BackgroundNon-traumatic coma (NTC) is a serious condition requiring swift medical or surgical decision making upon arrival at the emergency department. Knowledge of the most frequent etiologies of NTC and associated mortality might improve the management of these patients. Here, we present the results of a systematic literature search on the etiologies and prognosis of NTC.MethodsTwo reviewers independently performed a systematic literature search in the Pubmed, Embase and Cochrane databases with subsequent reference and citation checking. Inclusion criteria were retrospective or prospective observational studies on NTC, which reported on etiologies and prognostic information of patients admitted to the emergency department or intensive care unit.ResultsEventually, 14 studies with enough data on NTC, were selected for this systematic literature review. The most common causes of NTC were stroke (6-54%), post-anoxic coma (3-42%), poisoning (<1-39%) and metabolic causes (1-29%). NTC was also often caused by infections, especially in African studies affecting 10-51% of patients. The NTC mortality rate ranged from 25 to 87% and the mortality rate continued to increase long after the event had occurred. Also, 5-25% of patients remained moderately-severely disabled or in permanent vegetative state. The mortality was highest for stroke (60-95%) and post-anoxic coma (54-89%) and lowest for poisoning (0-39%) and epilepsy (0-10%).ConclusionNTC represents a challenge to the emergency and the critical care physicians with an important mortality and moderate-severe disability rate. Even though, included studies were very heterogeneous, the most common causes of NTC are stroke, post anoxic, poisoning and various metabolic etiologies. The best outcome is achieved for patients with poisoning and epilepsy, while the worst outcome was seen in patients with stroke and post-anoxic coma. Adequate knowledge of the most common causes of NTC and prioritizing the causes by mortality ensures a swift and adequate work-up in diagnosis of NTC and may improve outcome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12871-015-0041-9) contains supplementary material, which is available to authorized users.
Background. Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. Methods. Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study. Results. In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches’ performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance. Conclusions. Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).
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