Validation of a predictive model for operative trauma experience to facilitate selection of trauma sustainment military–civilian partnerships

Abstract

BackgroundTrauma readiness is a Department of Defense requirement for military healthcare providers. Surgeons must maintain readiness to optimize surgical care on the battlefield and minimize preventable death. The objective of this study was to validate a predictive model for trauma operative exposure by applying the model prospectively.MethodsThe predictive model for operative trauma exposure was prospectively applied to predict the number of emergent operative cases that would be experienced over predetermined time periods at four separate trauma sustainment military–civilian partnerships (TS-MCP). Notional courses were designed to be 2 or 4 weeks long and consisting of 5 and 12 overnight call periods, respectively. A total of 51 separate 2-week courses and 49 4-week courses were evaluated using the model. The outcome measure was the number of urgent (occurring within a day of arrival) operative trauma cases.ResultsTrauma/general surgery case volumes during call periods of notional courses were within the predicted range at least 98% of the time. Orthopedic volumes were more variable with a range of 82%–98% meeting expectation depending on the course length and institution.ConclusionThe previously defined model accurately predicted the number of urgent trauma/general surgery cases course participants would likely experience when applied prospectively to TS-MCP; however, the model was less accurate in predicting acute orthopedic trauma exposure. While it remains unknown how many cases need to be performed meet a trauma sustainment requirement, having a model with a predictive capability for case volume will facilitate metric development. This model may be useful when planning for future TS-MCP.Level of evidenceEconomic and Value Based Evaluations Level II

Maintaining vascular trauma proficiency for military non-vascular surgeons

“…Predictions to experience a relevant, urgent, operative vascular case were based on previous prediction models published for general trauma cases. 8 Urgent was defined as happening within a calendar day, consistent with previous publications. An identical methodology was used to calculate the number of 24-hour shifts or the number of cases expected after a certain number of shifts in this study.…”

“…If familiarity is all that is required, it is likely that a visiting model may be appropriate at an institution such as Saint Louis University Hospital or more appropriately at a higher volume center. 8 The idea to have a visiting surgeon exposed to trauma for a short time would seem on the surface to be a good idea. This would allow for a distinct time period to be set aside, enabling a surgeon to plan his life and clinical practice.…”

“…Accuracy tests of the methodology were done for trauma/general surgery and orthopedics and found that accuracy did depend on service, with orthopedics being less accurate. 8 The accuracy of these predictions may not achieve 95% certainty, but the authors suspect the time requirements to achieve any guaranteed level of physician exposure would be prohibitive. Finally, this is a single-institution study.…”

“…It is known that this institution has fewer trauma cases in general than other institutions, and if detailed analysis is done at other institutions more viable locations could be identified. 8 This study does not address any method of gauging at what defines 'competence' in a specific skill set or what level surgical skills are currently at within the military general surgeon population. This is currently being addressed at high levels through the Knowledge Skills and Abilities project and others, but it has not been completed.…”

“…Operations may have seasonal patterns according to previously published research. 8 To investigate whether there were significant differences in the volume of vascular operations among seasons, negative binomial regression was used to analyze the observed 2-year data with year, season, and season by year interaction in the model (figure 1). No statistically significant variables were found, and the remaining analysis was done without regarding seasonality.…”