Value of aggressive surgical and intensive care unit in elderly patients with traumatic spinal cord injury

Lau, Darryl; Ore, Cecilia L Dalle; Tarapore, Phiroz E.; Huang, Michael C.; Manley, Geoffrey T.; Singh, Vineeta; Mummaneni, Praveen V.; Beattie, Michael S.; Bresnahan, Jacqueline C.; Ferguson, Adam R.; Talbott, Jason F.; Whetstone, William D.; Dhall, Sanjay S.

doi:10.3171/2018.12.focus18555

Cited by 23 publications

(27 citation statements)

References 51 publications

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“…This is consistent with the findings by Martin and coworkers, 20 who described an ''inflection point'' at the age of 75 for in-hospital mortality following cervical SCI. Our overall in-hospital mortality rate of 16% is slightly lower than the 12-40% reported by others, [15][16][17][18][19][20] but our data may underestimate the rate, as some patients who died shortly after admission may not have been enrolled in the registry.…”

Section: Relation To Literaturecontrasting

confidence: 80%

“…In-hospital mortality rates in elderly patients with tSCI are reported as being 12–40%, 15–20 considerably higher than the estimated mortality of <5% in younger patient populations. 16 Previously identified risk factors for mortality in the elderly include older age, higher cervical level of injury, neurological severity, and increasing number of comorbidities; 17 , 18 , 20 however, to our knowledge, no one has developed a clinical decision-making aid using a combination of these factors for in-hospital mortality for this elderly population.…”

Section: Introductionmentioning

confidence: 99%

“… 27 Few studies focus on in-hospital mortality of the elderly population, with fewer comparing this outcome following surgical and non-surgical interventions. 17 , 19 Further, most studies compare the elderly with a younger population 13 , 15 , 16 , 20 or involve only cervical SCI or spine fractures. 15 , 17 , 19 , 20 , 28–30 Further understanding on the clinical course of elderly patients with tSCI and the specific risk factors for those who die despite aggressive surgical management will inform early communication and shared decision making between clinicians and patients and family.…”

Section: Introductionmentioning

confidence: 99%

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In-Hospital Mortality for the Elderly with Acute Traumatic Spinal Cord Injury

Inglis

Banaszek

Rivers

et al. 2020

Journal of Neurotrauma

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As the incidence of traumatic spinal cord injury (tSCI) in the elderly rises, clinicians are increasingly faced with difficult discussions regarding aggressiveness of management, likelihood of recovery, and survival. Our objective was to outline risk factors associated with in-hospital mortality in elderly surgical and non-surgical patients following tSCI and to determine those unlikely to have a favorable outcome. Data from elderly patients (≥ 65 years of age) in the Canadian Rick Hansen SCI Registry from 2004 to 2017 were analyzed using descriptive analysis. Survival and mortality groups in each of the surgical and non-surgical group were compared to explore factors associated with in-hospital mortality and their impact, using logistical regression. Of 1340 elderly patients, 1018 had surgical data with 826 having had surgery. In the surgical group, the median time to death post-injury was 30 days with 75% dying within 50 days compared with 7 days and 20 days, respectively, in the non-surgical group. Significant predictors for in-hospital mortality following surgery are age, comorbidities, neurological injury severity (American Spinal Injury Association [ASIA] Impairment Scale [AIS]), and ventilation status. The odds of dying 50 days post-surgery are six times higher for patients ≥77 years of age versus those 65–76 years of age, five times higher for those with AIS A versus those with AIS B/C/D, and seven times higher for those who are ventilator dependent. An expected probability of dying within 50 days post-surgery was determined using these results. In-hospital mortality in the elderly after tSCI is high. The trend with age and time to death and the significant predictors of mortality identified in this study can be used to inform clinical decision making and discussions with patients and their families.

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Section: Relation To Literaturecontrasting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-Hospital Mortality for the Elderly with Acute Traumatic Spinal Cord Injury

Inglis

Banaszek

Rivers

et al. 2020

Journal of Neurotrauma

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“…The copyright holder for this preprint this version posted June 30, 2021. ; https://doi.org/10.1101/2021.06.26.21259569 doi: medRxiv preprint other study [4]. Anyway, the imbalanced three-class categories did not significantly compromise the prediction accuracy in the current study.…”

Section: Discussionmentioning

confidence: 48%

“…More importantly, critical SCI patients may suffer from a higher mortality due to renal failure, respiratory failure, hemodynamic instability, sepsis from urinary tract infection, life-threatening autonomic hyper-reflexia etc. [3][4][5][6][7][8] . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Introductionmentioning

confidence: 99%

Discharge prediction of critical patients with spinal cord injury: a machine learning study with 1485 cases

Fan

Liu²,

Yang

et al. 2021

Preprint

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Objectives: Prognostication of spinal cord injury (SCI) is vital, especially for critical patients who need intensive care. The study aims to develop machine-learning (ML) classifiers for discharge prediction of SCI patients in the intensive care unit (ICU). Methods: Clinical data of patients diagnosed with SCI were extracted from the publicly available ICU database. A total of 105 ML classifiers were initially developed to predict the discharge destination (dead, further medical care, home), and then the top 3 classifiers with the best performance were stacked into an ensemble classifier (Esb-Clf). To balance the accuracy and the feasibility, the complete Esb-Clf was finally simplified with top 10 features (simplified Esb-Clf). The micro-average area under the curve (AUC) was used to compare the prediction performance of difference ML classifiers and 6 doctors' artificial prediction. Results: A total of 1485 SCI patients were used for the early and the recent prediction of discharge destination. In the early prediction, the micro-average AUC of the Esb-Clf and the simplified Esb-Clf was 0.846 and 0.835 during the independent testing, respectively. While in the recent prediction, the micro-average AUC of the Esb-Clf and the simplified Esb-Clf was 0.898 and 0.892. Performance of both the Esb-Clf and the simplified Esb-Clf were superior to the doctors' in the early and the recent prediction. Conclusions: ML classifiers can discriminate the discharge destination of SCI patients with high accuracy, feasibility and interpretability. Whether the simplified Esb-Clf as an online predictive tool is applicable to guiding clinical management needs further verification.

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