3 resultados para dynamic causal modeling


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The purpose of our study was to evaluate the accuracy of dynamic incremental bolus-enhanced conventional CT (DICT) with intravenous contrast administration, early phase, in the diagnosis of malignancy of focal liver lesions. A total of 122 lesions were selected in 74 patients considering the following criteria: lesion diameter 10 mm or more, number of lesions less than six per study, except in multiple angiomatosis and the existence of a valid criteria of definitive diagnosis. Lesions were categorized into seven levels of diagnostic confidence of malignancy compared with the definitive diagnosis for acquisition of a receiver-operator-characteristic (ROC) curve analysis and to determine the sensitivity and specificity of the technique. Forty-six and 70 lesions were correctly diagnosed as malignant and benign, respectively; there were 2 false-positive and 4 false-negative diagnoses of malignancy and the sensitivity and specificity obtained were 92 and 97%. The DICT early phase was confirmed as a highly accurate method in the characterization and diagnosis of malignancy of focal liver lesions, requiring an optimal technical performance and judicious analysis of existing semiological data.

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OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.