Dataset Information

Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm.

ABSTRACT: Objective:We investigated the reliability and accuracy of a bedside diagnostic algorithm for patients presenting with vertigo/unsteadiness to the emergency department. Methods:We enrolled consecutive adult patients presenting with vertigo/unsteadiness at a tertiary hospital. STANDING, the acronym for the four-step algorithm we have previously described, based on nystagmus observation and well-known diagnostic maneuvers includes (1) the discrimination between SponTAneous and positional nystagmus, (2) the evaluation of the Nystagmus Direction, (3) the head Impulse test, and (4) the evaluation of equilibrium (staNdinG). Reliability of each step was analyzed by Fleiss' K calculation. The reference standard (central vertigo) was a composite of brain disease including stroke, demyelinating disease, neoplasm, or other brain disease diagnosed by initial imaging or during 3-month follow-up. Results:Three hundred and fifty-two patients were included. The incidence of central vertigo was 11.4% [95% confidence interval (CI) 8.2-15.2%]. The leading cause was ischemic stroke (70%). The STANDING showed a good reliability (overall Fleiss K 0.83), the second step showing the highest (0.95), and the third step the lowest (0.74) agreement. The overall accuracy of the algorithm was 88% (95% CI 85-88%), showing high sensitivity (95%, 95% CI 83-99%) and specificity (87%, 95% CI 85-87%), very high-negative predictive value (99%, 95% CI 97-100%), and a positive predictive value of 48% (95% CI 41-50%) for central vertigo. Conclusion:Using the STANDING algorithm, non-sub-specialists achieved good reliability and high accuracy in excluding stroke and other threatening causes of vertigo/unsteadiness.

SUBMITTER: Vanni S

PROVIDER: S-EPMC5682038 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm.

Vanni Simone S Pecci Rudi R Edlow Jonathan A JA Nazerian Peiman P Santimone Rossana R Pepe Giuseppe G Moretti Marco M Pavellini Andrea A Caviglioli Cosimo C Casula Claudia C Bigiarini Sofia S Vannucchi Paolo P Grifoni Stefano S

Frontiers in neurology 20171107

<h4>Objective</h4>We investigated the reliability and accuracy of a bedside diagnostic algorithm for patients presenting with vertigo/unsteadiness to the emergency department.<h4>Methods</h4>We enrolled consecutive adult patients presenting with vertigo/unsteadiness at a tertiary hospital. STANDING, the acronym for the four-step algorithm we have previously described, based on nystagmus observation and well-known diagnostic maneuvers includes (1) the discrimination between <b>S</b>pon<b>TA</b>ne ...[more]

PMID: 29163350

Similar Datasets

Project description:Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.

Project description:Lung ultrasound (LUS) can help clinicians make a timely diagnosis of community-acquired pneumonia (CAP).ObjectivesTo assess if LUS can improve diagnosis and antibiotic initiation in emergency department (ED) patients with suspected CAP.DesignA prospective observational study.SettingsFour EDs.ParticipantsThe study included 150 patients older than 18 years with a clinical suspicion of CAP, of which 2 were subsequently excluded (incorrect identification), leaving 148 patients (70 women and 78 men, average age 72±18 years). Exclusion criteria included a life-threatening condition with do-not-resuscitate-order or patient requiring immediate intensive care.InterventionsAfter routine diagnostic procedure (clinical, radiological and laboratory tests), the attending emergency physician established a clinical CAP probability according to a four-level Likert scale (definite, probable, possible and excluded). An LUS was then performed, and another CAP probability was established based on the ultrasound result. An adjudication committee composed of three independent experts established the final CAP probability at hospital discharge.Primary and secondary outcome measuresPrimary objective was to assess concordance rate of CAP diagnostic probabilities between routine diagnosis procedure or LUS and the final probability of the adjudication committee. Secondary objectives were to assess changes in CAP probability induced by LUS, and changes in antibiotic treatment initiation.ResultsOverall, 27% (95% CI 20 to 35) of the routine procedure CAP classifications and 77% (95% CI 71 to 84) of the LUS CAP classifications were concordant with the adjudication committee classifications. Cohen's kappa coefficients between routine diagnosis procedure and LUS, according to adjudication committee, were 0.07 (95% CI 0.04 to 0.11) and 0.61 (95% CI 0.55 to 0.66), respectively. The modified probabilities for the diagnosis of CAP after LUS resulted in changes in antibiotic prescriptions in 32% (95% CI 25 to 40) of the cases.ConclusionIn our study, LUS was a powerful tool to improve CAP diagnosis in the ED, reducing diagnostic uncertainty from 73% to 14%.Trial registration numberNCT03411824.

Dataset Information

Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm.

Publications

Differential Diagnosis of Vertigo in the Emergency Department: A Prospective Validation Study of the STANDING Algorithm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets