Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments - Yang, Fan; Dong, Ruirui; Wang, Yating; Guo, Junshuang; Zang, Qiuling; Wen, Lijun; Huang, Peipei; Qin, Jinjin; Song, Dandan; Ren, Zhiping; Teng, Junfang; Miao, Wang (Frontiers in Neurology, 2024)
Objectives To investigate the risk factors of pulmonary infection in patients with severe myelitis and construct a prediction model. Methods The clinical data of 177 patients with severe myelitis at admission from the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were retrospectively analyzed. The predicting factors associated with pulmonary infection were screened by multivariate logistic regression analysis, and the nomogram model was constructed, and the predictive efficiency of the model was evaluated, which was verified by calibration curve, Hosmer–Lemeshow goodness-of-fit test and decision curve analysis. Results Of the 177 patients with severe myelitis, 38 (21.5%) had pulmonary infection. Multivariate logistic regression analysis showed that neutrophil percentage to albumin ratio (NPAR) (OR = 6.865, 95%CI:1.746–26.993, p = 0.006) and high cervical cord lesion (OR = 2.788, 95%CI:1.229–6.323, p = 0.014) were independent risk factors for pulmonary infection, and the combined nomogram could easily predict the occurrence of pulmonary infection, with a C-index of 0.766 (95% CI: 0.678–0.854). The calibration curve, Hosmer-Lemeshow goodness-of-fit test (χ2 = 9.539, p = 0.299) and decision curve analysis showed that the model had good consistency and clinical applicability. Conclusion The nomogram model constructed based on NPAR combined with high cervical cord lesion at admission has good clinical application value in predicting pulmonary infection in patients with severe myelitis, which is conducive to clinicians’ evaluation of patients.