Can a Novel NAFO Nomogram Improve Prediction of the Neurological Function at Discharge in Acut Ischemic Stroke Patients with Mechanical Thrombectomy? A Chinese Multicenter Cohort Study
Corresponding AuthorJian-Jun Zou
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
A B S T R A C T
Purpose: We aimed to develop a nomogram for individualized prediction of neurological impairment for acute ischemic stroke (AIS) patients with mechanical thrombectomy (MT). Methods: We conducted a multicenter prospective study in Chinese AIS patients with MT from January 2014 to December 2018. The clinical outcome was the neurological impairment at discharge. The nomogram was generated by multivariate logistic regression analysis for predicting the probability of neurological impairment using a forward stepwise method that included age, NIHSS (National Institutes of Health Stroke Scale) score on admission, fasting blood glucose (FBG), creatinine, clinical and demographic characteristics as pre-established variables. We assessed the discriminative performance by using the area under the receiver-operating characteristic curve (AUC-ROC) and calibration of neurological impairment prediction model by using the Hosmer-Lemeshow test. Results: The study included 238 patients, NIHSS score on admission (OR: 1.148, p < 0.0001), Age (OR: 1.028, p = 0.031), FBG (OR: 1.147, p = 0.025) and OTT (OR: 1.002, p=0.013) remained independent predictors of neurological impairment to develop the NAFO nomogram in Chinese AIS patients with MT. The AUC-ROC value of the NAFO nomogram was 0.792 (95% CI: 0.733 – 0.851) in the cohort. Calibration was good (p = 0.459 for the Hosmer-Lemeshow test). Conclusions: The NAFO nomogram is the first nomogram developed and validated in Chinese AIS patients with MT and it may be used to predict the neurological impairment for these patients.
Article TypeResearch Article
Publication historyReceived: Wed 01, Jan 2020
Accepted: Fri 17, Jan 2020
Published: Mon 27, Jan 2020
Copyright© 2021 Jian-Jun Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hosting by Science Repository.