Frontiers in neurology · 2026
Development and validation of a nomogram for predicting tracheostomy risk in traumatic cervical spinal cord injury.
СтатьяНазвание и аннотация: Перевод в очереди
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Chen Weiting, Jiang Xiaoshuang, Guo Xixi, Lin Jiuzhou, Tang Min, Dou Nanlin
Аннотация
BACKGROUND: Tracheostomy is common in traumatic cervical spinal cord injury (TCSCI) because of respiratory complications, yet objective tools to estimate individual risk remain limited.
METHODS: In this single-center retrospective cohort at the Second Affiliated Hospital, Zhejiang University School of Medicine, we enrolled 308 consecutive ICU admissions with TCSCI (January 2018-March 2023) and randomly split the cohort 7:3 (outcome-stratified) into training (n = 215) and validation (n = 93) sets. Candidate admission predictors were screened with Least Absolute Shrinkage and Selection Operator and then entered into multivariable logistic regression to construct a nomogram. Model performance included discrimination (AUC with bootstrap 95% CIs, 2,000 resamples), calibration (intercept, slope, Brier), and decision curve analysis (DCA). A prespecified clinical threshold of 0.30 was used to summarize sensitivity and specificity.
RESULTS: Five independent predictors were retained-smoking history, thoracic injury, BMI ≥ 25 kg/m2, cervical dislocation, and ASIA grade (A vs. B-D). The model showed strong discrimination (AUC 0.844, 95% CI 0.788-0.896 in training; 0.903, 95% CI 0.823-0.966 in validation) and good calibration. At the 0.30 threshold, performance was Sensitivity 0.781/Specificity 0.725 (training) and Sensitivity 0.812/Specificity 0.852 (validation); DCA demonstrated greater net benefit than "treat all/none" across threshold 0.10-0.70.
CONCLUSION: A parsimonious, five-factor nomogram based on routine admission data provides accurate, clinically interpretable stratification of tracheostomy risk in TCSCI. Clear reporting of ASIA coding and a prespecified decision threshold enhance bedside usability. Prospective, multi-center external validation is warranted.
METHODS: In this single-center retrospective cohort at the Second Affiliated Hospital, Zhejiang University School of Medicine, we enrolled 308 consecutive ICU admissions with TCSCI (January 2018-March 2023) and randomly split the cohort 7:3 (outcome-stratified) into training (n = 215) and validation (n = 93) sets. Candidate admission predictors were screened with Least Absolute Shrinkage and Selection Operator and then entered into multivariable logistic regression to construct a nomogram. Model performance included discrimination (AUC with bootstrap 95% CIs, 2,000 resamples), calibration (intercept, slope, Brier), and decision curve analysis (DCA). A prespecified clinical threshold of 0.30 was used to summarize sensitivity and specificity.
RESULTS: Five independent predictors were retained-smoking history, thoracic injury, BMI ≥ 25 kg/m2, cervical dislocation, and ASIA grade (A vs. B-D). The model showed strong discrimination (AUC 0.844, 95% CI 0.788-0.896 in training; 0.903, 95% CI 0.823-0.966 in validation) and good calibration. At the 0.30 threshold, performance was Sensitivity 0.781/Specificity 0.725 (training) and Sensitivity 0.812/Specificity 0.852 (validation); DCA demonstrated greater net benefit than "treat all/none" across threshold 0.10-0.70.
CONCLUSION: A parsimonious, five-factor nomogram based on routine admission data provides accurate, clinically interpretable stratification of tracheostomy risk in TCSCI. Clear reporting of ASIA coding and a prespecified decision threshold enhance bedside usability. Prospective, multi-center external validation is warranted.