School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, People's Republic of China.
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, People's Republic of China.
Clin Interv Aging. 2022 May 10;17:755-766. doi: 10.2147/CIA.S352641. eCollection 2022.
Predicting poor outcome for stroke patients with chronic kidney disease (CKD) in clinical practice is difficult. There are no tools available to use for predicting poor outcome in these patients. We aimed to construct and validate a dynamic nomogram to identify CKD-stroke patients at high risk of a 3-month poor outcome.
We used data for 502 CKD patients who had an acute ischemic stroke, from Nanjing First Hospital, between September 2014 and September 2020, to train the nomogram. An additional 108 patients enrolled from October 2020 to May 2021 were used for temporal external validation. The performance of the nomogram was evaluated by the area under the receiver operating characteristics curve (AUC) and a calibration plot. The clinical utility of the nomogram was measured by decision curve analysis (DCA) and the clinical impact curve (CIC).
The median age of the cohort was 79 (70-84) years. Age, urea, premorbid modified Rankin Scale (mRS), National Institutes of Health Stroke Scale (NIHSS) on admission, hemiplegia, mechanical thrombectomy, early neurological deterioration, and respiratory infection were used as predictors of 3-month poor outcome to develop the nomogram. In the training set, the AUC of the dynamic nomogram was 0.873 and the calibration plot showed good predictive ability, and both DCA and CIC indicated the excellent clinical usefulness and applicability of the nomogram. In the external validation set, the AUC was 0.875 and the calibration plot also showed good agreement.
This is the first dynamic nomogram constructed for CKD-stroke patients to precisely and expediently identify patients with a high risk of 3-month poor outcome. The outstanding performance and great clinical predictive utility demonstrated the ability of the dynamic nomogram to help clinicians to deploy preventive interventions.
在临床实践中，预测患有慢性肾脏病（CKD）的卒中患者的不良预后较为困难。目前尚无可用的工具来预测这些患者的不良预后。本研究旨在构建并验证一个动态列线图，以识别 3 个月预后不良风险较高的 CKD 卒中患者。
我们使用了 2014 年 9 月至 2020 年 9 月期间南京第一医院收治的 502 例患有急性缺血性卒中的 CKD 患者的数据来训练该列线图。另外，我们还纳入了 2020 年 10 月至 2021 年 5 月期间的 108 例患者用于外部时间验证。使用受试者工作特征曲线下面积（AUC）和校准图来评估列线图的性能。通过决策曲线分析（DCA）和临床影响曲线（CIC）来衡量列线图的临床实用性。
该队列的中位年龄为 79（70-84）岁。年龄、尿素、入院时的预患病改良Rankin 量表（mRS）评分、美国国立卫生研究院卒中量表（NIHSS）评分、偏瘫、机械取栓、早期神经功能恶化和呼吸道感染被用作预测 3 个月不良预后的指标，用于开发该列线图。在训练集中，动态列线图的 AUC 为 0.873，校准图显示出良好的预测能力，DCA 和 CIC 均表明该列线图具有出色的临床实用性和适用性。在外部验证集中，AUC 为 0.875，校准图也显示出良好的一致性。
这是首个为 CKD 卒中患者构建的动态列线图，能够准确、便捷地识别 3 个月预后不良风险较高的患者。其出色的性能和良好的临床预测效用表明，该动态列线图有能力帮助临床医生实施预防干预措施。
