RESUMEN
INTRODUCTION: Incidental findings are common in presumed healthy volunteers but are infrequently studied in patients in a clinical context. OBJECTIVE: To determine the prevalence, nature, and management implications of incidental findings on head MRI in patients presenting with cognitive symptoms, and to quantify and describe unexpected MRI abnormalities that are of uncertain relevance to the patient's cognitive symptoms. METHODS: A single-centre retrospective review of patients attending a regional early-onset cognitive disorders clinic between March 2012 and October 2018. Medical records of consecutive patients who underwent head MRI were reviewed. Unexpected MRI findings were classified according to their severity and likelihood of being incidental. Markers of small vessel disease and cerebral atrophy were excluded. RESULTS: Records of 694 patients were reviewed (median age 60 years, 49.9% female), of whom 514 (74.1%) underwent head MRI. 54% of the patients received a diagnosis of a neurodegenerative disorder. Overall 111 incidental findings were identified in 100 patients of whom 18 patients (3.5%, 95% CI 2.2-5.6%) had 18 incidental findings classified as requiring additional medical evaluation. 82 patients (16%, 95% CI 13.0-19.5%) had 93 incidental findings without clearly defined diagnostic consequences. 17 patients (3.3%) underwent further investigations, 14 patients (2.7%) were referred to another specialist clinic and 3 patients (0.6%) were treated surgically. Two patients had MRI findings of uncertain relevance to their cognitive symptoms, necessitating prolonged clinic follow-up. CONCLUSION: Incidental findings are common in patients with cognitive impairment from this large clinic-based series; however, few required additional medical evaluation. These data could help inform discussions between clinicians and people with cognitive symptoms regarding the likelihood and potential implications of incidental imaging findings.
Asunto(s)
Disfunción Cognitiva/diagnóstico por imagen , Cabeza/diagnóstico por imagen , Hallazgos Incidentales , Anciano , Atrofia , Enfermedades de los Pequeños Vasos Cerebrales/diagnóstico por imagen , Enfermedades de los Pequeños Vasos Cerebrales/psicología , Disfunción Cognitiva/epidemiología , Disfunción Cognitiva/psicología , Femenino , Voluntarios Sanos , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Prevalencia , Estudios Retrospectivos , Tomografía Computarizada por Rayos XRESUMEN
OBJECTIVE: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. BACKGROUND: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. METHOD: A multilevel workload model was developed in Study I with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters.The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. RESULTS: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. CONCLUSION: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. APPLICATION: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs.Tactical uses include the dynamic reallocation of resources to meet changes in demand.