RESUMO
Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing.
Assuntos
Algoritmos , Processamento de Linguagem Natural , Radiografia Torácica , Brasil , Humanos , Raios XRESUMO
BACKGROUND: Mechanical ventilation can injure lung tissue and respiratory muscles. The aim of the present study is to assess the effect of the amount of spontaneous breathing during mechanical ventilation on patient outcomes. METHODS: This is an analysis of the database of the 'Medical Information Mart for Intensive Care (MIMIC)'-III, considering intensive care units (ICUs) of the Beth Israel Deaconess Medical Center (BIDMC), Boston, MA. Adult patients who received invasive ventilation for at least 48 hours were included. Patients were categorized according to the amount of spontaneous breathing, i.e., ≥50% ('high spontaneous breathing') and <50% ('low spontaneous breathing') of time during first 48 hours of ventilation. The primary outcome was the number of ventilator-free days. RESULTS: In total, the analysis included 3,380 patients; 70.2% were classified as 'high spontaneous breathing', and 29.8% as 'low spontaneous breathing'. Patients in the 'high spontaneous breathing' group were older, had more comorbidities, and lower severity scores. In adjusted analysis, the amount of spontaneous breathing was not associated with the number of ventilator-free days [20.0 (0.0-24.2) vs. 19.0 (0.0-23.7) in high vs. low; absolute difference, 0.54 (95% CI, -0.10 to 1.19); P=0.101]. However, 'high spontaneous breathing' was associated with shorter duration of ventilation in survivors [6.5 (3.6 to 12.2) vs. 7.6 (4.1 to 13.9); absolute difference, -0.91 (95% CI, -1.80 to -0.02); P=0.046]. CONCLUSIONS: In patients surviving and receiving ventilation for at least 48 hours, the amount of spontaneous breathing during this period was not associated with an increased number of ventilator-free days.
RESUMO
OBJECTIVE: Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC. SETTING: Two intensive care units, one private and one public, from São Paulo, Brazil PATIENTS: An ICU for the first time. INTERVENTIONS: None. MEASUREMENTS AND MAINS RESULTS: The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 - 0.86) and standardized mortality ratio of 1.00 (0.91-1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM. CONCLUSIONS: Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.
Assuntos
Estado Terminal/mortalidade , Países em Desenvolvimento , Mortalidade Hospitalar/tendências , Unidades de Terapia Intensiva/estatística & dados numéricos , Modelos Estatísticos , Índice de Gravidade de Doença , Benchmarking , Brasil/epidemiologia , Estado Terminal/epidemiologia , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos RetrospectivosRESUMO
BACKGROUND: Datathons are increasingly organized in the healthcare field. The goal is to assemble people with different backgrounds to work together as a team and engage in clinically relevant research or develop algorithms using health-related datasets. Criteria to assess the return of investment on such events have traditionally included publications produced, patents for prediction, classification, image recognition and other types of software, and start-up companies around the application of machine learning in healthcare. Previous studies have not evaluated whether a datathon can promote affective learning and effective teamwork. METHODS: Fifty participants of a health datathon event in São Paulo, Brazil at Hospital Israelita Albert Einstein (HIAE) were divided into 8 groups. A survey with 25 questions, using the Affective Learning Scale and Team-Review Questionnaire, was administered to assess team effectiveness and affective learning during the event. Multivariate regression models and Pearson's correlation tests were performed to evaluate the effect of affective learning on teamwork. RESULTS: Majority of the participants were male 76% (37/49); 32% (16/49) were physicians. The mean score for learning (scale from 1 to 10) was 8.38, while that for relevance of the perceived teamwork was 1.20 (scale from 1 to 5; "1" means most relevant). Pearson's correlation between the learning score and perception of teamwork showed moderate association (râ¯=â¯0.36, pâ¯=â¯0.009). Five learning and 10 teamwork variables were on average positively graded in the event. The final regression model includes all learning and teamwork variables. Effective leadership was strongly correlated with affective learning (ßâ¯=â¯-0.27, pâ¯<â¯0.01, R2â¯=â¯75%). Effective leadership, team accomplishment, criticism, individual development and creativity were the variables significantly associated with higher levels of affective learning. CONCLUSION: It is feasible to enhance affective knowledge and the skill to work in a team during a datathon. We found that teamwork is associated with higher affective learning from participants' perspectives. Effective leadership is essential for teamwork and is a significant predictor of learning.