Assessment of patient mobility improves the risk stratification of triage with the Emergency Severity Index: a prospective cohort study.

BACKGROUND AND IMPORTANCE: Formal triage may assign a low acuity to patients at high risk of deterioration and mortality. A patient's mobility can be easily assessed at triage. OBJECTIVE: To investigate if a simple assessment of mobility at triage can improve the Emergency Severity Index's (ESI) prediction of adverse outcomes. DESIGN, SETTING AND PARTICIPANTS: Prospective observational study of all patients attending the emergency department (ED) of a single academic hospital in Switzerland over a period of 3 weeks. OUTCOME MEASURES AND ANALYSIS: Triage clinicians classified participants as having normal or impaired mobility at triage. Impaired mobility was defined as the lack of a stable independent gait, regardless of its cause or duration (e.g. any patient who needed help to walk). The primary outcome was 30-day mortality. We performed a survival analysis stratified by mobility and ESI level. We compared the performance of regression models including the ESI alone or in combination with mobility as predictors of mortality using the Bayesian information criterion (BIC). MAIN RESULTS: 2523 patients were included in the study and 880 (34.9%) had impaired mobility. Patients with impaired mobility had a lower median 30-day survival in ESI levels 1-3. Survival of patients with normal mobility was similar regardless of their ESI level. CONCLUSION: The assessment of mobility at triage improves the ESI algorithm's risk stratification.

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.