Department of Neurological Surgery, University of Southern California, Los Angeles, CA, USA; Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA; Department of Neurological Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Department of Neurological Surgery, University of Southern California, Los Angeles, CA, USA.
World Neurosurg. 2024 Jul;187:e560-e567. doi: 10.1016/j.wneu.2024.04.129. Epub 2024 Apr 26.
We evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery.
Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) was compared using DeLong's test.
A total of 8104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI + frailty + age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI + frailty alone (AUC 0.603 vs. 0.599, P = 0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, P = 0.043).
These data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model's performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.
我们评估了年龄、合并症负担和/或衰弱在预测退行性脊柱侧凸手术患者 90 天再入院中的作用。
通过医疗保健成本和利用项目全国再入院数据库识别患者。使用约翰霍普金斯调整临床组衰弱定义指标评估衰弱。使用 Elixhauser 合并症指数（ECI）评估合并症。使用广义线性混合效应模型，使用年龄、衰弱和/或 ECI 预测再入院。使用 DeLong 检验比较曲线下面积（AUC）。
共确定了 8104 名患者。再入院率为 9.8%，感染是最常见的原因（3.5%）。我们的第一个模型使用年龄、ECI 和/或衰弱作为主要预测因素。ECI+衰弱+年龄的组合表现最佳，但与单独使用 ECI+衰弱相比，包含年龄并不能显著提高性能（AUC 0.603 与 0.599，P=0.290）。第二个模型仅使用年龄和衰弱作为主要预测因素表现更好，然而，与单独使用衰弱相比，包含年龄会降低性能（AUC 0.747 与 0.743，P=0.043）。
这些数据支持衰弱作为一个全国代表性样本中 90 天再入院的预测因素。衰弱本身的表现优于 ECI 和年龄的组合。有趣的是，整合年龄并没有显著改善模型的性能。限制包括使用国家登记处和单一衰弱指数。这为探索生物年龄，而不是实际年龄，作为手术风险评估的潜在工具提供了动力。
