Predicting kidney allograft survival with explainable machine learning.

INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning method to identify these variables which may predict the early graft loss in kidney transplant patients and to assess their usefulness for improving clinical decisions. MATERIAL AND METHODS: A retrospective cohort study was carried out with 627 kidney transplant patients followed at least three months. All these data were pre-processed, and their selected features were used to develop an automatically working a machine learning algorithm; this algorithm was then applied for training and parameterization of the model; and finally, the tested model was then used for the analysis of patients' features that were the most impactful for the prediction of clinical outcomes. Our models were evaluated using the Area Under the Curve (AUC), and the SHapley Additive exPlanations (SHAP) algorithm was used to interpret its predictions. RESULTS: The final selected model achieved a precision of 0.81, a sensitivity of 0.61, a specificity of 0.89, and an AUC value of 0.84. In our model, serum creatinine levels of kidney transplant patients, evaluated at the hospital discharge, proved to be the most important factor in the decision-making for the allograft loss. Patients with a weight equivalent to a BMI closer to the normal range prior to a kidney transplant are less likely to experience graft loss compared to patients with a BMI below the normal range. The age of patients at transplantation and Polyomavirus (BKPyV) infection had significant impact on clinical outcomes in our model. CONCLUSIONS: Our algorithm suggests that the main characteristics that impacted early allograft loss were serum creatinine levels at the hospital discharge, as well as the pre-transplant values such as body weight, age of patients, and their BKPyV infection. We propose that machine learning tools can be developed to effectively assist medical decision-making in kidney transplantation.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study.

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Assessment of Human Fetuses Undergoing Acute Pain: Validation of the Fetal-7 Scale.

Improvements in fetal ultrasound have allowed for the diagnosis and treatment of fetal diseases in the uterus, often through surgery. However, little attention has been drawn to the assessment of fetal pain. To address this gap, a fetal pain scoring system, known as the Fetal-7 scale, was developed. The present study is a full validation of the Fetal-7 scale. The validation involved 2 steps: 1) 4 fetuses with the indication of surgery were evaluated in 3 conditions perioperatively: acute pain, rest, and under loud sound stimulation. Facial expressions were assessed by 30 raters using screenshots from 4D high-definition ultrasound films; 2) assessment of sensitivity and specificity of the Fetal-7 scale in 54 healthy fetuses and 2 fetuses undergoing acute pain after preoperative anesthetic intramuscular injection. There was high internal consistency with Cronbach's alpha (α) of .99. Intrarater reliability of the Fetal-7 scale (test-retest) calculated by intraclass correlation coefficient was .95, and inter-rater reliability was .99. The scale accurately differentiated between healthy fetuses at rest and those experiencing acute pain (sensitivity of 100% and specificity of 94.4%). The Fetal-7 scale is a valid tool for assessing acute pain-related behavior in third-trimester fetuses and may be of value in guiding analgesic procedures efficacy in these patients. Further research is warranted to explore the presence of postoperative pain in fetuses and its effects after birth. PERSPECTIVE: Recordings with 3-dimensional ultrasound of human fetuses undergoing preoperative anesthetic injections revealed complex facial expressions during acute pain, similar to those collected in newborns. This study presented the validation process and cut-off value of the Fetal-7 scale, paving the way for the study of pain before birth in humans.

miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach.

Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD-related miRNAs in cerebrospinal fluid (CSF) found in the GeoDataSets, evaluated by machine learning, with miRNAs listed in a systematic review, and a pathway analysis. Using machine learning approaches, we identified most differentially expressed miRNAs in Gene Expression Omnibus (GEO), which were validated by the systematic review, using the acronym PECO-Population (P): Patients with AD, Exposure (E): expression of miRNAs, Comparison (C): Healthy individuals, and Objective (O): miRNAs differentially expressed in CSF. Additionally, pathway enrichment analysis was performed to identify the main pathways involving at least four miRNAs selected. Four miRNAs were identified for differentiating between patients with and without AD in machine learning combined to systematic review, and followed the pathways analysis: miRNA-30a-3p, miRNA-193a-5p, miRNA-143-3p, miRNA-145-5p. The pathways epidermal growth factor, MAPK, TGF-beta and ATM-dependent DNA damage response, were regulated by these miRNAs, but only the MAPK pathway presented higher relevance after a randomic pathway analysis. These findings have the potential to assist in the development of diagnostic tests for AD using miRNAs as biomarkers, as well as provide understanding of the relationship between different pathophysiological mechanisms of AD.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal / Desenvolvimento de rede neural convolucional para o diagnóstico radiográfico de osteoartrite dos joelhos no ELSA-Brasil Musculoesquelético

Abstract Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Resumo Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrangendo todos os exames da linha de base do ELSA-Brasil Musculoesquelético (5.660 radiografias dos joelhos em incidência posteroanterior). Os exames foram interpretados por médico radiologista com treinamento específico e calibração previamente publicada. Resultados: A RNC desenvolvida apresentou área sob a curva característica de operação do receptor de 0,866 (IC 95%: 0,842-0,882). O modelo pode ser calibrado para alcançar, não simultaneamente, valores máximos de 0,907 para acurácia, 0,938 para sensibilidade e 0,994 para especificidade. Conclusão: A RNC desenvolvida pode ser utilizada como ferramenta de triagem, reduzindo o número total de exames avaliados pelos radiologistas do estudo, e/ou como ferramenta de segunda leitura, contribuindo com a redução de possíveis erros de interpretação.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal.

Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrangendo todos os exames da linha de base do ELSA-Brasil Musculoesquelético (5.660 radiografias dos joelhos em incidência posteroanterior). Os exames foram interpretados por médico radiologista com treinamento específico e calibração previamente publicada. Resultados: A RNC desenvolvida apresentou área sob a curva característica de operação do receptor de 0,866 (IC 95%: 0,842-0,882). O modelo pode ser calibrado para alcançar, não simultaneamente, valores máximos de 0,907 para acurácia, 0,938 para sensibilidade e 0,994 para especificidade. Conclusão: A RNC desenvolvida pode ser utilizada como ferramenta de triagem, reduzindo o número total de exames avaliados pelos radiologistas do estudo, e/ou como ferramenta de segunda leitura, contribuindo com a redução de possíveis erros de interpretação.

Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus.

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

Ectasia Risk Model: A Novel Method Without Cut-off Point Based on Artificial Intelligence Improves Detection of Higher-Risk Eyes.

PURPOSE: To develop a new ectasia risk model through artificial intelligence (AI) and machine learning, enabling the creation of an integrated method without a cut-off point per risk factor, and subsequently better at differentiating patients at higher risk of ectasia with normal topography. METHODS: This comparative case-control study included 339 eyes with normal preoperative topography, with 65 eyes that developed ectasia after laser in situ keratomileusis (ectasia group) and 274 eyes that did not develop ectasia (control group). The AI model used known risk factors to engineer 14 additional ones, totaling 20 features. In this methodology, no variable is used in isolation because its cut-off point is never considered. All separation between cases and controls is made through the interaction detected by the machine learning model that gathers the variables considered relevant. The ability to correctly separate ectatic cases identified as high risk, ectatic cases wrongly classified as low risk, and controls were illustrated by the diagram t-distributed stochastic neighbor embedding (t-SNE). RESULTS: Only two original variables (percent tissue altered and corneal thickness) and two derived from the feature engineering process (derivative percent tissue altered and age weighted value) were selected by the final AI model (ie, best performing AI-based model to separate patients at higher risk). The t-SNE visualization demonstrated the greater ability to differentiate between patients considered at risk by the AI-based model, without a cut-off point, compared to all other methods used alone (P < .0001). CONCLUSIONS: This study describes a new AI-based model that integrates different risk factors without a cut-off point, increasing the number of cases correctly identified as at higher risk. [J Refract Surg. 2022;38(11):716-724.].

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil.

BACKGROUND: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease. OBJECTIVE: This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil. METHODS: We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations. RESULTS: We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window. CONCLUSION: Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

A Novel Panel of Plasma Proteins Predicts Progression in Prodromal Alzheimer's Disease.

BACKGROUND: A cheap and minimum-invasive method for early identification of Alzheimer's disease (AD) pathogenesis is key to disease management and the success of emerging treatments targeting the prodromal phases of the disease. OBJECTIVE: To develop a machine learning-based blood panel to predict the progression from mild cognitive impairment (MCI) to dementia due to AD within a four-year time-to-conversion horizon. METHODS: We created over one billion models to predict the probability of conversion from MCI to dementia due to AD and chose the best-performing one. We used Alzheimer's Disease Neuroimaging Initiative (ADNI) data of 379 MCI individuals in the baseline visit, from which 176 converted to AD dementia. RESULTS: We developed a machine learning-based panel composed of 12 plasma proteins (ApoB, Calcitonin, C-peptide, CRP, IGFBP-2, Interleukin-3, Interleukin-8, PARC, Serotransferrin, THP, TLSP 1-309, and TN-C), and which yielded an AUC of 0.91, accuracy of 0.91, sensitivity of 0.84, and specificity of 0.98 for predicting the risk of MCI patients converting to dementia due to AD in a horizon of up to four years. CONCLUSION: The proposed machine learning model was able to accurately predict the risk of MCI patients converting to dementia due to AD in a horizon of up to four years, suggesting that this model could be used as a minimum-invasive tool for clinical decision support. Further studies are needed to better clarify the possible pathophysiological links with the reported proteins.

Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning.

Background: The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person's blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes. Methods: We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil. Results: We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses. Conclusions: We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios-both during the pandemic and after.

Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations.

The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasing importance in the ICU setting. However, they generally present the black-box issue because they do not provide meaningful information about the logic involved in patient-specific predictions. Fortunately, effective algorithms exist for explaining black-box models, and in practice, they offer valuable explanations for model predictions. These explanations are becoming essential to engender trust and accreditation to the model. However, once the model is implemented, a major issue is whether it will continue to employ the same prediction logic as originally intended to. To build our models, features are obtained from patient administrative data, laboratory results and vital signs available within the first hour after ICU admission. This enables our models to provide great anticipation because complications can occur at any moment during ICU stay. To build models that continue to work as originally designed we first propose to measure (i) how the provided explanations vary for different inputs (that is, robustness), and (ii) how the provided explanations change with models built from different patient sub-populations (that is, stability). Second, we employ these measures as regularization terms that are coupled with a feature selection procedure such that the final model provides predictions with more robust and stable explanations. Experiments were conducted on a dataset containing 6000 ICU admissions of 5474 patients. Results obtained on an external validation cohort of 1069 patients with 1086 ICU admissions showed that selecting features based on robustness led to gains in terms of predictive power that varied from 6.8% to 9.4%, whereas selecting features based on stability led to gains that varied from 7.2% to 11.5%, depending on the target complication. Our results are of practical importance as our models predict complications with great anticipation, thus facilitating timely and protective interventions.

Explainable death toll motion modeling: COVID-19 data-driven narratives.

Models have gained the spotlight in many discussions surrounding COVID-19. The urgency for timely decisions resulted in a multitude of models as informed policy actions must be made even when so many uncertainties about the pandemic still remain. In this paper, we use machine learning algorithms to build intuitive country-level COVID-19 motion models described by death toll velocity and acceleration. Model explainability techniques provide insightful data-driven narratives about COVID-19 death toll motion models-while velocity is explained by factors that are increasing/reducing death toll pace now, acceleration anticipates the effects of public health measures on slowing the death toll pace. This allows policymakers and epidemiologists to understand factors driving the outbreak and to evaluate the impacts of different public health measures.

Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning.

Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high-cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97-0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95-0.98) using a miniaturized spectral range (896-1039 cm-1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.

Bone Flap Conservation in the Scalp After Decompressive Craniectomy.

BACKGROUND: Decompressive craniectomy may be used as a primary or secondary treatment for intracranial hypertension and is clearly associated with reduced mortality. The removed bone flap is usually preserved in the abdominal subcutaneous tissue or in the bone bank. The aim of this study was to describe an option for preserving the bone flap after decompressive craniectomy using bone flap preservation in the skull subcutaneous tissue in subgaleal space over the pericranium contralateral to the craniectomy site. METHODS: This was a multicenter retrospective study including patients with severe traumatic brain injury from 2014 to 2016. There were 23 patients who had their bone fragments preserved below the scalp in the subcutaneous tissue for analysis. The following results were analyzed: surgical site infection, bone flap resorption during the period of preservation, and patient discomfort. RESULTS: Five patients died of systemic infectious complications, and the remaining patients underwent cranioplasty a mean 118 days after craniectomy. There were no surgical wound infections, macroscopically evident bone absorption, or site discomfort in any of the patients during a period of 18 months. CONCLUSIONS: This variant of the bone flap preservation technique has been shown to be satisfactory as an option for routine use.

Practical detection of spammers and content promoters in online video sharing systems.

A number of online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, content promoters may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we first manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimate users. Using our test collection, we provide a characterization of content, individual, and social attributes that help distinguish each user class. We then investigate the feasibility of using supervised classification algorithms to automatically detect spammers and promoters, and assess their effectiveness in our test collection. While our classification approach succeeds at separating spammers and promoters from legitimate users, the high cost of manually labeling vast amounts of examples compromises its full potential in realistic scenarios. For this reason, we further propose an active learning approach that automatically chooses a set of examples to label, which is likely to provide the highest amount of information, drastically reducing the amount of required training data while maintaining comparable classification effectiveness.

[Decompressive craniectomy for treatment of intracranial hypertension secondary to large ischemic cerebral infarction: analysis of 34 cases]. / Craniectomia descompressiva para tratamento de hipertensão intracraniana secundária a infarto encefálico isquêmico extenso: análise de 34 casos.

UNLABELLED: The large ischemic cerebral infarction (LICI) is a blood supply loss of a large area in the brain, mainly on the middle cerebral artery. Is possible that evolutes a major edema, intracranial hypertension and death in about 80% of the cases. OBJECTIVE: To evaluate the results of a decompressive craniectomy on the treatment of the secondary intracranial hypertension to LICI, comparing to other results of medical literature already published. METHOD: Were analysed 34 patients diagnosed with LICI clinically treated unsuccessfully that needed further on the decompressive craniectomy treatment, for the control of intracranial hypertension. RESULTS: 8 patients (23.52%) died, 26 (76.47%) survived, and 2 (7.70%) developed a vegetative state condition. CONCLUSION: The factors age over 50 years and male gender were associated with a high death risk. The level of consciousness at admission and bone flap area were nearer the values of statistic significance.

Craniectomia descompressiva para tratamento de hipertensão intracraniana secundária a infarto encefálico isquêmico extenso: análise de 34 casos / Decompressive craniectomy for treatment of intracranial hypertension secondary to large ischemic cerebral infarction: analysis of 34 cases

Infarto encefálico isquêmico extenso (IEIE) é a perda do suprimento sangüíneo de uma grande área cerebral, principalmente do território da artéria cerebral média. Pode evoluir com edema importante, hipertensão intracraniana e óbito em até 80 por cento dos casos. OBJETIVO: Avaliar os resultados da craniectomia descompressiva no tratamento da hipertensão intracraniana secundária ao IEIE, comparando com os resultados de outros estudos publicados na literatura. MÉTODO: Foram analisados 34 pacientes com IEIE tratados clinicamente sem sucesso e que necessitaram de craniectomia descompressiva para controle da hipertensão intracraniana. RESULTADOS: 8 pacientes (23,52 por cento) faleceram, 26 (76,47 por cento) sobreviveram, sendo que 2 (7,70 por cento) permaneceram em estado vegetativo. CONCLUSÃO: Os fatores idade acima de 50 anos e sexo masculino se associaram a maior risco de evolução para óbito. O nível de consciência à admissão e a área do retalho ósseo apresentaram valores próximos de significância estatística.

The large ischemic cerebral infarction (LICI) is a brain supply loss of a large area in the brain, mainly on the middle cerebral artery. Is possible that evolutes a major edema, intracranial hypertension and death in about 80 percent of the cases. OBJECTIVE: To avaliate the results of a descompressive craniectomy on the treatment of the secundary intracranial hypertension to LICI, comparing to other results of medical literature already published. METHOD: Were analysed 34 pacients diagnosed with LICI clinically treated unsuccesfully that needed forther on the decompressive craniectomy treatment, for the control of intracranial hypertension. RESULTS: 8 pacients (23.52 percent) died, 26 (76.47 percent) survived, and 2 (7.70 percent) developed a vegetative state condition. CONCLUSION: The factors age over 50 years and male gender were associated with a high death risk. The level of consciousness at admission and bone flap area were nearer the values of statistic significance.

[Transspheinoidal transpalatal meningoencephalocele]. / Meningoencefalocele transesfenoidal transpalatina.

Transspheinoidal meningoencephalocele is a rare presentation of dysraphism of the neural tube. It is characterized by the herniation of the neural tissue through a bony defect in the sphenoid bone. The clinical presentation is variable. It may be assymptomatic or it may include an upper airway obstruction, rhinorrhea, meningitis, hypothalamic dysfunction and optic anomalies. The surgical treatment is controversial. We describe the case of a 7-year-old boy who presented a pulsate structure filling the palate, palate digenesis and hypertelorism. The diagnosis of transsphenoidal transpalatal meningoencephalocele was confirmed by a computerized tomography and a magnetic resonance imaging. The child was operated on by the transpalatal/transspheinoidal approach with a good result.

Meningoencefalocele transesfenoidal transpalatina / Transspheinoidal transpalatal meningoencephalocele / Transspheinoidal transpalatal meningoencephalocele

A meningoencefalocele transesfenoidal é rara apresentação dos disrafismos do tubo neural. Caracteriza-se pela herniação do tecido neural através de uma falha óssea no osso esfenóide. A apresentação clínica é variável, podendo ser desde assintomática até obstrução das vias aéreas, rinorréia, meningite, disfunção hipotalâmica e anomalias ópticas. Seu tratamento cirúrgico é controverso. Descrevemos o caso de um menino de 7 anos que apresentava uma estrutura pulsátil preenchendo o teto da cavidade oral, disgenesia palatina e hipertelorismo. O diagnóstico de meningoencefalocele transesfenoidal transpalatina foi confirmado pela tomografia computadorizada e ressonância magnética. A criança foi operada com sucesso pela via transpalatal/transesfenoidal.

Transspheinoidal meningoencephalocele is a rare presentation of dysraphism of the neural tube. It is characterized by the herniation of the neural tissue through a bony defect in the sphenoid bone. The clinical presentation is variable. It may be assymptomatic or it may include an upper airway obstruction, rhinorrhea, meningitis, hypothalamic dysfunction and optic anomalies. The surgical treatment is controversial. We describe the case of a 7-year-old boy who presented a pulsate structure filling the palate, palate digenesis and hypertelorism. The diagnosis of transsphenoidal transpalatal meningoencephalocele was confirmed by a computerized tomography and a magnetic resonance imaging. The child was operated on by the transpalatal/transspheinoidal approach with a good result.