| Popis: |
Fusang Wang,1,2,* Xiaohan Zheng,1,2,* Juan Zhang,3,* Fuping Jiang,4 Nihong Chen,5 Mengyi Xu,5 Yuezhang Wu,2 Junshan Zhou,5Xiaoli Cui,3Jianjun Zou,2,61School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 2Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Geriatrics, Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 5Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 6Department of Clinical Pharmacology, Nanjing First Hospital, China Pharmaceutical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianjun Zou; Xiaoli Cui, Email zoujianjun100@126.com; 849110554@qq.comBackground and Purpose: 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.Patients and Methods: 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).Results: 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.Conclusion: 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.Keywords: dynamic nomogram, stroke, chronic kidney disease, poor outcome, predict |
