AIM:To analyze the influencing factors of postoperative malignant glaucoma(MG)in patients with primary angle-closure glaucoma(PACG)using logistic regression and decision tree models.METHODS:A retrospective study was conducted on PACG patients who underwent surgery at Eye Hospital of Handan City from March 2020 to March 2025. Patients were divided into two groups: the MG group, who developed MG within 6 mo postoperatively, and the non-MG group. Data were collected from the electronic medical record system. Univariate analysis was performed, followed by multivariate logistic regression to identify independent risk factors. A classification and regression tree model was constructed to visualize the hierarchical relationships among predictors. The predictive performance of the two models was evaluated and compared using receiver operating characteristic(ROC)curve analysis.RESULTS:Totally 182 cases(182 eyes)with PACG were enrolled in this study, including 91 cases(91 eyes)in the MG group and 91 cases(91 eyes)in the non-MG group. In the MG group, there were 53 males and 38 females; 69 cases were aged ≥60 y and 22 cases were aged <60 y. In the non-MG group, there were 47 males and 44 females; 33 cases were aged ≥60 y and 58 cases were aged <60 y. The non-MG group comprised 91 patients, including 47 males and 44 females. Among them, 33 cases were aged ≥60 y, and 58 cases were aged<60 y. The MG group had significantly higher proportions of patients aged ≥60 y, diabetes, moderate-stage PACG, persistent high intraocular pressure(IOP), complete anterior chamber angle closure, lens thickness <4.5 mm, axial length <22 mm, and severe postoperative inflammation compared to the non-MG group(all P<0.01). Multivariate Logistic regression identified the following as independent influencing factors for postoperative MG: age [OR (95%CI)=2.136(1.401-3.255)], PACG stage [OR (95%CI)=2.996(2.044-4.391)], IOP [OR (95%CI)=3.527(1.604-7.755)],anterior chamber angle [OR (95%CI)=4.826(2.498-9.324)], axial length [OR (95%CI)=5.125(1.265-20.771)], and severe postoperative inflammation [OR (95%CI)=2.338(1.478-3.699)](all P<0.05). The decision tree model selected six explanatory variables: age, PACG stage, IOP, anterior chamber angle status, axial length, and severe postoperative inflammation. Axial length was the primary splitting factor at the root node. The areas under the ROC curve(AUC)for the logistic regression and decision tree models were 0.913(0.863-0.950)and 0.921(0.872-0.956), respectively, with no significant difference between them(Z=0.561, P=0.575).CONCLUSION:Both the logistic regression and decision tree models effectively identify key influencing factors for postoperative MG in PACG patients, including age, PACG stage, IOP, anterior chamber angle status, axial length, and severe postoperative inflammation. The decision tree model offers an intuitive, visual representation of risk stratification, facilitating clinical decision-making. Both models are applicable for clinical risk assessment.

AIM: To investigate and analyze serum nitric oxide synthase 4(NOX4)and matrix metalloproteinase(MMP)-2 expressions in patients with diabetes mellitus(DM)complicated with neovascular glaucoma(NVG)and their clinical significance.METHODS: A prospective study was conducted on 161 patients with DM complicated with NVG admitted to Handan City Eye Hospital(The Third Hospital of Handan)from June 2020 to June 2023. Based on whether complications occurred 1 a after trabeculectomy in the study group patients, they were divided into a group with good prognosis(n=90)and a group with poor prognosis(n=71). During the same period, 161 patients with chronic angle-closure glaucoma without iris neovascularization were selected as the control group. ELISA method was applied to detect the expression levels of serum NOX4 and MMP-2. ROC curve was plotted to evaluate the diagnostic value and postoperative complication prediction value of NOX4 and MMP-2 in patients with DM complicated with NVG. Pearson method was applied to analyze the correlation of NOX4 and MMP-2 with vascular endothelial growth factor(VEGF)and interleukin-6(IL-6). Multivariate Logistic regression was applied to analyze the influencing factors of postoperative complications in the study group.RESULTS:The general information of the study group and control group of patients is comparable. Compared with the control group, the expression levels of serum NOX4, MMP-2, VEGF, and IL-6 in the study group were significantly increased(P<0.001). According to Pearson analysis, serum NOX4 and MMP-2 levels significantly positively correlated with VEGF and IL-6 levels, respectively(P<0.001). According to ROC curve, the AUC of NOX4 combined with MMP-2 in the diagnosis of DM complicated with NVG was better than that of individual diagnosis of NOX4(Z=3.341, P<0.05)and MMP-2(Z=2.788, P<0.05). The duration of DM, the proportion of people with intraocular pressure >21 mmHg, and the expression levels of NOX4, MMP-2, VEGF, and IL-6 in the poor prognosis group were all higher than those in the good prognosis group(P<0.001). The duration of DM, intraocular pressure >21 mmHg, the levels of NOX4, MMP-2, VEGF, and IL-6 were all risk factors for poor prognosis in DM patients with NVG(P<0.001). According to ROC curve, the combined prediction of NOX4 and MMP-2 for postoperative complications in patients with DM complicated by NVG was superior to the AUC predicted by NOX4(Z=3.727, P<0.05)and MMP-2(Z=2.219, P<0.05), respectively.CONCLUSION:NOX4 and MMP-2 are upregulated in the serum of patients with DM complicated by NVG. Combined detection of these two markers holds significant clinical value for the early diagnosis and prognosis of patients with DM complicated by NVG.

The reliability and practicality of research findings in health economics are gradually becoming core issues of close concern for clinical and public health experts. As healthcare resources remain under increasing pressure, conducting efficient cost-effectiveness analyses and achieving rational resource allocation are becoming ever more critical. In recent years, medical laboratory health economics has transitioned from purely academic discussions to integration into clinical practice, becoming a key tool for improving the efficiency and quality of healthcare services. It has demonstrated remarkable results in optimizing patient management and medical decision-making processes. So we invited experts from the fields of medical laboratory science and health economics to share valuable experiences and unique insights on topics such as cost assessment methods, pricing strategies, quality regulation, and the role of medical laboratory health economics in enhancing clinical practice and patient benefits. These experts generally agree that, while research in medical laboratory health economics has shown significant advantages and effectively addressed urgent clinical needs in resource allocation and cost control, it still faces multiple challenges, including limitations in research methodology application, high operational costs, and insufficient standardization of management systems.

The reliability and practicality of research findings in health economics are gradually becoming core issues of close concern for clinical and public health experts. As healthcare resources remain under increasing pressure, conducting efficient cost-effectiveness analyses and achieving rational resource allocation are becoming ever more critical. In recent years, medical laboratory health economics has transitioned from purely academic discussions to integration into clinical practice, becoming a key tool for improving the efficiency and quality of healthcare services. It has demonstrated remarkable results in optimizing patient management and medical decision-making processes. So we invited experts from the fields of medical laboratory science and health economics to share valuable experiences and unique insights on topics such as cost assessment methods, pricing strategies, quality regulation, and the role of medical laboratory health economics in enhancing clinical practice and patient benefits. These experts generally agree that, while research in medical laboratory health economics has shown significant advantages and effectively addressed urgent clinical needs in resource allocation and cost control, it still faces multiple challenges, including limitations in research methodology application, high operational costs, and insufficient standardization of management systems.