Defeitos do Tubo Neural. Experiência de 10 Anos numa Maternidade Central

OBJECTIVES: 1) To determine trends in prevalence of neural tube defects and the impact of therapeutic abortion. 2) To review perinatal management of spina bifida. DESIGN: All spontaneous and therapeutic abortions, still births and live births affected by neural tube defects registered in Alfredo da Costa Maternity in Lisbon, from 1983 to 1992, were retrospectively analysed. RESULTS: Eighty-two cases with neural tube defects are reported and myelomeningocele and anencephaly++ were the most frequent ones. Total prevalence for all defects was 0.78:1000 births with a small upward trend during the last two years. Birth prevalence was 0.6:1000, with a clear downward trend, due to therapeutic abortion. Prenatal diagnosis improved significantly, from 9% of all defects detected in 1983-87 to 77.5% in 1988-92. Since 1989, all cases of anencephaly were detected before birth. Most cases of spina bifida were vaginally delivered, and elective cesarean section occurred in 4. Early closure of the defect was undertaken in 87.6% of the newborns with open spina bifida. CONCLUSION: While total prevalence of neural tube defects remained stable, with only a small upward trend, prenatal diagnosis and therapeutic abortion resulted in a 56.3% fall in birth prevalence. Optimal management of open spina bifida demands a multidisciplinary team with an individual program for each case.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

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.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.