Bocci, Luigi Carbone, Enrico Torelli, Mariella Fuorlo, Andrea Piccioni, Michele Santoro, Claudio Sandroni, Francesco Franceschi
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- Journal:
- Prehospital and Disaster Medicine / Volume 36 / Issue 2 / April 2021
- Published online by Cambridge University Press:
- 21 December 2020, pp. 175-182
- Print publication:
- April 2021
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- Article
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Introduction:
This study was aimed to assess if combining the evaluation of blood glucose level (BGL) and the Triage Revised Trauma Score (T-RTS) may result in a more accurate prediction of the actual clinical outcome, both in general adult population and in elderly patients with trauma.
Methods:This is a retrospective cohort study, conducted in the emergency department (ED) of an urban teaching hospital, with an average ED admission rate of 75,000 patients per year. Those excluded: known diagnosis of diabetes, age <18 years old, pregnancy, and mild trauma (classified as isolate trauma of upper or lower limb, in absence of exposed fractures). A combined Revised Trauma Score Glucose (RTS-G) score was obtained adding to T-RTS: two for BGL <160mg/dL (8.9mmol/L); one for BGL ≥160mg/dL and < 200mg/dL (11.1mmol/L); and zero for BGL ≥ 200mg/dL. The primary outcome was a composite of patient’s death in ED or admission to intensive care unit (ICU). Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the overall performance of T-RTS and of the combined RTS-G score.
Results:Among a total of 68,933 traumas, 9,436 patients (4,407 females) were enrolled, aged from 18 to 103 years; 4,288 were aged ≥65 years. A total of 577 (6.1%) met the primary endpoint: 38 patients died in ED (0.4%) and 539 patients were admitted to ICU. The T-RTS and BGL were independently associated to primary endpoint at multivariate analysis. The cumulative RTS-G score was significantly more accurate than T-RTS and reached the best accuracy in elderly patients. In general population, ROC area under curve (AUC) for T-RTS was 0.671 (95% CI, 0.661 - 0.680) compared to RTS-G ROC AUC 0.743 (95% CI, 0.734 - 0.752); P <.001. In patients ≥65 years, T-RTS ROC AUC was 0.671 (95% CI, 0.657 - 0.685) compared to RTS-G ROC AUC 0.780 (95% CI, 0.768 - 0.793); P <.001.
Conclusions:Results showed RTS-G could be used effectively at ED triage for the risk stratification for death in ED and ICU admission of trauma patients, and it could reduce under-triage of approximately 20% compared to T-RTS. Comparing ROC AUCs, the combined RTS-G score performs significantly better than T-RTS and gives best results in patients ≥65 years.
3 - Economic Complexity as a Determinant of Industrialization of Countries: The Case of India
- from Section 2 - Promoting Industrial Development for Sustaining High Growth Rates
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- By Emanuele Pugliese, Institute for Complex Systems of the National Council of Research, Italy, Guido L. Chiarotti, Managing Director of MaX, a European Centre of Excellence-Publisher at Scienza Express, Andrea Zaccaria, Institute for Complex Systems (CNR), Luciano Pietronero, University of Roma ‘La Sapienza’
- Edited by Pradeep Agrawal, Institute of Economic Growth, Delhi
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- Book:
- Sustaining High Growth in India
- Published online:
- 08 February 2018
- Print publication:
- 06 September 2017, pp 87-110
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Summary
INTRODUCTION
The industrialization of a country is an impressive process, deeply changing the population and the institutions of the country while new and old resources are tapped to achieve growth. During this transition, the growth rate of the economy is much higher than the global average and much higher than the past and future growth rates of that country.
It is, however, a transition, and as such, is limited in time. When the industrialization process has touched all the sectors, when most of the population is educated and near full employment and when all the scale economies have been fulfilled, the process loses its revolutionary power and the new society now sits among the developed countries.
Two questions have haunted economists since the beginning of the discipline, since Adam Smith and Max Weber. The first question concerns the drivers of this sudden spurt of growth: how can an entire society change dramatically in fifty years after being static for thousands of years? Moreover, why is this process suddenly interrupted after catching-up with the other developed countries? There are many competing answers to this part of the puzzle, the most basic being the poverty trap due to multiple equilibriums (Solow, 1956): the phenomenon is sudden because there is a barrier – a given level of wealth needs to be reached to start a quick transition to a different equilibrium. There are several alternative explanations, the most notable being the relation between increasing returns and demand, as in Rosenstein-Rodan (1943) and – more formally – in Murphy et al. (1988), in which the barrier to overcome is a minimum internal demand which inhibits scaling returns in manufacturing.
The second question is about the heterogeneity of the process among countries. England experienced its Industrial Revolution in the second half of the eighteenth century, followed by other Western countries. In the nineteenth century, the United States became industrialized too, other countries followed in the twentieth century, while others did not. What prevented countries lagging behind from moving towards prosperity? There are many alternative explanations, ranging from cultural (McCloskey, 2006; Weber et al., 2002) to geographic (Diamond and Ordunio, 1997) and even biological (Ashraf and Galor, 2011). Another explanation is political: to achieve growth the population has to be empowered through inclusive political institutions, leading to more inclusive economic institutions and widespread prosperity (Acemoglu et al., 2005).