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Background: A correlation between unsuccessful noninvasive ventilation (NIV) and poor outcome has been suggested in de-novo Acute Respiratory Failure (ARF) patients. Consequently, it is of paramount importance to identify accurate predictors of NIV outcome. The aim of our preliminary study is to evaluate the Diaphragmatic Thickening Fraction (DTF) and the respiratory rate/DTF ratio as predictors of NIV outcome in de-novo ARF patients., Methods: Over 36 months, we studied patients admitted to the emergency department with a diagnosis of de-novo ARF and requiring NIV treatment. DTF and respiratory rate/DTF ratio were measured by 2 trained operators at baseline, at 1, 4, 12, 24, 48, 72 and 96 h of NIV treatment and/or until NIV discontinuation or intubation. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of DTF and respiratory rate/DTF ratio to distinguish between patients who were successfully weaned and those who failed., Results: Eighteen patients were included. We found overall good repeatability of DTF assessment, with Intra-class Correlation Coefficient (ICC) of 0.82 (95% confidence interval 0.72-0.88). The cut-off values of DTF for prediction of NIV failure were < 36.3% and < 37.1% for the operator 1 and 2 (p < 0.0001), respectively. The cut-off value of respiratory rate/DTF ratio for prediction of NIV failure was > 0.6 for both operators (p < 0.0001)., Conclusion: DTF and respiratory rate/DTF ratio may both represent valid, feasible and noninvasive tools to predict NIV outcome in patients with de-novo ARF. Trial registration ClinicalTrials.gov Identifier: NCT02976233, registered 26 November 2016.

Italian Poison Centers answer to approximately 100,000 calls per year. Potentially, this activity is a huge source of data for toxicovigilance and for syndromic surveillance. During the last decade, surveillance systems for early detection of outbreaks have drawn the attention of public health institutions due to the threat of terrorism and high-profile disease outbreaks. Poisoning surveillance needs the ongoing, systematic collection, analysis, interpretation, and dissemination of harmonised data about poisonings from all Poison Centers for use in public health action to reduce morbidity and mortality and to improve health. The entity-relationship model for a Poison Center relational database is extremely complex and not studied in detail. For this reason, not harmonised data collection happens among Italian Poison Centers. Entities are recognizable concepts, either concrete or abstract, such as patients and poisons, or events which have relevance to the database, such as calls. Connectivity and cardinality of relationships are complex as well. A one-to-many relationship exist between calls and patients: for one instance of entity calls, there are zero, one, or many instances of entity patients. At the same time, a one-to-many relationship exist between patients and poisons: for one instance of entity patients, there are zero, one, or many instances of entity poisons. This paper shows a relational model for a poison center database which allows the harmonised data collection of poison centers calls.