Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis
Abstract Objective To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients. Methods A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection op...
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02787-7 |
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| author | Linghong Wu Xiaozhong Peng Yao Lu Cuiping Fu Liujun She Guangwei Zhu Xianglong Zhuo Wei Hu Xiangtao Xie |
| author_facet | Linghong Wu Xiaozhong Peng Yao Lu Cuiping Fu Liujun She Guangwei Zhu Xianglong Zhuo Wei Hu Xiangtao Xie |
| author_sort | Linghong Wu |
| collection | DOAJ |
| description | Abstract Objective To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients. Methods A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection operator (LASSO) regression was performed on the training sets to screen variables, and the importance of independent variables was ranked via random forest. In addition, various independent variables were used in the construction of models 1 and 2. A receiver operating characteristic curve was used to evaluate the models’ predictive performance. We employed Delong tests to compare the area under the curve (AUC) of the different models. Assessment of the models’ capability to improve classification efficiency was achieved using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The Hosmer–Lemeshow method and calibration curve was utilised to assess the calibration degree, and decision curve to evaluate its clinical practicality. A bootstrap technique that involved 10 cross-validations and was performed 10,000 times was used to conduct internal and external validation. The were outcomes of the model exhibited in a nomogram graphics. The developed nomogram was validated both internally and externally. Results Model 1 was identified as the optimal model. The risk factors for prolonged LOS comprised blood transfusion, operation type, use of tranexamic acid (TXA), diabetes, electrolyte disturbance, body mass index (BMI), surgical procedure performed, the number of preoperative diagnoses and operative time. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.784 and 0.795 for the internal and external validation sets, respectively. Model discrimination was favourable in both the internal (C-statistic, 0.811) and external (C-statistic, 0.814) validation sets. Calibration curve and Hosmer-Lemeshow test showed acceptable agreement between predicted and actual results. The decision curve shows that the model provides net clinical benefit within a certain decision threshold range. Conclusions This study developed and validated a nomogram to identify the risk of prolonged LOS in spinal fusion patients, which may help clinicians to identify high-risk groups at an early stage. Predictors identified included blood transfusion, operation type, use of TXA, diabetes, electrolyte disturbance, BMI, surgical procedure performed, number of preoperative diagnoses and operative time. |
| format | Article |
| id | doaj-art-f794b8edb8a24c1cb01fe6e5e2d6ace4 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-f794b8edb8a24c1cb01fe6e5e2d6ace42025-01-05T12:32:26ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124111410.1186/s12911-024-02787-7Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysisLinghong Wu0Xiaozhong Peng1Yao Lu2Cuiping Fu3Liujun She4Guangwei Zhu5Xianglong Zhuo6Wei Hu7Xiangtao Xie8Guangxi Key Laboratory of Orthopaedic Biomaterials Development and Clinical Translation, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalGuangxi Key Laboratory of Orthopaedic Biomaterials Development and Clinical Translation, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalMedical Department, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalMedical Department, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalMedical Department, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalMedical Department, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalGuangxi Key Laboratory of Orthopaedic Biomaterials Development and Clinical Translation, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalSpine Surgery, Liuzhou People’s HospitalGuangxi Key Laboratory of Orthopaedic Biomaterials Development and Clinical Translation, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker’s HospitalAbstract Objective To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients. Methods A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection operator (LASSO) regression was performed on the training sets to screen variables, and the importance of independent variables was ranked via random forest. In addition, various independent variables were used in the construction of models 1 and 2. A receiver operating characteristic curve was used to evaluate the models’ predictive performance. We employed Delong tests to compare the area under the curve (AUC) of the different models. Assessment of the models’ capability to improve classification efficiency was achieved using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The Hosmer–Lemeshow method and calibration curve was utilised to assess the calibration degree, and decision curve to evaluate its clinical practicality. A bootstrap technique that involved 10 cross-validations and was performed 10,000 times was used to conduct internal and external validation. The were outcomes of the model exhibited in a nomogram graphics. The developed nomogram was validated both internally and externally. Results Model 1 was identified as the optimal model. The risk factors for prolonged LOS comprised blood transfusion, operation type, use of tranexamic acid (TXA), diabetes, electrolyte disturbance, body mass index (BMI), surgical procedure performed, the number of preoperative diagnoses and operative time. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.784 and 0.795 for the internal and external validation sets, respectively. Model discrimination was favourable in both the internal (C-statistic, 0.811) and external (C-statistic, 0.814) validation sets. Calibration curve and Hosmer-Lemeshow test showed acceptable agreement between predicted and actual results. The decision curve shows that the model provides net clinical benefit within a certain decision threshold range. Conclusions This study developed and validated a nomogram to identify the risk of prolonged LOS in spinal fusion patients, which may help clinicians to identify high-risk groups at an early stage. Predictors identified included blood transfusion, operation type, use of TXA, diabetes, electrolyte disturbance, BMI, surgical procedure performed, number of preoperative diagnoses and operative time.https://doi.org/10.1186/s12911-024-02787-7Spinal fusionProlonged length of hospital staysNomogramPredictionRisk factors |
| spellingShingle | Linghong Wu Xiaozhong Peng Yao Lu Cuiping Fu Liujun She Guangwei Zhu Xianglong Zhuo Wei Hu Xiangtao Xie Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis BMC Medical Informatics and Decision Making Spinal fusion Prolonged length of hospital stays Nomogram Prediction Risk factors |
| title | Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis |
| title_full | Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis |
| title_fullStr | Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis |
| title_full_unstemmed | Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis |
| title_short | Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis |
| title_sort | development and validation of a nomogram model for prolonged length of stay in spinal fusion patients a retrospective analysis |
| topic | Spinal fusion Prolonged length of hospital stays Nomogram Prediction Risk factors |
| url | https://doi.org/10.1186/s12911-024-02787-7 |
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