Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Department of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami Miller School of Medicine, Miami, FL, USA.
J Neurooncol. 2019 Dec;145(3):509-518. doi: 10.1007/s11060-019-03317-6. Epub 2019 Oct 22.
Reducing the time from surgery to adjuvant chemoradiation, by decreasing unnecessary readmissions, is paramount for patients undergoing glioma surgery. The effects of intraoperative risk factors on 30-day readmission rates for such patients is currently unclear. We utilized a predictive model-driven approach to assess the impact of intraoperative factors on 30-day readmission rates for the cranial glioma patient.
Retrospectively, the intraoperative records of 290 patients who underwent glioma surgery at a single institution by a single surgeon were assessed. Data on operative variables including anesthesia specific factors were analyzed via univariate and stepwise regression analysis for impact on 30-day readmission rates. A predictive model was built to assess the capability of these results to predict readmission and validated using leave-one-out cross-validation.
In multivariate analysis, end case hypothermia (OR 0.28, 95% CI [0.09, 0.84]), hypertensive time > 15 min (OR 2.85, 95% CI [1.21, 6.75]), and pre-operative Karnofsky performance status (KPS) (OR 0.63, 95% CI [0.41, 0.98] were identified as being significantly associated with 30-day readmission rates (chi-squared statistic vs. constant model 25.2, p < 0.001). Cross validation of the model resulted in an overall accuracy of 89.7%, a specificity of 99.6%, and area under the receiver operator curve (AUC) of 0.763.
Intraoperative risk factors may help risk-stratify patients with a high degree of accuracy and improve postoperative patient follow-up. Attention should be paid to duration of hypertension and end-case final temperature as these represent potentially modifiable factors that appear to be highly associated with 30-day readmission rates. Prospective validation of our model is needed to assess its potential for implementation as a screening tool to identify patients undergoing glioma surgery who are at a higher risk of post-operative readmission within 30 days.
通过减少不必要的再次入院，缩短手术到辅助放化疗的时间，对接受胶质瘤手术的患者至关重要。目前尚不清楚术中危险因素对这类患者 30 天再入院率的影响。我们利用预测模型驱动的方法来评估术中因素对颅胶质瘤患者 30 天再入院率的影响。
回顾性分析了单中心单外科医生治疗的 290 例胶质瘤患者的术中记录。通过单变量和逐步回归分析，对包括麻醉特定因素在内的手术变量数据进行分析，以评估其对 30 天再入院率的影响。建立了一个预测模型来评估这些结果预测再入院的能力，并使用留一交叉验证进行验证。
在多变量分析中，终末病例低温（OR 0.28，95%CI [0.09，0.84]）、高血压时间> 15 分钟（OR 2.85，95%CI [1.21，6.75]）和术前 Karnofsky 表现状态（KPS）（OR 0.63，95%CI [0.41，0.98]）与 30 天再入院率显著相关（卡方检验与常数模型 25.2，p < 0.001）。模型的交叉验证结果显示，总体准确率为 89.7%，特异性为 99.6%，受试者工作特征曲线下面积（AUC）为 0.763。
术中危险因素可以帮助高危患者进行风险分层，并提高术后患者的随访效果。应注意高血压持续时间和终末病例最终温度，因为这些因素可能是可改变的，与 30 天再入院率密切相关。需要前瞻性验证我们的模型，以评估其作为识别接受胶质瘤手术的患者在 30 天内有更高术后再入院风险的筛查工具的潜在应用价值。
