Frontiers in medicine · 2025
Development and validation of a predictive model for postoperative delirium after traumatic cervical spinal cord surgery.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Xia Han, Tusongtuoheti Dilinuer
Аннотация
OBJECTIVE: This study aimed to develop and validate a nomogram for predicting the risk of postoperative delirium (POD) in patients undergoing surgery for traumatic cervical spinal cord injury (TCSCI).
METHODS: We retrospectively analyzed 412 patients with TCSCI who underwent surgery between January 2018 and December 2024. POD was diagnosed using the Confusion Assessment Method (CAM). Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. A nomogram was constructed based on these factors, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Internal validation was performed via bootstrap resampling.
RESULTS: The incidence of POD was 22.08%. Multivariate analysis identified six independent predictors: diabetes mellitus (OR = 2.156, 95% CI: 1.451-3.204), history of alcohol abuse (OR = 1.929, 95% CI: 1.259-2.957), ASIA Impairment Scale grade A-B (OR = 3.030, 95% CI: 1.910-4.807), prolonged operative duration (OR = 1.363 per hour, 95% CI: 1.141-1.628), intraoperative blood transfusion (OR = 2.473, 95% CI: 1.648-3.712), and decreased postoperative hemoglobin level (OR = 0.967 per 1 g/dL, 95% CI: 0.952-0.982). The nomogram demonstrated excellent discrimination, with an AUC of 0.912 (95% CI: 0.883-0.941), sensitivity of 85.6%, and specificity of 82.3%. Calibration and DCA indicated high predictive accuracy and clinical utility.
CONCLUSION: We developed a nomogram incorporating six readily available clinical factors to predict POD in TCSCI patients. The model shows promising performance and may assist in early identification of high-risk individuals, though external validation is warranted before clinical implementation.
METHODS: We retrospectively analyzed 412 patients with TCSCI who underwent surgery between January 2018 and December 2024. POD was diagnosed using the Confusion Assessment Method (CAM). Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. A nomogram was constructed based on these factors, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Internal validation was performed via bootstrap resampling.
RESULTS: The incidence of POD was 22.08%. Multivariate analysis identified six independent predictors: diabetes mellitus (OR = 2.156, 95% CI: 1.451-3.204), history of alcohol abuse (OR = 1.929, 95% CI: 1.259-2.957), ASIA Impairment Scale grade A-B (OR = 3.030, 95% CI: 1.910-4.807), prolonged operative duration (OR = 1.363 per hour, 95% CI: 1.141-1.628), intraoperative blood transfusion (OR = 2.473, 95% CI: 1.648-3.712), and decreased postoperative hemoglobin level (OR = 0.967 per 1 g/dL, 95% CI: 0.952-0.982). The nomogram demonstrated excellent discrimination, with an AUC of 0.912 (95% CI: 0.883-0.941), sensitivity of 85.6%, and specificity of 82.3%. Calibration and DCA indicated high predictive accuracy and clinical utility.
CONCLUSION: We developed a nomogram incorporating six readily available clinical factors to predict POD in TCSCI patients. The model shows promising performance and may assist in early identification of high-risk individuals, though external validation is warranted before clinical implementation.