Development and validation of an interpretable nomogram for predicting the risk of the prolonged postoperative length of stay for tuberculous spondylitis: a novel approach for risk stratification.

BACKGROUND

Tuberculous spondylitis (TS) is a challenging health care condition requiring spine surgery, and predicting the probabilities of prolonged postoperative length of stay (PLOS) can aid in effective management, especially with the increasing number of aging patients. This study aimed to develop and validate an interpretable nomogram for risk stratification and prediction of prolonged PLOS for TS patients after surgery, utilizing SHapley Additive exPlanations (SHAP) for model interpretation.

METHODS

This retrospective analysis comprised data of 580 TS patients that were hospitalized between January 2016 and December 2022. Prolonged PLOS was defined as hospitalization exceeding the 75th percentile. Factors associated with an increased risk of prolonged PLOS were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method and incorporated into a multivariable logistic regression model. SHAP values were generated to explain the contribution of each variable in predicting prolonged PLOS. Based on the identified risk variables, a nomogram was constructed using the SHAP values to represent each factor's contribution to the final outcome. The nomogram's effectiveness was assessed using calibration plots, discrimination analysis, and decision curve analysis.

RESULTS

Among 580 patients, 127 had prolonged postoperative length of stay (PLOS > 11 days), while 453 had normal stays (≤ 11 days). The developed nomogram incorporated 7 significant risk factors along with their corresponding SHAP values, which are C-reactive protein (CRP), multiple sections, CT-vertebral destruction, MRI-epidural abscess, transfusions, blood loss and postoperative drainage (Drainage volume on the first day after surgery). The calibration graphs showed that the expected and observed probability of prolonged PLOS was in close agreement. Discrimination analysis yielded an area under the curve (AUC) of 0.867 (95% CI: 0.828-0.908) in the training set, indicating good predictive performance. Decision curve analysis confirmed the clinical utility of the nomogram in risk stratification and treatment decision-making.

CONCLUSIONS

By utilizing SHAP for model interpretation, this study provides an interpretable nomogram for assessing the possibility of extended PLOS in TS patients. The inclusion of SHAP values allows clinicians to understand the contribution of each variable in the prediction, thereby increasing transparency and aiding in the decision-making process. This nomogram has the potential to contribute to improved patient management and optimization of resource allocation. Prospective validation studies are recommended to further evaluate its effectiveness in clinical practice.

CLINICAL TRIAL NUMBER

Not applicable.