0218 Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents. (27th May 2020)

• Record Type: Journal Article
• Authors: Schwartz, L P; Devine, J K; Hursh, S R; Mosher, E; Schumacher, S; Boyle, L; Davis, J E; Smith, M; Fitzgibbons, S
• Is Part Of: Sleep. Volume 43(2020)Supplement 1
• Page Start: A84
• Page End: A85
• Publication Date: 2020-05-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa056.216
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Fatigue and its effects on performance have long been a concern in medicine. Evidence exists that current duty-hour restrictions for resident trainees have a limited impact on physician wellbeing and patient safety, prompting renewed efforts to address this threat. In this study, sleep patterns of general-surgery residents were used to optimize a biomathematical model of performance for use as a tool for fatigue risk management with residents. Methods: General surgery residents based at a multi-hospital, general surgery residency program were approached for participation in this study. Enrolled residents wore actigraph devices for 8 weeks and completed subjective sleep assessments. Sleep data and shift schedules were then input into the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model to assess predicted cognitive performance. Performance was compared to an "effectiveness" level of 77 (equivalent to a blood-alcohol content of 0.05g/dL). Eight hours of sleep debt was considered "below reservoir criteria". Results: Sleep actigraphy data was collected from 22 general surgery residents. Modeling results showed that as shift lengths increased, effectiveness scores generally decreased, and the time spent below criterion (77) increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts included time spent below the reservoir criterion. Adjustments to the sleep prediction were made based on actual sleep, and performance predictions from actual sleep and the adjusted model were significantly correlated (p<.0001). Conclusion: Despite adherence to national standards limiting work hours, current surgical resident sleep patterns and shift schedules create concerning levels of fatigue. This study illustrates how biomathematical fatigue models can predict resident physician sleep patterns and performance. Modeling represents a novel and important tool for medical educators seeking to create shift schedules that maintain physician preparedness and minimize fatigue risk. Support: N/A