Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury
Abstract Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | European Journal of Medical Research |
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| Online Access: | https://doi.org/10.1186/s40001-024-01655-4 |
| _version_ | 1797275384640700416 |
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| author | Zhibin Xing Lingli Cai Yuxuan Wu Pengfei Shen Xiaochen Fu Yiwen Xu Jing Wang |
| author_facet | Zhibin Xing Lingli Cai Yuxuan Wu Pengfei Shen Xiaochen Fu Yiwen Xu Jing Wang |
| author_sort | Zhibin Xing |
| collection | DOAJ |
| description | Abstract Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis. Methods This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer–Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853–0.961) and 0.856 (95% CI = 0.746–0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147–0.940) and 0.784 (95% CI = 0.282–1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004–0.123; P = 0.037) and 0.103 (95% CI = 0.002–0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system. Conclusion We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient’s status and implement interventions to improve prognosis accordingly. |
| first_indexed | 2024-03-07T15:13:35Z |
| format | Article |
| id | doaj.art-f30a90f4eee440b48d5ba31f3ff7b2cd |
| institution | Directory Open Access Journal |
| issn | 2047-783X |
| language | English |
| last_indexed | 2024-03-07T15:13:35Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj.art-f30a90f4eee440b48d5ba31f3ff7b2cd2024-03-05T18:04:23ZengBMCEuropean Journal of Medical Research2047-783X2024-01-012911910.1186/s40001-024-01655-4Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injuryZhibin Xing0Lingli Cai1Yuxuan Wu2Pengfei Shen3Xiaochen Fu4Yiwen Xu5Jing Wang6The First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityThe First Affiliated Hospital of Jinan UniversityAbstract Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis. Methods This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer–Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853–0.961) and 0.856 (95% CI = 0.746–0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147–0.940) and 0.784 (95% CI = 0.282–1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004–0.123; P = 0.037) and 0.103 (95% CI = 0.002–0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system. Conclusion We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient’s status and implement interventions to improve prognosis accordingly.https://doi.org/10.1186/s40001-024-01655-4Cervical spine fractureMortalityNomogramPredictive modelIntensive care unit |
| spellingShingle | Zhibin Xing Lingli Cai Yuxuan Wu Pengfei Shen Xiaochen Fu Yiwen Xu Jing Wang Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury European Journal of Medical Research Cervical spine fracture Mortality Nomogram Predictive model Intensive care unit |
| title | Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury |
| title_full | Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury |
| title_fullStr | Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury |
| title_full_unstemmed | Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury |
| title_short | Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury |
| title_sort | development and validation of a nomogram for predicting in hospital mortality of patients with cervical spine fractures without spinal cord injury |
| topic | Cervical spine fracture Mortality Nomogram Predictive model Intensive care unit |
| url | https://doi.org/10.1186/s40001-024-01655-4 |
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