Jin Michael C, Ho Allen L, Feng Austin Y, Medress Zachary A, Pendharkar Arjun V, Rezaii Paymon, Ratliff John K, Desai Atman M
Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Neurospine. 2022 Mar;19(1):133-145. doi: 10.14245/ns.2143244.622. Epub 2022 Mar 31.
Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors.
IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset.
A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n = 5,023, 99.3%) and tumors were most commonly found in the thoracic region (n = 1,941, 38.4%), followed by the lumbar (n = 1,781, 35.2%) and cervical (n = 1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%).
Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.
硬脊膜内脊髓肿瘤并不常见,虽然已经探讨了临床特征与手术结果之间的关联,但将各种预测因素整合到一个综合风险模型中的文献仍然很少。旨在预测脊髓肿瘤患者术后的结果。
查询IBM MarketScan索赔数据库,以获取2007年至2016年间接受硬脊膜内肿瘤手术的成年患者。主要关注的结果是未回家出院和出院后90天再入院。次要结果包括住院时间和术后并发症。使用正则化逻辑回归框架(LASSO,最小绝对收缩和选择算子)进行风险建模,并在保留的子集中进行验证。
共纳入5060例成年患者。大多数手术采用后路入路(n = 5023,99.3%),肿瘤最常见于胸段(n = 1941,38.4%),其次是腰段(n = 1781,35.2%)和颈段(n = 1294,25.6%)。与仅使用肿瘤特异性或患者特异性特征的模型相比,我们的综合模型显示出更好的辨别力(曲线下面积[AUC][未回家出院]=0.786;AUC[90天再入院]=0.693)和准确性(Brier评分[未回家出院]=0.155;Brier评分[90天再入院]=0.093)。与预测为最低风险的患者相比,预测为未回家出院最高风险的患者需要持续护理的频率高16.3倍(64.5%对3.9%)。同样,预测为出院后再入院最高风险的患者的再入院频率是预测为最低风险患者的7.3倍(32.6%对4.4%)。
利用一组涵盖肿瘤、患者和住院数据的不同临床特征,我们开发并验证了整合不同临床数据的风险模型,用于预测未回家出院和出院后再入院情况。