TNM Staging of Neoplasms of the Endocrine Pancreas: Results From a Large International Cohort Study

Background Both the European Neuroendocrine Tumor Society (ENETS) and the International Union for Cancer Control/American Joint Cancer Committee/World Health Organization (UICC/AJCC/WHO) have proposed TNM staging systems for pancreatic neuroendocrine neoplasms. This study aims to identify the most accurate and useful TNM system for pancreatic neuroendocrine neoplasms. Methods The study included 1072 patients who had undergone previous surgery for their cancer and for which at least 2 years of follow-up from 1990 to 2007 was available. Data on 28 variables were collected, and the performance of the two TNM staging systems was compared by Cox regression analysis and multivariable analyses. All statistical tests were two-sided. Results Differences in distribution of sex and age were observed for the ENETS TNM staging system. At Cox regression analysis, only the ENETS TNM staging system perfectly allocated patients into four statistically significantly different and equally populated risk groups (with stage I as the reference; stage II hazard ratio [HR] of death = 16.23, 95% confidence interval [CI] = 2.14 to 123, P = .007; stage III HR of death = 51.81, 95% CI = 7.11 to 377, P < .001; and stage IV HR of death = 160, 95% CI = 22.30 to 1143, P < .001). However, the UICC/AJCC/WHO 2010 TNM staging system compressed the disease into three differently populated classes, with most patients in stage I, and with the patients being equally distributed into stages II-III (statistically similar) and IV (with stage I as the reference; stage II HR of death = 9.57, 95% CI = 4.62 to 19.88, P < .001; stage III HR of death = 9.32, 95% CI = 3.69 to 23.53, P = .94; and stage IV HR of death = 30.84, 95% CI = 15.62 to 60.87, P < .001). Multivariable modeling indicated curative surgery, TNM staging, and grading were effective predictors of death, and grading was the second most effective independent predictor of survival in the absence of staging information. Though both TNM staging systems were independent predictors of survival, the UICC/AJCC/WHO 2010 TNM stages showed very large 95% confidence intervals for each stage, indicating an inaccurate predictive ability. Conclusion Our data suggest the ENETS TNM staging system is superior to the UICC/AJCC/WHO 2010 TNM staging system and supports its use in clinical practic

Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia

Individual differences in the encoding of contextual details following acute stress:An explorative study

Information processing under stressful circumstances depends on many experimental conditions, like the information valence or the point in time at which brain function is probed. This also holds true for memorizing contextual details (or 'memory contextualization'). Moreover, large interindividual differences appear to exist in (context-dependent) memory formation after stress, but it is mostly unknown which individual characteristics are essential. Various characteristics were explored from a theory-driven and data-driven perspective, in 120 healthy men. In the theory-driven model, we postulated that life adversity and trait anxiety shape the stress response, which impacts memory contextualization following acute stress. This was indeed largely supported by linear regression analyses, showing significant interactions depending on valence and time point after stress. Thus, during the acute phase of the stress response, reduced neutral memory contextualization was related to salivary cortisol level; moreover, certain individual characteristics correlated with memory contextualization of negatively valenced material: (a) life adversity, (b) alpha-amylase reactivity in those with low life adversity and (c) cortisol reactivity in those with low trait anxiety. Better neutral memory contextualization during the recovery phase of the stress response was associated with (a) cortisol in individuals with low life adversity and (b) alpha-amylase in individuals with high life adversity. The data-driven Random Forest-based variable selection also pointed to (early) life adversity-during the acute phase-and (moderate) alpha-amylase reactivity-during the recovery phase-as individual characteristics related to better memory contextualization. Newly identified characteristics sparked novel hypotheses about non-anxious personality traits, age, mood and states during retrieval of context-related information

Predicting Hospital Length of Stay in Intensive Care Unit

In this thesis, we investigate the performance of a series of classification methods for the Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting LOS for an inpatient in an hospital is a challenging task but is essential for the operational success of a hospital. Since hospitals are faced with severely limited resources including beds to hold admitted patients, prediction of LoS will assist the hospital staff for better planning and management of hospital resources. The goal of this project is to create a machine learning model that predicts the length-of stay for each patient at the time of admission. MIMIC-III database has been used for this project due to detailed information it contains about ICU stays. MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising de-identified health data associated with ~40,000 critical care patients at Beth Israel Deaconess Medical Centre. It includes demographics, vital signs, laboratory tests, medications, and more. Different machine learning techniques/classifiers have been investigated in this thesis. We experimented with regression models as well as classification models with different classes of varying granularity as target for LoS prediction. It turned out that granular classes (in small unit of days) work better than regression models trying to predict exact duration in days and hours. The overall performance of our classifiers was ranging from fair to very good and has been discussed in the results. Secondly, we also experimented with building separate LoS prediction models built for patients with different disease conditions and compared it to the joint model built for all patients

Predicting Hospital Length of Stay in Intensive Care Unit

In this thesis, we investigate the performance of a series of classification methods for the Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting LOS for an inpatient in an hospital is a challenging task but is essential for the operational success of a hospital. Since hospitals are faced with severely limited resources including beds to hold admitted patients, prediction of LoS will assist the hospital staff for better planning and management of hospital resources. The goal of this project is to create a machine learning model that predicts the length-of stay for each patient at the time of admission. MIMIC-III database has been used for this project due to detailed information it contains about ICU stays. MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising de-identified health data associated with ~40,000 critical care patients at Beth Israel Deaconess Medical Centre. It includes demographics, vital signs, laboratory tests, medications, and more. Different machine learning techniques/classifiers have been investigated in this thesis. We experimented with regression models as well as classification models with different classes of varying granularity as target for LoS prediction. It turned out that granular classes (in small unit of days) work better than regression models trying to predict exact duration in days and hours. The overall performance of our classifiers was ranging from fair to very good and has been discussed in the results. Secondly, we also experimented with building separate LoS prediction models built for patients with different disease conditions and compared it to the joint model built for all patients