Multi Agent Functional Bone Simulation: A theoretical study

In 200 A.D., Galen described bones as the fundamental system of body protection. Bones are highly dynamic, in constant renovation to preserve their properties. Understanding bone metabolism has become a relevant area of research. The most common bone disease is osteoporosis and is characterized by low bone mass and microarchitecture disturbances. The major consequence of osteoporosis are fragility fractures (fractures that occur with low impact trauma). Osteoporosis causes more than 8.9 million fractures each year worldwide. Several therapies are effective in preventing fractures and treating osteoporosis. However, there is an enormous difficulty in predicting osteoporosis related fractures and understanding who needs these therapies in order to prevent bone loss. In practical clinic, it becomes essential to have a model describing the bone remodeling process and the impact of the different cellular mediators in the bone metabolism. Although some mathematical models already describe the variation of the bone cells, they do it in a continuous and deterministic way with few cellular mediators. Many studies and experimental results have shown that cellular metabolism and birth-and-death processes in population dynamics are stochastic. Furthermore, mathematical models are reliable on describing a macro level, whereas multiagent simulation models are used to link micro and macro perspectives. In this thesis we have developed a multiagent stochastic model that simulates a timeline remodeling cycle. Our simulator reproduces the homeostatic process of remodeling with the different phases of it, which is time consistent with the real biological process. Our model includes the most relevant cellular mediators in the bone metabolism. Our model demonstrated to have great sensibility to predict bone loss caused by some chronic diseases such hyper and hypoparathyroidism, and excess of glucocorticoids, and also to the most known causes of osteoporosis: estrogen or vitamin D deficiency. Overall, this model provides a deeper understanding about bone metabolism and the pathologies associated with it.Em 200 A.D, Galen descreveu os ossos como o sistema fundamental de proteção do corpo. Os ossos são estruturas altamente dinâmicas e estão em constante remodelação para preservarem as suas propriedades. Compreender o metabolismo do osso tornou-se uma marcante área de pesquisa. A doença óssea mais comum é a osteoporose que causa mais de 8.9 milhões de fraturas em todo o mundo. Existem várias terapias eficazes em prevenir e tratar esta doença. Contudo, existe uma enorme dificuldade em prevê-la, bem como em entender quem necessita de terapêuticas para a retardar ou evitar a perda óssea. Na prática clínica, revelou-se importante existir um modelo que descreva o processo de remodelação óssea assim como o impacto dos diferentes mediadores celulares no metabolismo ósseo. Apesar de já existirem alguns modelos matemáticos que descrevem a variação das células ósseas na remodelação óssea, fazem-no de uma forma contínua e determinística e com poucos mediadores celulares. Bastantes estudos e resultados experimentais revelam que o metabolismo celular, bem como os processos de nascimento e morte em populações dinâmicas, são estocásticos. Para além disso, os modelos matemáticos são fidedignos em descrever um nível macro enquanto modelos de simulação de multiagentes são utilizados para conectar ambas as perspetivas, micro e macro. O modelo estocástico de multiagentes desenvolvido neste trabalho, simula um ciclo de remodelação ao longo do tempo. O nosso simulador reproduz o processo homeostático de remodelação com as diferentes fases deste, o que é consistente com o processo biológico real. Para além disso, o nosso modelo inclui os mediadores celulares mais relevantes no metabolismo ósseo. Os resultados do modelo demonstram ter sensibilidade em prever a perda óssea devido a algumas doenças crónicas como hiper e hipoparatiroidismo e o excesso de glucocorticoides, bem como das mais conhecidas causas de osteoporose: a deficiência de estrogénio e vitamina D. No geral, este modelo permite-nos ter um maior entendimento do metabolismo ósseo, bem como das patologias associadas a este

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)