ALFREDO MANOEL DA SILVA FERNANDES
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Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina - Médico
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conferenceObject The EuroScore Additive is a Valid Measure to Predict Mortality in Cardiac Surgery?(2013) CHANG JUNIOR, Joao; FERNANDES, Alfredo Manoel da Silva; PANAINO, Anibal Claudio; GUERRERO, Gabriela Favaro F.In the last years advancements of cardiological research in Brazil were very significant, both at the academy, such as in hospitals and clinics, generating not only an increase in the number of publications, but also an improvement in the results achieved. The cardiological surgery has achieved excellent results, because has reduced the mortality tax and has increased life expectancy of people who need this type of surgery. This article aims to evaluate the effectiveness of an instrument called EuroScore Additive (ES), which is used to evaluate the risk of death in patients undergoing cardiological surgery. So this, the research had used applied statistical methodology to analyze data of 238 patients who have undergone valve surgery on a public Hospital specializing in cardiological surgery, in a period of two years. The results show the limitations of the EuroScore Additive in predicting the outcome of valve surgery.- SIMULATION OF THE AMBULATORY PROCESSES IN THE BIGGEST BRAZILIAN CARDIOLOGY HOSPITAL: A PETRI NET APPROACH(2021) LIMA, Fabio; CORTEZ, Matheus Felipe; SCHMIDT, Patricia Pessoa; SILVERI, Ana Karoline; FERNANDES, Alfredo Manoel da Silva; CHANG JUNIOR, JoaoThis paper presents a simulation of an ambulatory processes using timed Petri net (TPN). The simulation considers the flow of patients in the biggest Brazilian cardiology hospital. The TPN is used as a decision support system (DSS) to improve the processes, to reduce the waiting time of the patients in the ambulatory and in this way to assure a high-quality service to the patients. Simulations were carried out using the software Visual Object Net++. This is a free software and therefore the presented solution is a low-cost solution. Providing a low-cost solution has a huge importance in this work since the hospital is kept from the government efforts and operates with limited financial resources. The patients' flow in the hospital can be faced as a service and the modelling and optimization of these services bring more efficiency to the system as well as improve the human factors involved. The results proved that some changes could be made in the processes to improve the performance of the system.
- Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study(2020) CHANG JUNIOR, Joao; BINUESA, Fabio; CANEO, Luiz Fernando; TURQUETTO, Aida Luiza Ribeiro; ARITA, Elisandra Cristina Trevisan Calvo; BARBOSA, Aline Cristina; FERNANDES, Alfredo Manoel da Silva; TRINDADE, Evelinda Marramon; JATENE, Fabio Biscegli; DOSSOU, Paul-Eric; JATENE, Marcelo BiscegliBackground Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce. Objectives Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery. Methods Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT). Results The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and pulmonary atresia. These combined predictor variables represent 67.8% of importance for the risk of mortality in the Random Forest algorithm. Conclusions The representativeness of ""hospital death"" is greater in patients up to 66 cm in height and body mass index below 13.0 for InCor's patients. The proportion of ""hospital death"" declines with the increased arterial oxygen saturation index. Patients with prior hospitalization before surgery had higher ""hospital death"" rates than who did not required such intervention. The diagnoses groups having the higher fatal outcomes probability are aligned with the international literature. A web application is presented where researchers and providers can calculate predicted mortality based on the CgntSCORE on any web browser or smartphone.
article Telecardiology guideline in Patient Care with Acute Coronary Syndrome and Other Respiratory Diseases(2015) OLIVEIRA JUNIOR, Mucio Tavares de; CANESIN, Manoel Fernandes; MARCOLINO, Milena Soriano; RIBEIRO, Antonio Luiz Pinho; CARVALHO, Antonio Carlos de Camargo; REDDY, Shankar; SANTOS, Adson Roberto Franca dos; FERNANDES, Alfredo Manoel da Silva; AMARAL, Amaury Zatorre; REZENDE, Ana Carolina de; NECHAR JUNIOR, Antonio; NASCIMENTO, Bruno Ramos do; PASTORE, Carlos Alberto; WEN, Chao Lung; GUALANDRO, Danielle Menosi; NAPOLI, Domingos Guilherme; FRANCA, Francisco Faustino A. C.; FEITOSA-FILHO, Gilson Soares; SAAD, Jamil Abdalla; PILLI, Jeanne; PAULA, Leonardo Jorge Cordeiro de; LODI-JUNQUEIRA, Lucas; CESAR, Luis Antonio Machado; BODANESE, Luiz Carlos; GUTIERREZ, Marco Antonio; ALKMIM, Maria Beatriz Moreira; NUNES, Mauricio Batista; MEDEIROS, Orlando Otavio de; MORENO, Ramon Alfredo; GUNDIM, Rosangela Simoes; MONTENEGRO, Sergio Tavares; NAZIMA, Willyan Issamu- COMPUTER SIMULATION MODEL FOR OUTPATIENT CLINICS IN A BRAZILIAN LARGE PUBLIC HOSPITAL SPECIALIZED IN CARDIOLOGY(2019) CHANG JUNIOR, Joao Chang; LIMA, Fabio; FERNANDES, Alfredo Manoel da Silva; GUARDIA, Felipe de Almeida; SILVA, Vanessa Dias da; MACCHERI, Giovanni AugustoGoal: the main objective of this study is to analyze the behavior of the outpatient department of a large public hospital specialized in cardiology, understanding how the components of this system are related, in order to improve the hospital's performance. Design/Methodology/Approach: a case study was carried out in a public hospital specializing in cardiology with the aid of Modeling and Simulation of System Dynamics. Results: the result showed that variables such as doctor availability and average consultation time have great influence on the service capacity. Limitations of the investigation: the proceedings and times related to the medical staff are particular to each team and they are not standardized. However, in the system dynamics modeling these particularities cannot be included. Practical implications: for theory, there is the state-of-the-art development in terms of how to manage and regarding the methodologies should be applied in a complex referential model composed of several moderating variables, in order to obtain the best use of the available resources (human and material) of the hospital. For practice, the flow of patients in the hospital should be predicted and optimized, adding value to the services provided to its users. Originality/Value: the originality of the work is based on the unprecedented application of quantitative methods for solving problems in Brazilian hospitals.
conferenceObject Operating Rooms Optimization in a Cardiology Public School Hospital: The Joint and Sequential Use of the Models of Min and Belien(2016) CHANG JUNIOR, Joao; CARVALHO, Talles Newton de; SANTOS, Suzana Bierrenbach de Souza; FERNANDES, Alfredo Manoel da SilvaThis study uses joint and sequential models of Min and Belien to allocate elective cardiac patients in a finite number of operating rooms at any given day of the week, with particular medical staff, in a cardiology public school hospital. Currently, due to the process being empirical, most patients awaiting surgery are at a critical medical condition. The lack of systematic programming also causes a prolonged wait time for scheduling, which further aggravates the patient's condition and extends his or her stay in the hospital. With this, the risk of the patient collapsing and having to undergo an emergency surgery increases, leading to imbalance of the surgical center's routine and hindering the surgical program. The joint use of the two models provides a weekly schedule of surgeries, prioritizing patients by level of criticality and increasing the level of use of post-operative beds. Therefore, a reduction of 29% was obtained in quantities of beds needed to meet the demand of the surgical hospital. The daily and weekly occupancy of these beds was maintained, thus avoiding the oscillations between idleness and burden on the medical staff. This work has used mathematical models to focus on optimization of scheduling elective surgeries, with attention to the level of critically of patients present in the current waiting list for their surgeries.