ALMIR FERREIRA DE ANDRADE
Projetos de Pesquisa
Unidades Organizacionais
Instituto Central, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/62 - Laboratório de Fisiopatologia Cirúrgica, Hospital das Clínicas, Faculdade de Medicina
LIM/62 - Laboratório de Fisiopatologia Cirúrgica, Hospital das Clínicas, Faculdade de Medicina
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- Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population(2020) AMORIM, Robson Luis; OLIVEIRA, Louise Makarem; MALBOUISSON, Luis Marcelo; NAGUMO, Marcia Mitie; SIMOES, Marcela; MIRANDA, Leandro; BOR-SENG-SHU, Edson; BEER-FURLAN, Andre; ANDRADE, Almir Ferreira De; RUBIANO, Andres M.; TEIXEIRA, Manoel Jacobsen; KOLIAS, Angelos G.; PAIVA, Wellingson SilvaBackground: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in Sao Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members.
- The influence of intracranial hypertension on static cerebral autoregulation(2020) DE-LIMA-OLIVEIRA, Marcelo; FERREIRA, Almir Andrade; BELON, Alessandro Rodrigo; SALINET, Angela Macedo; NOGUEIRA, Ricardo Carvalho; PING, Brasil Chian; PAIVA, Wellingson Silva; TEIXEIRA, Manoel Jacobsen; BOR-SENG-SHU, EdsonObjectives To analyze the influences of mild and severe intracranial hypertension on cerebral autoregulation (CA). Patients and Methods Duroc piglets were monitored with an intracranial pressure (ICP) catheter. Intracranial hypertension was induced via infusion of 4 or 7 ml of saline solution by a bladder catheter that was inserted into the parietal lobe. The static cerebral autoregulation (sCA) index was evaluated via cerebral blood flow velocities (CBFv). Piglets with ICPs <= 25 and > 25 mmHg were considered as group 1 and 2, respectively. Continuous variables were evaluated using the Kolmogorov-Smirnov goodness-of-fit test. The main parameters were collected before and after ICH induction and compared using two-factor mixed-design ANOVAs with the factor of experimental group (mild and severe ICH). Results In group 1 (ICP <= 25 mmHg), there were significant differences in sCA (p= .01) and ICP (p= .0002) between the basal and balloon inflation conditions. In group 2 (ICP > 25 mmHg), there were significant differences in CBFv (p= .0072), the sCA index (p= .0001) and ICP (p= .00001) between the basal and balloon inflation conditions. Conclusion We conclude that ICH may have a direct effect on mild and severe sCA.
- Cognitive Assessment in Patients with Traumatic Brain Injury(2020) ZANINOTTO, Ana Luiza; GUIRADO, Vinicius Monteiro de Paula; LUCIA, Mara Cristina Souza De; ANDRADE, Almir Ferreira de; TEIXEIRA, Manoel Jacobsen; PAIVA, Wellingson SilvaTraumatic brain injury (TBI) is a major public health problem in Western countries. ATBI brings many negative consequences, including behavioral and cognitive changes, which affect social adjustment and the performance of functional activities. Cognitive evaluation after TBI is a complex issue in what pertains to definition of the most appropriate questionnaires for clinical use in a comprehensive analysis of the condition of the patient. In this paper, we described a critical review of the main cognitive assessment tests currently used in clinical and research settings in patients with TBI.
- Tranexamic acid for traumatic brain injury(2020) SOLLA, Davi J. Fontoura; RUBIANO, Andres Mariano; TEIXEIRA, Manoel Jacobsen; ANDRADE, Almir Ferreira de; PAIVA, Wellingson Silva
article New technique for surgical decompression in traumatic brain injury: merging two concepts to prevent early and late complications of unilateral decompressive craniectomy with dural expansion(2020) ANDRADE, Almir Ferreira de; AMORIM, Robson Luis; SOLLA, Davi Jorge Fontoura; ALMEIDA, Cesar Cimonari; FIGUEIREDO, Eberval Gadelha; TEIXEIRA, Manoel Jacobsen; PAIVA, Wellingson SilvaIntroduction: Decompressive craniectomy (DC) in severe traumatic brain injury (TBI) is associated with acute and late complications. To avoid these complications, we proposed a technical modification in DC. In this paper analyze a series of patients underwent to surgical treatment for acute subdural hematoma (ASDH). Methods: We perform a prospective cohort with TBI patients undergoing DC for treatment of diffuse hemispheric brain swelling and ASDH. The effect of modified craniectomy was assessed using postoperative CT. Clinical outcome was evaluated at ICU mortality in 2 weeks. Results: Comparing the CT scans before and after surgery, the midline shift decreases from median of 11 mm to 5.5 mm (P<0.001). Only one patient had presented uncontrolled intracranial hypertension after surgery. Postoperative mortality in the intensive care unit within 14 days was 48.8%. Conclusion: this is an interesting technical modification. In this pilot study, we observed ICP control, avoiding the complications of classical decompression.