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Objectives: Patients presenting to the emergency department (ED) with nonspecific complaints are difficult to accurately triage, risk stratify, and diagnose. This can delay appropriate treatment. The extent to which key medical outcomes are at all predictable in these patients, and which (if any) predictors are useful, has previously been unclear. To investigate these questions, we tested an array of statistical and machine learning models in a large group of patients and estimated the predictability of mortality (which occurred in 6.6% of our sample of patients), acute morbidity (58%), and presence of acute infectious disease (28.2%).Methods: To investigate whether the best available tools can predict the three key outcomes, we fed data from a sample of 1,278 ED patients with nonspecific complaints into 17 state-of-the-art statistical and machine learning models. The patient sample stems from a diagnostic multicenter study with prospective 30-day follow-up conducted in Switzerland. Predictability of the three key medical outcomes was quantified by computing the area under the receiver operating characteristic curve (AUC) for each model. Results:The models performed at different levels but, on average, the predictability of the target outcomes ranged between 0.71 and 0.82. The better models clearly outperformed physicians' intuitive judgments of how ill patients looked (AUC = 0.67 for mortality, 0.65 for morbidity, and 0.60 for infectious disease).Conclusions: Modeling techniques can be used to derive formalized models that, on average, predict the outcomes of mortality, acute morbidity, and acute infectious disease in patients with nonspecific complaints with a level of accuracy far beyond chance. The models also predicted these outcomes more accurately than did physicians' intuitive judgments of how ill the patients look; however, the latter was among the small set of best predictors for mortality and acute morbidity. These results lay the groundwork for further refining triage and risk stratification tools for patients with nonspecific complaints. More research, informed by whether the goal of a model is high sensitivity or high specificity, is needed to develop readily applicable clinical decision support tools (e.g., decision trees) that could be supported by electronic health records.ACADEMIC EMERGENCY MEDICINE 2015;22:1155-1163© 2015 by the Society for Academic Emergency Medicine E mergency physicians (EPs) frequently encounter patients with nonspecific complaints. These patients tend to report general feelings of weakness, discomfort, fatigue, or dizziness, but not more specific complaints. 1 They are often undertriaged (i.e., their initial risk assessments are too low), and the severity of the illness causing their nonspecific complaints is often misjudged. 2 These systematic misjudgments can result in delays in definitive treatment of the underlying cause of their nonspecific chief complaints.Improved triage, rapid diagnosis, timely treatment, and appropriate disposition decisions to the proper...

Background. Dizziness is a frequent presentation in patients presenting to emergency departments (EDs), often triggering extensive work-up, including neuroimaging. Therefore, gathering knowledge on final diagnoses and outcomes is important. We aimed to describe the incidence of dizziness as primary or secondary complaint, to list final diagnoses, and to determine the use and yield of neuroimaging and outcomes in these patients. Methods. Secondary analysis of two observational cohort studies, including all patients presenting to the ED of the University Hospital of Basel from 30th January 2017–19th February 2017 and from 18th March 2019–20th May 2019. Baseline demographics, Emergency Severity Index (ESI), hospitalization, admission to Intensive Care Units (ICUs), and mortality were extracted from the electronic health record database. At presentation, patients underwent a structured interview about their symptoms, defining their primary and secondary complaints. Neuroimaging results were obtained from the picture archiving and communication system (PACS). Patients were categorized into three non-overlapping groups: dizziness as primary complaint, dizziness as secondary complaint, and absence of dizziness. Results. Of 10076 presentations, 232 (2.3%) indicated dizziness as their primary and 984 (9.8%) as their secondary complaint. In dizziness as primary complaint, the three (out of 73 main conditions defined) main diagnoses were nonspecific dizziness (47, 20.3%), dysfunction of the peripheral vestibular system (37, 15.9%), as well as somatization, depression, and anxiety (20, 8.6%). 104 of 232 patients (44.8%) underwent neuroimaging, with relevant findings in 5 (4.8%). In dizziness as primary complaint 30-day mortality was 0%. Conclusion. Work-up for dizziness in emergency presentations has to consider a broad differential diagnosis, but due to the low yield, it should include neuroimaging only in few and selected cases, particularly with additional neurological abnormalities. Presentation with primary dizziness carries a generally favorable prognosis lacking short-term mortality. .