Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram
ObjectiveThe purpose of this study was to develop and internally validate a prediction nomogram model in patients undergoing lumbar fusion surgery.MethodsA total of 310 patients undergoing lumbar fusion surgery were reviewed, and the median and quartile interval were used to describe postoperative l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Surgery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2022.925354/full |
| _version_ | 1811321416555954176 |
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| author | Chen-Xin Lu Zhi-Bin Huang Xiao-Mei Chen Xiao-Dan Wu |
| author_facet | Chen-Xin Lu Zhi-Bin Huang Xiao-Mei Chen Xiao-Dan Wu |
| author_sort | Chen-Xin Lu |
| collection | DOAJ |
| description | ObjectiveThe purpose of this study was to develop and internally validate a prediction nomogram model in patients undergoing lumbar fusion surgery.MethodsA total of 310 patients undergoing lumbar fusion surgery were reviewed, and the median and quartile interval were used to describe postoperative length of stay (PLOS). Patients with PLOS > P75 were defined as prolonged PLOS. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables for building the prolonged PLOS risk model. Multivariable logistic regression analysis was applied to build a predictive model using the variables selected in the LASSO regression model. The area under the ROC curve (AUC) of the predicting model was calculated and significant test was performed. The Kappa consistency test between the predictive model and the actual diagnosis was performed. Discrimination, calibration, and the clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.ResultsAccording to the interquartile range of PLOS in a total of 310 patients, the PLOS of 235 patients was ≤P75 (7 days) (normal PLOS), and the PLOS of 75 patients was > P75 (prolonged PLOS). The LASSO selected predictors that were used to build the prediction nomogram included BMI, diabetes, hypertension, duration of surgery, duration of anesthesia, anesthesia type, intraoperative blood loss, sufentanil for postoperative analgesia, and postoperative complication. The model displayed good discrimination with an AUC value of 0.807 (95% CI: 0.758–0.849, P < 0.001), a Kappa value of 0.5186 (cutoff value, 0.2445, P < 0.001), and good calibration. A high C-index value of 0.776 could still be reached in the interval validation. Decision curve analysis showed that the prolonged PLOS nomogram was clinically useful when intervention was decided at the prolonged PLOS possibility threshold of 3%.ConclusionsThis study developed a novel nomogram with a relatively good accuracy to help clinicians access the risk of prolonged PLOS in lumbar fusion surgery patients. By an estimate of individual risk, surgeons and anesthesiologists may shorten PLOS and accelerate postoperative recovery of lumbar fusion surgery through more accurate individualized treatment. |
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| format | Article |
| id | doaj.art-6b1ce83b8bd44027b593745f8892ad71 |
| institution | Directory Open Access Journal |
| issn | 2296-875X |
| language | English |
| last_indexed | 2024-04-13T13:16:11Z |
| publishDate | 2022-08-01 |
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| series | Frontiers in Surgery |
| spelling | doaj.art-6b1ce83b8bd44027b593745f8892ad712022-12-22T02:45:27ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2022-08-01910.3389/fsurg.2022.925354925354Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogramChen-Xin Lu0Zhi-Bin Huang1Xiao-Mei Chen2Xiao-Dan Wu3Department of Anesthesiology, Fuzhou Second Hospital, Fuzhou, ChinaDepartment of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, ChinaDepartment of Anesthesiology, Fuzhou Second Hospital, Fuzhou, ChinaDepartment of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, ChinaObjectiveThe purpose of this study was to develop and internally validate a prediction nomogram model in patients undergoing lumbar fusion surgery.MethodsA total of 310 patients undergoing lumbar fusion surgery were reviewed, and the median and quartile interval were used to describe postoperative length of stay (PLOS). Patients with PLOS > P75 were defined as prolonged PLOS. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables for building the prolonged PLOS risk model. Multivariable logistic regression analysis was applied to build a predictive model using the variables selected in the LASSO regression model. The area under the ROC curve (AUC) of the predicting model was calculated and significant test was performed. The Kappa consistency test between the predictive model and the actual diagnosis was performed. Discrimination, calibration, and the clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.ResultsAccording to the interquartile range of PLOS in a total of 310 patients, the PLOS of 235 patients was ≤P75 (7 days) (normal PLOS), and the PLOS of 75 patients was > P75 (prolonged PLOS). The LASSO selected predictors that were used to build the prediction nomogram included BMI, diabetes, hypertension, duration of surgery, duration of anesthesia, anesthesia type, intraoperative blood loss, sufentanil for postoperative analgesia, and postoperative complication. The model displayed good discrimination with an AUC value of 0.807 (95% CI: 0.758–0.849, P < 0.001), a Kappa value of 0.5186 (cutoff value, 0.2445, P < 0.001), and good calibration. A high C-index value of 0.776 could still be reached in the interval validation. Decision curve analysis showed that the prolonged PLOS nomogram was clinically useful when intervention was decided at the prolonged PLOS possibility threshold of 3%.ConclusionsThis study developed a novel nomogram with a relatively good accuracy to help clinicians access the risk of prolonged PLOS in lumbar fusion surgery patients. By an estimate of individual risk, surgeons and anesthesiologists may shorten PLOS and accelerate postoperative recovery of lumbar fusion surgery through more accurate individualized treatment.https://www.frontiersin.org/articles/10.3389/fsurg.2022.925354/fullPLOSlumbar fusion surgerynomogrampredictive modelLASSO regression |
| spellingShingle | Chen-Xin Lu Zhi-Bin Huang Xiao-Mei Chen Xiao-Dan Wu Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram Frontiers in Surgery PLOS lumbar fusion surgery nomogram predictive model LASSO regression |
| title | Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram |
| title_full | Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram |
| title_fullStr | Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram |
| title_full_unstemmed | Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram |
| title_short | Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram |
| title_sort | predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery development and assessment of a novel predictive nomogram |
| topic | PLOS lumbar fusion surgery nomogram predictive model LASSO regression |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2022.925354/full |
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