Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023
Establishment of a comprehensive diagnostic model for neuromyelitis optica spectrum disorders based on the analysis of laboratory indicators and clinical data.
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Jiang Wencan, Yin Xiaoxuan, Wang Yufei, Ding Yaowei, Pan Yu, Zheng Guanghui, Lv Hong, Chen Kelin, Li Siwen, Wang Lijuan, Shi Yijun, Li Guoge, Zhang Guojun
Аннотация
BACKGROUND: To establish a comprehensive diagnostic model for neuromyelitis optica spectrum disorders (NMOSDs) based on laboratory indicators and clinical data.
METHODS: A retrospective method was used to query the medical records of patients with NMOSD from January 2019 to December 2021. At the same time, clinical data of other neurological diseases were also collected for comparison. Clinical data of the NMOSD group and non-NMOSD group were analyzed, and the diagnostic model was established based on these data. In addition, the model was evaluated and verified by the receiver operating curve.
RESULTS: A total of 73 patients with NMOSD were included, and the ratio of males to females was 1:3.06. The indicators that showed differences between the NMOSD group and non NMOSD group included neutrophils (P = 0.0438), PT (P = 0.0028), APTT (P < 0.0001), CK (P = 0.002), IBIL (P = 0.0181), DBIL (P < 0.0001), TG (P = 0.0078), TC (P = 0.0117), LDL-C (P = 0.0054), ApoA1 (P = 0.0123), ApoB (P = 0.0217), TPO antibody (P = 0.012), T3 (P = 0.0446), B lymphocyte subsets (P = 0.0437), urine sg (P = 0.0123), urine pH (P = 0.0462), anti-SS-A antibody (P = 0.0036), RO-52 (P = 0.0138), CSF simplex virus antibody I-IGG (P = 0.0103), anti-AQP4 antibody (P < 0.0001), and anti-MOG antibody (P = 0.0036). Logistic regression analysis showed that changes in ocular symptoms, anti-SSA antibody, anti-TPO antibody, B lymphocyte subsets, anti-AQP4 antibody, anti-MOG antibody, TG, LDL, ApoB, and APTT had a significant impact on diagnosis. The AUC of the combined analysis was 0.959. The AUC of the new ROC for AQP4- and MOG- antibody negative NMOSD was 0.862.
CONCLUSIONS: A diagnostic model was successfully established, which can play an important role in differential diagnosis of NMOSD.
METHODS: A retrospective method was used to query the medical records of patients with NMOSD from January 2019 to December 2021. At the same time, clinical data of other neurological diseases were also collected for comparison. Clinical data of the NMOSD group and non-NMOSD group were analyzed, and the diagnostic model was established based on these data. In addition, the model was evaluated and verified by the receiver operating curve.
RESULTS: A total of 73 patients with NMOSD were included, and the ratio of males to females was 1:3.06. The indicators that showed differences between the NMOSD group and non NMOSD group included neutrophils (P = 0.0438), PT (P = 0.0028), APTT (P < 0.0001), CK (P = 0.002), IBIL (P = 0.0181), DBIL (P < 0.0001), TG (P = 0.0078), TC (P = 0.0117), LDL-C (P = 0.0054), ApoA1 (P = 0.0123), ApoB (P = 0.0217), TPO antibody (P = 0.012), T3 (P = 0.0446), B lymphocyte subsets (P = 0.0437), urine sg (P = 0.0123), urine pH (P = 0.0462), anti-SS-A antibody (P = 0.0036), RO-52 (P = 0.0138), CSF simplex virus antibody I-IGG (P = 0.0103), anti-AQP4 antibody (P < 0.0001), and anti-MOG antibody (P = 0.0036). Logistic regression analysis showed that changes in ocular symptoms, anti-SSA antibody, anti-TPO antibody, B lymphocyte subsets, anti-AQP4 antibody, anti-MOG antibody, TG, LDL, ApoB, and APTT had a significant impact on diagnosis. The AUC of the combined analysis was 0.959. The AUC of the new ROC for AQP4- and MOG- antibody negative NMOSD was 0.862.
CONCLUSIONS: A diagnostic model was successfully established, which can play an important role in differential diagnosis of NMOSD.