Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients
Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients
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DOI：
10.1016/j.wneu.2020.11.018
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发表时间：
2021-02-16
影响因子：
2
通讯作者：
Mukherjee, Debraj
中科院分区：
文献类型：
--
作者：
Huq, Sakibul;Khalafallah, Adham M.;Mukherjee, Debraj
背景：在以价值为基础的支付模式时代，神经外科医生必须消除低效率并提供高质量的护理。出院处置是脑肿瘤患者临床和经济后果的相关考虑因素。我们开发了一个预测模型和在线计算器，用于脑肿瘤患者术后非家庭出院处置，可纳入术前工作流程。方法：我们回顾了我院2017 - 2019年所有脑肿瘤患者。采用逐步多变量逻辑回归建立了包含术前可用变量的出院处置预测模型。采用接收机工作特性曲线和校准曲线评估模型性能。用2000个样品进行了内部验证。结果：我们的队列包括2335名患者，他们接受了2586次手术，非家庭出院率为16%。非居家出院的显著预测因子为年龄（比值比[OR]， 2.02）、非裔美国人（OR, 1.73）或亚洲人（OR, 2.05）、种族、未婚状态（OR, 1.48）、医疗补助保险（OR, 1.90）、从其他医疗机构入院（OR, 2.30）、较高的五因素修正虚弱指数（OR, 1.61，五因素修正虚弱指数>= 2）和较低的Karnofsky绩效状态（Karnofsky绩效状态每降低10点，OR增加）。该模型校准良好，具有良好的判别性（乐观校正的c统计量，0.82）。部署了一个开放获取计算器（https://neurooncsurgery.shinyapps.io/discharge_calc/）。结论：开发了一种功能强大的脑肿瘤患者非居家出院处置预测模型和在线计算器。通过进一步的验证，该工具可以促进更有效的出院计划，从而提高脑肿瘤患者的护理质量和价值。
BACKGROUND: In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows.METHODS: We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using boot-strapping with 2000 samples.RESULTS: Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index >= 2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/).CONCLUSIONS: A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.