255. A clinical calculator to predict survival in elderly spine trauma patients

Carlile, Catherine; Schultz, Jacob; Nian, Hui; Pennings, Jacquelyn S.; Abtahi, Amir M.; Stephens, Byron F.

doi:10.1016/j.spinee.2021.05.368

Cited by 1 publication

(8 citation statements)

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“…As a secondary analysis, a multivariable Cox proportional hazard model was fitted to predict the patient survival up to nine years after spine trauma presentation. 23 Time to death was the response variable and was defined as the time interval between the date of traumatic injury and the date of death from any cause; patients not dead were right censored at the date of data collection (December 1, 2019). Survival curves were estimated using the Kaplan–Meier method.…”

Section: Methodsmentioning

confidence: 99%

“…Spinal cord injury was classified via the American Spinal Injury Association Impairment Scale (ASIA). 23,27 For all operative patients, operative specifics including site, number of levels instrumented, estimated blood loss, and approach were obtained along with trends in lactate at the time of admission and the time of surgery. 23 After data collection, several variables were selected a priori as explanatory variables.…”

Section: Study Design and Patient Selectionmentioning

confidence: 99%

“…23,27 For all operative patients, operative specifics including site, number of levels instrumented, estimated blood loss, and approach were obtained along with trends in lactate at the time of admission and the time of surgery. 23 After data collection, several variables were selected a priori as explanatory variables. These variables included: presenting injury information, demographics, frailty scores, comorbid conditions, common laboratory values upon admission, imaging indicators of osteopenia and sarcopenia, operative intervention, and presence of spinal cord injury.…”

Section: Study Design and Patient Selectionmentioning

confidence: 99%

“…These variables included: presenting injury information, demographics, frailty scores, comorbid conditions, common laboratory values upon admission, imaging indicators of osteopenia and sarcopenia, operative intervention, and presence of spinal cord injury. 23 The data from the retrospective chart review were recorded in a password-protected REDCap database only accessible by the research team. These variables included ISS, frailty scores, age, comorbidities, and ASIA scores, which have been shown to be associated with mortality at various time points in both general trauma and specifically spine trauma in the elderly.…”

Section: Study Design and Patient Selectionmentioning

confidence: 99%

“…The model has an optimism-corrected C-index of 0.713. 23 Figure 7 shows the calibration plot of the model at 2 years.…”

Section: Predictive Model For Mortalitymentioning

confidence: 99%

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Predicting Mortality in Elderly Spine Trauma Patients

Carlile

Rees

Schultz

et al. 2022

Spine

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Study Design. Retrospective analysis on prospectively collected data. Objective. The aim of this study was to construct a clinical prediction model for 90-day mortality in elderly patients with traumatic spine injuries. Summary of Background Data. Spine trauma in the elderly population is increasing. Comparing elderly spine trauma patients to younger patients with similar injuries proves challenging due to the extensive comorbidities and frailty found in the elderly. There is a paucity of evidence to predict survival of elderly patients following traumatic spinal injuries. Methods. All patients 65+ with spine trauma presenting to a level I trauma center from 2010 to 2019 were reviewed from a prospectively maintained trauma registry. Retrospective chart review was performed to record injury, frailty scores, comorbidities, presence of spinal cord injury, imaging evidence of sarcopenia and osteopenia, mortality, and complications. We preselected 13 variables for our multivariable logistic regression model: hypotension on admission, gender, marital status, age, max Abbreviated Injury Scale, Modified Frailty Index, surgical treatment, hematocrit, white blood count, spinal cord injury, closed head injury, injury level and presence of high energy mechanism. The performance of the prediction model was evaluated using a concordance index and calibration plot. The model was internally validated via bootstrap approach. Results. Over the 9-year period, 1746 patients met inclusion criteria; 359 (20.6%) patients died within 90 days after presenting with spine trauma. The most important predictors for 90-day mortality were age, hypotension, closed head injury, max Abbreviated Injury Scale and hematocrit. There was an optimism-corrected C-index of 0.77. A calculator was created to predict a personalized mortality risk. Conclusion. The incidence of spine trauma in elderly patients continues to increase. Previous publications described preexisting conditions that imply increased mortality, but ours is the first to develop a predictive calculator. Prospective research is planned to externally validate this model to better determine its predictive value and utility in the clinical setting.

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Section: Methodsmentioning

confidence: 99%