Development and validation of a risk prediction model for tracheostomy in acute traumatic cervical spinal cord injury patients

Hou, Yun Fei; Lv, Yang; Zhou, Fang; Tian, Yun; Ji, Hongquan; Zhang, Zhi Shan; Guo, Yan

doi:10.1007/s00586-014-3731-y

Cited by 29 publications

(28 citation statements)

References 22 publications

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“…It is reported that the tracheostomy rate ranges from 8.4% to 62.1% in patients with CSCI [ 8 – 23 ]. Although many studies have investigated the factors that affect tracheostomy after CSCI, the results are different, and a consensus on the predictive factors for tracheostomy has not yet been reached [ 9 , 24 ]. This study is the first to perform a meta-analysis on various reported factors that influence tracheostomy.…”

Section: Discussionmentioning

confidence: 99%

“…The influencing factors for tracheostomy after CSCI have been widely studied. Among these previous studies, age [ 8 – 21 ], sex [ 8 , 9 , 11 , 12 , 14 – 16 , 19 , 20 , 22 ], AIS (American Spinal Injury Association Impairment Scale) [ 8 – 22 ], neurological level of injury (NLI) [ 8 , 9 , 11 , 12 , 14 , 16 – 23 ], injury seventy score (ISS) [ 8 , 9 , 12 , 14 , 19 , 20 , 22 ], Glasgow Coma Scale (GCS) score [ 8 , 11 , 14 , 15 ], associated injury [ 8 , 9 , 11 , 12 , 16 , 19 ], respiratory complications [ 9 , 10 , 12 , 13 , 19 ], smoking history [ 9 , 16 , 19 , 21 ], and other factors have been reported. However, a consensus has not been reached due to the differences among these previous results.…”

Section: Introductionmentioning

confidence: 99%

“…However, a consensus has not been reached due to the differences among these previous results. Thus, the predictive indexes for tracheostomy after CSCI remain unclear and controversial [ 9 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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A Meta-Analysis of the Influencing Factors for Tracheostomy after Cervical Spinal Cord Injury

Wang

Guo²,

Fan

et al. 2018

BioMed Research International

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Background Traumatic cervical spinal cord injury (CSCI) is a common disease that has high complication, disability, and mortality rates and a poor prognosis. Tracheostomy is an important supportive therapy for patients with CSCI. However, a consensus on the predictive factors for tracheostomy after CSCI has not been reached. Objective This meta-analysis study assessed the influencing factors for tracheostomy after CSCI. Methods We searched for relevant studies on the influencing factors for tracheostomy after CSCI. The extracted data were analyzed using RevMan 5.3 software. We calculated the odds ratio (OR) or mean difference (MD) and 95% confidence intervals (CIs). Results Sixteen eligible studies containing 9697 patients with CSCI were selected. The pooled OR (MD) and 95% CI of the influencing factors were as follows: age (mean ± SD): -0.98 (-4.00 to 2.03), advanced age: 1.93 (0.80 to 4.63), sex (male): 1.29 (1.12 to 1.49), American Spinal Injury Association Impairment Scale (AIS) A grade: 7.79 (5.28 to 11.50), AIS B grade: 1.15 (1.13 to 2.02), AIS C grade: 0.28 (0.20 to 0.41), AIS D grade: 0.04 (0.02 to 0.09), neurological level of injury (upper CSCI): 2.36 (1.51 to 3.68), injury severity score (ISS): 8.97 (8.11 to 9.82), Glasgow Coma Scale (GCS) score ≤8: 6.03 (2.19 to 16.61), thoracic injury: 1.78 (1.55 to 2.04), brain injury: 0.96 (0.55 to 1.69), respiratory complications: 5.97 (4.03 to 8.86), smoking history: 1.45 (0.99 to 2.13), traffic accident injury: 1.27 (0.92 to 1.74), and fall injury: 0.72 (0.52 to 1.01). Conclusions The current evidence shows that male sex, AIS A grade, AIS B grade, neurological level of injury (upper CSCI), high ISS, GCS≤8, thoracic injury, and respiratory complications are risk factors for tracheostomy after CSCI, and AIS C grade and AIS D grade are protective factors. This study will allow us to use these factors for tracheostomy decisions and ultimately optimize airway management in patients with CSCI.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Meta-Analysis of the Influencing Factors for Tracheostomy after Cervical Spinal Cord Injury

Wang

Guo²,

Fan

et al. 2018

BioMed Research International

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“…Lesion level, as well as completeness, is important. Changes on magnetic resonance imaging at the C3 level are predictive of the need for tracheostomy [115] and C omo et al [29] found a tracheostomy rate of 81–83% in patients with a complete SCI above the level of C5. Overall tracheostomy incidence in tetraplegia is reported to be between 10% and 60% [116, 117] with FVC being an important predictor of the need for tracheostomy, with cut-points of <830 and ≤500 mL.…”

Section: Respiratory Treatmentmentioning

confidence: 99%

Respiratory problems and management in people with spinal cord injury

Berlowitz

Wadsworth

Ross

2016

Breathe

141

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Spinal cord injury (SCI) is characterised by profound respiratory compromise secondary to the level of loss of motor, sensory and autonomic control associated with the injury. This review aims to detail these anatomical and physiological changes after SCI, and outline their impact on respiratory function. Injury-related impairments in strength substantially alter pulmonary mechanics, which in turn affect respiratory management and care. Options for treatments must therefore be considered in light of these limitations.Key pointsRespiratory impairment following spinal cord injury (SCI) is more severe in high cervical injuries, and is characterised by low lung volumes and a weak cough secondary to respiratory muscle weakness.Autonomic dysfunction and early-onset sleep disordered breathing compound this respiratory compromise.The mainstays of management following acute high cervical SCI are tracheostomy and ventilation, with noninvasive ventilation and assisted coughing techniques being important in lower cervical and thoracic level injuries.Prompt investigation to ascertain the extent of the SCI and associated injuries, and appropriate subsequent management are important to improve outcomes.Educational aimsTo describe the anatomical and physiological changes after SCI and their impact on respiratory function.To describe the changes in respiratory mechanics seen in cervical SCI and how these changes affect treatments.To discuss the relationship between injury level and respiratory compromise following SCI, and describe those at increased risk of respiratory complications.To present the current treatment options available and their supporting evidence.

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“…As an automatic “machine‐learning” method, classification and regression tree (CART) analysis can be used to classify people into ordinal risk or clinically important categories. This tree‐building technique could help in the selection of risk factors irrespective of their distributions or interactions; it has been shown to be effective in the development of clinical decision rules that perform as well or better than rules developed using more traditional methods . This simple classification tool may also help to provide a reference pattern of risk discrimination for clinical decision‐making on hip fracture prevention.…”

Section: Introductionmentioning

confidence: 99%

Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?

Cummings

Yip

et al. 2019

JBMR Plus

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Although the WHO fracture risk algorithm (FRAX) is used to predict fracture, the utility of some simple machine‐learning methods, such as classification and regression trees (CARTs) should be evaluated to determine their efficacy in fracture prediction. Follow‐up time for the hip fracture analyses of 5977 community‐dwelling American men aged ≥65 years old was truncated to 10 years. There were 172 (2.9%) men who had an incident nontraumatic hip fracture. The CARTs were developed using hip BMD and common clinical risk factors as follows: model 1 = using classification with continuous variables of age, total hip BMD, and femoral neck BMD, or together with common clinical risk factors; and model 2 = using classification with continuous variables of age, total hip BMD, femoral neck BMD, FRAX score, osteoporosis by T‐score at the hip, and common clinical risk factors. The predictive performance of risk models derived from CARTs was compared with the basic classification of FRAX at 3% (basic model). From model 1, discriminators selected by CART were total hip BMD, age, and femoral neck BMD; no other clinical risk factors were selected. From model 2, discriminators selected by CART were FRAX score, femoral neck BMD, and age. Compared with the basic model using only a high‐risk group by FRAX ≥3%, no significantly improved predictive performance was demonstrated by model 1 or model 2 as identified by CART with the area under the receiver‐operating characteristic curve for each model of 0.714 (95% CI, 0.676 to 0.751) or 0.726 (95% CI, 0.690 to 0.762) versus 0.703 (95% CI, 0.667 to 0.740), respectively. The improved overall net reclassification improvement index was 0.02 (95% CI, –0.04 to 0.08) and 0.05 (95% CI, –0.01 to 0.10), respectively. Although a FRAX category is a good clinical indicator for hip fracture risk, a simple classification by age and BMD may provide an alternative way to estimate a clinical risk level of 3.0%. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

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Development and validation of a risk prediction model for tracheostomy in acute traumatic cervical spinal cord injury patients

Cited by 29 publications

References 22 publications

A Meta-Analysis of the Influencing Factors for Tracheostomy after Cervical Spinal Cord Injury

A Meta-Analysis of the Influencing Factors for Tracheostomy after Cervical Spinal Cord Injury

Respiratory problems and management in people with spinal cord injury

Can Classification and Regression Tree Analysis Help Identify Clinically Meaningful Risk Groups for Hip Fracture Prediction in Older American Men (The MrOS Cohort Study)?

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