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OBJECTIVE: The variation in mortality rates of intensive care unit oncological patients may imply that clinical characteristics and prognoses are very different between specific subsets of patients with cancer. The specific characteristics of patients with cancer have not been included as risk factors in the established severity-of-illness scoring systems and comorbidity scores, showing limitations in predicting mortality risk. This study aimed to devise a predictive tool for in-hospital mortality for adult patients with a respiratory neoplasm admitted to the intensive care unit, using an artificial neural network. METHODS: A total of 1,221 stays in the intensive care unit from the Beth Israel Deaconess Medical Center were studied. The primary endpoint was the all-cause in-hospital mortality prediction. An artificial neural network was developed and compared with six severity-of-illness scores and one comorbidity score. Model building was based on important predictors of lung cancer mortality, such as several laboratory parameters, demographic parameters, organ-supporting treatments, and other clinical information. Discrimination and calibration were assessed. RESULTS: The AUROC for the multilayer perceptron was 0.885, while it was <0.74 for the conventional systems. The AUPRC for the multilayer perceptron was 0.731, whereas it was ≤0.482 for the conventional systems. The superiority of multilayer perceptron was statistically significant for all pairwise AUROC and AUPRC comparisons. The Brier Score was better for the multilayer perceptron (0.109) than for OASIS (0.148), SAPS III (0.163), and SAPS II (0.154). CONCLUSION: Discrimination was excellent for multilayer perceptron, which may be a valuable tool for assessing critically ill patients with lung cancer.

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ABSTRACT Objective: The variation in mortality rates of intensive care unit oncological patients may imply that clinical characteristics and prognoses are very different between specific subsets of patients with cancer. The specific characteristics of patients with cancer have not been included as risk factors in the established severity-of-illness scoring systems and comorbidity scores, showing limitations in predicting mortality risk. This study aimed to devise a predictive tool for in-hospital mortality for adult patients with a respiratory neoplasm admitted to the intensive care unit, using an artificial neural network. Methods: A total of 1,221 stays in the intensive care unit from the Beth Israel Deaconess Medical Center were studied. The primary endpoint was the all-cause in-hospital mortality prediction. An artificial neural network was developed and compared with six severity-of-illness scores and one comorbidity score. Model building was based on important predictors of lung cancer mortality, such as several laboratory parameters, demographic parameters, organ-supporting treatments, and other clinical information. Discrimination and calibration were assessed. Results: The AUROC for the multilayer perceptron was 0.885, while it was <0.74 for the conventional systems. The AUPRC for the multilayer perceptron was 0.731, whereas it was ≤0.482 for the conventional systems. The superiority of multilayer perceptron was statistically significant for all pairwise AUROC and AUPRC comparisons. The Brier Score was better for the multilayer perceptron (0.109) than for OASIS (0.148), SAPS III (0.163), and SAPS II (0.154). Conclusion: Discrimination was excellent for multilayer perceptron, which may be a valuable tool for assessing critically ill patients with lung cancer.

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BACKGROUND AND OBJECTIVE: Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scoring systems to anticipate the risk of mortality in the MICU. Machine Learning (ML) models such as probabilistic graphical models and Extreme Gradient Boosting (XGB) have demonstrated improved prediction accuracy in patient outcomes compared to LR. The aim was to compare three ML models to the SAPS, SAPS II, SAPS III, SOFA, serial SOFA, LODS, and OASIS for prediction of MICU mortality. METHODS: A Bayesian Network (BN), Naïve Bayes network (NB), and a XGB model were developed. 9893 adult MICU-stays from the MIMIC-III database were studied. The primary outcome was MICU mortality prediction and the secondary outcome was 1-year mortality prediction. Data analyzed consisted on routine physiological measurements collected during 5 hours in the MICU, demographic and diagnoses/procedure features. The performance was evaluated by accuracy statistics, discrimination and calibration measures. Limitations of the study were discussed. RESULTS: The AUROC for MICU mortality prediction was 0.919 for XGB, 0.905 for BN, and 0.864 for NB, while the conventional systems displayed much lower values with the serial SOFA having the best value (0.814). The Diagnostic Odds Ratio was ≤7.099 for all the conventional systems, reaching values of 30.115 for XGB and 22.648 for BN. The XGB achieved a sensitivity of 0.831 and specificity of 0.86 assuring an acceptable precision (0.528), whose values were much lower for the conventional systems. The Brier score was better for the ML models, except for the NB (0.119), with 0.072 for XGB and 0.081 for BN. CONCLUSIONS: The XGB and BN substantially outperformed the conventional systems for discrimination, calibration and the accuracy statistics assessed. The NB showed inferior performance to the XGB and BN but improved the discrimination and all accuracy statistics of the conventional systems except for an inferior calibration and 1-year mortality discrimination. The XGB showed the best performance among all models. These ML models have the potential to improve the monitoring of MICU patients, which must be evaluated in future studies.

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Most established severity-of-illness systems used for prediction of intensive care unit (ICU) mortality were developed targeted at the general ICU population, based on logistic regression (LR). To date, no dynamic predictive tool for ICU mortality has been developed targeted at the Cardiac Surgery Recovery Unit (CSRU) and Coronary Care Unit (CCU) using machine learning (ML). CSRU and CCU adult patients from the MIMIC-III critical care database were studied. The ML methods developed extract ICU data during a 5-h window and demographic features to produce mortality predictions and were compared to six established severity-of-illness systems and LR. In a secondary experiment, additional procedure/surgery and ICU features were added to the models. The ML models developed were the Tree Ensemble (TE), Random Forest, XGBoost Tree Ensemble (XGB), Naive Bayes (NB), and Bayesian network. The discrimination, calibration and accuracy statistics were assessed. The AUROC values were superior for the ML models reaching 0.926 and 0.924 for the XGB, and 0.904 and 0.908 for the TE for ICU mortality prediction in the primary and secondary experiments respectively. Among the conventional systems, the serial SOFA obtained the highest AUROC (0.8405). The Brier score was better for the ML models except the NB over the conventional systems. The accuracy statistics less sensitive to unbalanced cohorts were higher for all the ML models. In conclusion, the predictive power of XGB was excellent, substantially outperforming the conventional systems and LR. The ML models developed in this work offer promising results that could benefit CSRU and CCU.

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OBJECTIVE: To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, and a logistic regression model built on the same platform. METHODS: A gradient-boosted decision trees model and a logistic regression model were trained and tested with the Medical Information Mart for Intensive Care database. The 1-hour resolution physiological measurements of adult patients, collected during 5 hours in the intensive care unit, consisted of eight routine clinical parameters. The study addressed how the models learn to categorize patients to predict intensive care unit mortality or survival within 12 hours. The performance was evaluated with accuracy statistics and the area under the Receiver Operating Characteristic curve. RESULTS: The gradient-boosted trees yielded an area under the Receiver Operating Characteristic curve of 0.89, compared to 0.806 for the logistic regression. The accuracy was 0.814 for the gradient-boosted trees, compared to 0.782 for the logistic regression. The diagnostic odds ratio was 17.823 for the gradient-boosted trees, compared to 9.254 for the logistic regression. The Cohen's kappa, F-measure, Matthews correlation coefficient, and markedness were higher for the gradient-boosted trees. CONCLUSION: The discriminatory power of the gradient-boosted trees was excellent. The gradient-boosted trees outperformed the logistic regression regarding intensive care unit mortality prediction. The high diagnostic odds ratio and markedness values for the gradient-boosted trees are important in the context of the studied unbalanced dataset.

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Non-thyroid disorders may modify thyroid hormone metabolism, resulting in an 'euthyroid sick syndrome'. Studies determining the association of cardiopulmonary bypass to thyroid function showed changes in line with this euthyroid sick syndrome. In some cases, cardiovascular dysfunction after cardiac surgery with cardiopulmonary bypass is comparable to that noticed in hypothyroidism associated with low cardiac output and elevated systemic vascular resistance. Numerous lines of research have proposed that triiodothyronine can behave acutely as a positive inotropic and vasodilator agent. The aim of this review is to present an update on the current literature about in what clinical situations the use of thyroid supplementation during the perioperative period of extracorporeal circulation in the adult and paediatric populations may impact outcome to any appreciable degree. The contribution of thyroid function in patients undergoing a ventricular assist device implantation is additionally reviewed and future study directions are proposed. This is a narrative review, where the search strategy consisted on retrieving the articles through an extensive literature search performed using electronic databases from January 1978 up to September 2019. All controlled trials randomly allocating to perioperative thyroid hormone administration in children and adults undergoing extracorporeal circulation for cardiac surgery were considered. Thyroid hormone supplementation may be recommended particularly in selected paediatric sub-populations. There is currently no firm evidence regarding the benefits of routine use of thyroid hormone administration in cardiac adult patients. Further studies are required to assess the beneficial effect of thyroid hormone on patients with end-stage heart failure supported by ventricular assist devices.

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ABSTRACT Objective To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, and a logistic regression model built on the same platform. Methods A gradient-boosted decision trees model and a logistic regression model were trained and tested with the Medical Information Mart for Intensive Care database. The 1-hour resolution physiological measurements of adult patients, collected during 5 hours in the intensive care unit, consisted of eight routine clinical parameters. The study addressed how the models learn to categorize patients to predict intensive care unit mortality or survival within 12 hours. The performance was evaluated with accuracy statistics and the area under the Receiver Operating Characteristic curve. Results The gradient-boosted trees yielded an area under the Receiver Operating Characteristic curve of 0.89, compared to 0.806 for the logistic regression. The accuracy was 0.814 for the gradient-boosted trees, compared to 0.782 for the logistic regression. The diagnostic odds ratio was 17.823 for the gradient-boosted trees, compared to 9.254 for the logistic regression. The Cohen's kappa, F-measure, Matthews correlation coefficient, and markedness were higher for the gradient-boosted trees. Conclusion The discriminatory power of the gradient-boosted trees was excellent. The gradient-boosted trees outperformed the logistic regression regarding intensive care unit mortality prediction. The high diagnostic odds ratio and markedness values for the gradient-boosted trees are important in the context of the studied unbalanced dataset.

RESUMO Objetivo Explorar uma abordagem de inteligência artificial baseada em árvores de decisão impulsionadas por gradiente para previsão de mortalidade por todas as causas em unidade de terapia intensiva, comparando seu desempenho com um sistema de regressão logística recente na literatura e um modelo de regressão logística construído na mesma plataforma. Métodos Foram desenvolvidos um modelo de árvores impulsionadas por gradiente e um modelo de regressão logística, treinados e testados com o banco de dados Medical Information Mart for Intensive Care. As medidas fisiológicas de pacientes adultos com resolução de 1 hora, coletadas durante 5 horas na unidade de terapia intensiva, consistiram em oito parâmetros clínicos de rotina. Estudou-se como os modelos aprendem a categorizar os pacientes para prever a mortalidade ou a sobrevida, em unidades de terapia intensiva, em 12 horas. O desempenho foi avaliado por meio de estatísticas de acurácia e pela área sob a curva Característica de Operação do Receptor. Resultados As árvores impulsionadas por gradiente produziram área sob a curva Característica de Operação do Receptor de 0,89, em comparação com 0,806 para a regressão logística. A acurácia foi de 0,814 para as árvores impulsionadas por gradiente, em comparação com 0,782 para a regressão logística. A razão de chances de diagnóstico foi de 17,823 para as árvores impulsionadas por gradiente, em comparação a 9,254 para a regressão logística. O kappa de Cohen, a medida F, o coeficiente de correlação de Matthews e a marcação foram maiores para as árvores impulsionadas por gradiente. Conclusão O poder discriminatório das árvores impulsionadas por gradiente foi excelente. As árvores impulsionadas por gradiente superaram a regressão logística em relação à previsão de mortalidade em unidade de terapia intensiva. A alta razão de chances de diagnóstico e os valores de marcação para as árvores impulsionadas por gradiente são importantes no contexto do conjunto de dados não balanceados estudado.

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OBJECTIVE: To propose a preliminary artificial intelligence model, based on artificial neural networks, for predicting the risk of nosocomial infection at intensive care units. METHODS: An artificial neural network is designed that employs supervised learning. The generation of the datasets was based on data derived from the Japanese Nosocomial Infection Surveillance system. It is studied how the Java Neural Network Simulator learns to categorize these patients to predict their risk of nosocomial infection. The simulations are performed with several backpropagation learning algorithms and with several groups of parameters, comparing their results through the sum of the squared errors and mean errors per pattern. RESULTS: The backpropagation with momentum algorithm showed better performance than the backpropagation algorithm. The performance improved with the xor. README file parameter values compared to the default parameters. There were no failures in the categorization of the patients into their risk of nosocomial infection. CONCLUSION: While this model is still based on a synthetic dataset, the excellent performance observed with a small number of patterns suggests that using higher numbers of variables and network layers to analyze larger volumes of data can create powerful artificial neural networks, potentially capable of precisely anticipating nosocomial infection at intensive care units. Using a real database during the simulations has the potential to realize the predictive ability of this model.

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ABSTRACT Objective: To propose a preliminary artificial intelligence model, based on artificial neural networks, for predicting the risk of nosocomial infection at intensive care units. Methods: An artificial neural network is designed that employs supervised learning. The generation of the datasets was based on data derived from the Japanese Nosocomial Infection Surveillance system. It is studied how the Java Neural Network Simulator learns to categorize these patients to predict their risk of nosocomial infection. The simulations are performed with several backpropagation learning algorithms and with several groups of parameters, comparing their results through the sum of the squared errors and mean errors per pattern. Results: The backpropagation with momentum algorithm showed better performance than the backpropagation algorithm. The performance improved with the xor. README file parameter values compared to the default parameters. There were no failures in the categorization of the patients into their risk of nosocomial infection. Conclusion: While this model is still based on a synthetic dataset, the excellent performance observed with a small number of patterns suggests that using higher numbers of variables and network layers to analyze larger volumes of data can create powerful artificial neural networks, potentially capable of precisely anticipating nosocomial infection at intensive care units. Using a real database during the simulations has the potential to realize the predictive ability of this model.

RESUMO Objetivo: Propor um modelo preliminar de inteligência artificial, baseado em redes neurais artificiais, para previsão do risco de infecção hospitalar em unidades de cuidado intensivo. Métodos: Foi usada uma rede neural artificial, que utiliza aprendizagem supervisionada. A geração dos conjuntos de dados baseia-se em dados derivados do sistema Japanese Nosocomial Infection Surveillance . Estudamos como o Java Neural Network Simulator aprende a categorizar esses pacientes para prever o respectivo risco de infecção hospitalar. As simulações são realizadas com diferentes algoritmos de aprendizagem por retropropagação e diversos grupos de parâmetros, comparando-se os resultados com base na soma dos erros quadráticos e erros médios por padrão. Resultados: O algoritmo de retropropagação com momentum mostrou desempenho superior ao do algoritmo de retropropagação. O desempenho foi melhor com os valores de parâmetros do arquivo xor. README em comparação aos parâmetros default . Não houve falhas na categorização de pacientes quanto ao respectivo risco de infecção hospitalar. Conclusão: Embora esse modelo se baseie em um conjunto de dados sintéticos, o excelente desempenho observado com um pequeno número de padrões sugere que o uso de números maiores de variáveis e camadas de rede para analisar volumes maiores de dados pode criar redes neurais artificiais poderosas, possivelmente capazes de prever com precisão o risco de infecção hospitalar em unidades de cuidado intensivo. O uso de um banco de dados real durante as simulações torna possível a realização da capacidade preditiva desse modelo.

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INTRODUCTION: Many criteria such as USMLE scores, applicant resumes, Dean's letters, recommendation letters, personal discussions, interview scores and medical school transcripts can be used to predict the success of a medical trainee in the USA. This information is either relatively objective, or subjective. It would be valuable if we had some objective measures that might predict a successful resident performance early in the process or on the other side to allow remediation or redirection. Actual performance of a resident or fellow is based upon his or her ability to execute sound judgments within the complex healthcare setting. The Hartman Value Profile (HVP) evaluates the structure and the dynamics of an individual value system. This study has the primary goal of determining whether specific indices on the HVP correlate with the management's evaluation of the residents established by the Department of Anesthesiology at Yale University. METHODS: The protocol developed uses univariate correlations between residents' HVP subscales and their performance scores, which will be determined with the Pearson correlation coefficient or Spearman rank coefficient as appropriate. Demographic and clinical variables will be reported descriptively. A two-sided alpha value of 0.05 will be used for identifying statistically significant findings. CONCLUSION: The potential benefits are that obtaining specific indices on the HVP would enable management to better engage and work with residents. Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity in predicting resident performance during training. Given the potential impact, it could be implemented as an adjuvant tool to the traditional evaluation process.

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Unpredictability arises in numerous real-world implementations especially in medical fields, being one of the principal subjects that expert systems have to deal with. Bayesian networks (BNs) allow constructing expert systems by utilizing likelihood as a measure of uncertainty. A BN is a statistical model that yields a graphical description of the probabilistic associations between a group of pertinent variables. Probabilistic reasoning involves computing the posterior probability of the unobserved variables given the accessible evidence, based on the application of the Bayes theorem. The principal benefit from BNs is that intricate relationships are decreased to simpler local dependencies. Graphical models utilization in health economics and medical decision-making has been supported lately. The non-existence of objective data frequently pressures the knowledge engineer to acquire the likelihoods from human experts' estimations, which turns this task laborious. For this logic, a software explanation facility can help the expert and the knowledge engineer through this procedure. In order to depict graphically and approximate a decision problem, a BN can be extended with decision and utility nodes associated with the realization of the random variables. Such model is named influence diagram (ID). The main aim of this article is to present a tutorial of BNs and the decision-theoretic model's IDs applied to medicine, including an explanation of these methods through several theoretical examples and illustrations of studies in the medical literature.

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In the past 16 years, a variety of factors might have impacted traffic accidents in Chile. In order to identify and quantify differential rates of change over time this study employed a novel analytic method to assess temporal trends in traffic morbi-mortality. Overall death and injury rates and associated to alcohol per 100,000 inhabitants were monitored between 2000 and 2015. Joinpoint regression was used to calculate annual percent changes (APCs) and average APCs. Permutation tests were used to determine joinpoints. P < 0.05 was considered statistically significant. The rate of traffic deaths related to alcohol declined from 2006 until 2015 at a rate of 9.53% per year. The rate of traffic injuries related to alcohol decreased at a rate of 4.32% per year since 2008 to 2015. The use of the most sensitive approach to trend analysis brings new ele-ments to form the epidemiological analyses in Chile and similar countries.

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BACKGROUND: In Chile, a new law introduced in March 2012 decreased the legal blood alcohol concentration (BAC) limit for driving while impaired from 1 to 0.8 g/l and the legal BAC limit for driving under the influence of alcohol from 0.5 to 0.3 g/l. The goal is to assess the impact of this new law on mortality and morbidity outcomes in Chile. METHODS: A review of national databases in Chile was conducted from January 2003 to December 2014. Segmented regression analysis of interrupted time series was used for analyzing the data. In a series of multivariable linear regression models, the change in intercept and slope in the monthly incidence rate of traffic deaths and injuries and association with alcohol per 100,000 inhabitants was estimated from pre-intervention to postintervention, while controlling for secular changes. In nested regression models, potential confounding seasonal effects were accounted for. All analyses were performed at a two-sided significance level of 0.05. RESULTS: Immediate level drops in all the monthly rates were observed after the law from the end of the prelaw period in the majority of models and in all the de-seasonalized models, although statistical significance was reached only in the model for injures related to alcohol. After the law, the estimated monthly rate dropped abruptly by -0.869 for injuries related to alcohol and by -0.859 adjusting for seasonality (P < 0.001). Regarding the postlaw long-term trends, it was evidenced a steeper decreasing trend after the law in the models for deaths related to alcohol, although these differences were not statistically significant. CONCLUSIONS: A strong evidence of a reduction in traffic injuries related to alcohol was found following the law in Chile. Although insufficient evidence was found of a statistically significant effect for the beneficial effects seen on deaths and overall injuries, potential clinically important effects cannot be ruled out.

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The pathophysiology of myocardial injury that results from cardiac ischemia and reperfusion (I/R) is incompletely understood. Experimental evidence from murine models indicates that innate immune mechanisms including complement activation via the classical and lectin pathways are crucial. Whether factor B (fB), a component of the alternative complement pathway required for amplification of complement cascade activation, participates in the pathophysiology of myocardial I/R injury has not been addressed. We induced regional myocardial I/R injury by transient coronary ligation in WT C57BL/6 mice, a manipulation that resulted in marked myocardial necrosis associated with activation of fB protein and myocardial deposition of C3 activation products. In contrast, in fB-/- mice, the same procedure resulted in significantly reduced myocardial necrosis (% ventricular tissue necrotic; fB-/- mice, 20 ± 4%; WT mice, 45 ± 3%; P < 0.05) and diminished deposition of C3 activation products in the myocardial tissue (fB-/- mice, 0 ± 0%; WT mice, 31 ± 6%; P<0.05). Reconstitution of fB-/- mice with WT serum followed by cardiac I/R restored the myocardial necrosis and activated C3 deposition in the myocardium. In translational human studies we measured levels of activated fB (Bb) in intracoronary blood samples obtained during cardio-pulmonary bypass surgery before and after aortic cross clamping (AXCL), during which global heart ischemia was induced. Intracoronary Bb increased immediately after AXCL, and the levels were directly correlated with peripheral blood levels of cardiac troponin I, an established biomarker of myocardial necrosis (Spearman coefficient = 0.465, P < 0.01). Taken together, our results support the conclusion that circulating fB is a crucial pathophysiological amplifier of I/R-induced, complement-dependent myocardial necrosis and identify fB as a potential therapeutic target for prevention of human myocardial I/R injury.

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BACKGROUND: In Chile, a new law introduced in March 2012 lowered the blood alcohol concentration (BAC) limit for impaired drivers from 0.1% to 0.08% and the BAC limit for driving under the influence of alcohol from 0.05% to 0.03%, but its effectiveness remains uncertain. The goal of this investigation was to evaluate the effects of this enactment on road traffic injuries and fatalities in Chile. STUDY DESIGN: A retrospective cohort study. METHODS: Data were analyzed using a descriptive and a Generalized Linear Models approach, type of Poisson regression, to analyze deaths and injuries in a series of additive Log-Linear Models accounting for the effects of law implementation, month influence, a linear time trend and population exposure. A review of national databases in Chile was conducted from 2003 to 2014 to evaluate the monthly rates of traffic fatalities and injuries associated to alcohol and in total. RESULTS: It was observed a decrease by 28.1 percent in the monthly rate of traffic fatalities related to alcohol as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 20.9 percent (P<0.001).There was a reduction in the monthly rate of traffic injuries related to alcohol by 10.5 percent as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 24.8 percent (P<0.001). CONCLUSIONS: Positive results followed from this new 'zero-tolerance' law implemented in 2012 in Chile. Chile experienced a significant reduction in alcohol-related traffic fatalities and injuries, being a successful public health intervention.

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BACKGROUND: Experimental models using short-duration noxious stimuli have led to the concept of preemptive analgesia. Ketorolac, a nonsteroidal anti-inflammatory drug, has been shown to have a postoperative narcotic-sparing effect when given preoperatively and alternatively to not have this effect. This study was undertaken to determine whether a single intravenous (IV) dose of ketorolac would result in decreased postoperative pain and narcotic requirements. METHODS: In a double-blind, randomized controlled trial, 48 women undergoing abdominal hysterectomy were studied. Patients in the ketorolac group received 30 mg of IV ketorolac 30 min before surgical incision, while the control group received normal saline. The postoperative analgesia was performed with a continuous infusion of tramadol at 12 mg/h with the possibility of a 10 mg bolus for every 10 min. Pain was assessed using the visual analog scale (VAS), tramadol consumption, and hemodynamic parameters at 0, 1, 2, 4, 8, 12, 16, and 24 h postoperatively. We quantified times to rescue analgesic (morphine), adverse effects, and patient satisfaction. RESULTS: There were neither significant differences in VAS scores between groups (P > 0.05) nor in the cumulative or incremental consumption of tramadol at any time point (P > 0.05). The time to first requested rescue analgesia was 66.25 ± 38.61 min in the ketorolac group and 65 ± 28.86 min in the control group (P = 0.765). There were no significant differences in systolic blood pressure (BP) between both groups, except at 2 h (P = 0.02) and 4 h (P = 0.045). There were no significant differences in diastolic BP between both groups, except at 4 h (P = 0.013). The respiratory rate showed no differences between groups, except at 8 h (P = 0.017), 16 h (P = 0.011), and 24 h (P = 0.049). These differences were not clinically significant. There were no statistically significant differences between groups in heart rate (P > 0.05). CONCLUSIONS: Preoperative ketorolac neither showed a preemptive analgesic effect nor was it effective as an adjuvant for decreasing opioid requirements or postoperative pain in patients receiving IV analgesia with tramadol after abdominal hysterectomy.

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Lumbar epidural anesthesia is commonly used for labor analgesia. The 'loss-of- resistance' to air technique (LORA) is generally employed for recognition of the epidural space. One of the rare complications of this technique is pneumocephalus (PC). Here we describe the case of a parturient who developed a frontal headache when locating the epidural space using LORA. On the second day after epidural injection, the patient exhibited occipital headaches with gradual worsening. Computed tomography scans of the brain indicated PC. Following symptomatic treatment, our patient was discharged on the 13th day. We concluded that the amount of air used to identify the epidural space in LORA should be minimized, LORA should not be used after dural puncture and the use of saline avoids PC complications.

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BACKGROUND: The analgesic properties of ketamine are associated with its non-competitive antagonism of the N-methyl-D-aspartate receptor; these receptors exhibit an excitatory function on pain transmission and this binding seems to inhibit or reverse the central sensitization of pain. In the literature, the value of this anesthetic for preemptive analgesia in the control of postoperative pain is uncertain. The objective of this study was to ascertain whether preoperative low-dose ketamine reduces postoperative pain and morphine consumption in adults undergoing colon surgery. METHODS: In a double-blind, randomized trial, 48 patients were studied. Patients in the ketamine group received 0.5 mg/kg intravenous ketamine before surgical incision, while the control group received normal saline. The postoperative analgesia was achieved with a continuous infusion of morphine at 0.015 mg∙kg-¹âˆ™h-¹ with the possibility of 0.02 mg/kg bolus every 10 min. Pain was assessed using the Visual Analog Scale (VAS), morphine consumption, and hemodynamic parameters at 0, 1, 2, 4, 8, 12, 16, and 24 hours postoperatively. We quantified times to rescue analgesic (Paracetamol), adverse effects and patient satisfaction. RESULTS: No significant differences were observed in VAS scores between groups (P>0.05), except at 4 hours postoperatively (P=0.040). There were no differences in cumulative consumption of morphine at any time point (P>0.05). We found no significant differences in incremental postoperative doses of morphine consumption in bolus, except at 12 h (P =0.013) and 24 h (P =0.002). The time to first required rescue analgesia was 70 ± 15.491 min in the ketamine group and 44 ± 19.494 min in the control (P>0.05). There were no differences in hemodynamic parameters or patient satisfaction (P>0.05). CONCLUSIONS: Preoperative low-dose-ketamine did not show a preemptive analgesic effect or efficacy as an adjuvant for decreasing opioid requirements for postoperative pain in patients receiving intravenous analgesia with morphine after colon surgery.