Objective:To investigate the relationship between metabolic syndrome and 1-year poor outcome in elderly patients with acute cerebral infarction (ACI).Methods:The clinical data of elderly patients with ACI admitted to Renqiu Kangjixintu Hospital from January 2014 to November 2018 were selected and divided into metabolic syndrome group (931 cases) and non-metabolic syndrome group (1 851 cases). The clinical data of the two groups of elderly patients with ACI were compared, and the effect of metabolic syndrome on poor outcome (modified Rankin scale>2 scores) of elderly patients with ACI in 1 year was analyzed by multivariate Logistic regression.Results:The proportion of female, hypertension, diabetes, hyperlipidemia, coronary heart disease, smoking, excessive alcohol consumption and antiplatelet drug use in the metabolic syndrome group were higher than those in the non-metabolic syndrome group: 52.74%(491/931) vs. 32.58%(603/1 851), 79.16%(737/931) vs. 64.29% (1 190/1 851), 42.32% (394/931) vs. 6.43% (119/1 851), 17.19% (160/931) vs. 11.62% (215/1 851), 18.90% (176/931) vs. 14.10% (261/1 851), 62.73% (584/931) vs. 50.89% (942/1 851), 3.73% (69/931) vs. 1.61% (15/1 851), 19.23% (179/931) vs. 15.51% (287/1 851), the levels of body mass index, systolic blood pressure, diastolic blood pressure, fasting plasma glucose (FPG), fasting plasma glucose (TG), total cholesterol (TC), platelet (PLT), fibrinogen (FIB), fall score were higher than those in non-metabolic syndrome group: 26.67 (25.31, 28.60) kg/m 2 vs. 23.30 (21.48, 24.91) kg/m 2, (167.17 ± 22.96) mmHg (1 mmHg = 0.133 kPa) vs. (164.21 ± 24.90) mmHg, (87.06 ± 13.10) mmHg vs. (85.76 ± 12.99) mmHg, (7.33 ± 2.64) mmol/L vs. (5.35 ± 1.38) mmol/L, (2.12 ± 1.51) mmol/L vs. (1.13 ± 0.78) mmol/L, (4.97 ± 1.31) mmol/L vs. (4.65 ± 0.99) mmol/L, 213.00 (179.00, 256.00) × 10 9/L vs. 203.00 (172.00, 241.00) × 10 9/L, 3.07 (2.63, 3.52) g/L vs. 2.94 (2.55, 3.37) g/L, (6.12 ± 1.70) scores vs. (5.93±1.74) scores, the levels of age, high density lipoprotein cholesterol (HDL-C), homocysteine (Hcy) and pressure ulcer score were lower than those of non-metabolic syndrome group: (69.29 ± 6.96) years vs. (71.28 ± 7.66) years, (0.98 ± 0.34) mmol/L vs. (1.31 ± 0.88) mmol/L, (18.93 ± 13.07) mmol/L vs. (21.66 ± 16.39) mmol/L, (18.55 ± 2.42) vs. (19.02 ± 2.43), with statistical significance ( P<0.05). After 1-year follow-up, the proportion of poor outcomes in the metabolic syndrome group was higher than that in the non-metabolic syndrome group: 21.70%(202/931) vs. 18.69% (346/1 851), with statistical significance ( P<0.05). Multivariate Logistic regression analysis showed that age, stroke, national institutes of health stroke scale (NIHSS) score at admission, systolic blood pressure, Hcy, pressure ulcer score, fall score, metabolic syndrome were independent risk factors for poor outcome of ACI in 1 year ( OR = 1.056, 1.309, 1.138, 1.005, 1.006, 0.882, 1.076 and 1.285; 95% CI 1.040 to 1.072, 1.037 to 1.652, 1.097 to 1.180, 1.000 to 1.010, 1.000 to 1.013, 0.834 to 0.933, 1.004 to 1.152 and 1.001 to 1.657; P<0.05). Conclusions:Multiple risk factors for stroke are closely related to poor outcome of ACI in the elderly. And metabolic syndrome is an independent risk factor for poor outcome of ACI in the elderly in 1 year.

With the increasing cost of drug development and clinical trials, it is of great value to make full use of all kinds of data to improve the efficiency of drug development and to provide valid information for medication guidelines. Model-based meta-analysis (MBMA) combines mathematical models with meta-analysis to integrate information from multiple sources (preclinical and clinical data, etc.) and multiple dimensions (targets/mechanisms, pharmacokinetics/pharmacodynamics, diseases/indications, populations, regimens, biomarkers/efficacy/safety, etc.), which not only provides decision-making for all key points of drug development, but also provides effective information for rational drug use and cost-effectiveness analysis. The classical meta-analysis requires high homogeneity of the data, while MBMA can combine and analyze the heterogeneous data of different doses, different time courses, and different populations through modeling, so as to quantify the dose-effect relationship, time-effect relationship, and the relevant impact factors, and thus the efficacy or safety features at the level of dose, time and covariable that have not been involved in previous studies. Although the modeling and simulation methods of MBMA are similar to population pharmacokinetics/pharmacodynamics (Pop PK/PD), compared with Pop PK/PD, the advantage of MBMA is that it can make full use of literature data, which not only improves the strength of evidence, but also can answer the questions that have not been proved or can not be answered by a single study. At present, MBMA has become one of the important methods in the strategy of model-informed drug development (MIDD). This paper will focus on the application value, data analysis plan, data acquisition and processing, data analysis and reporting of MBMA, in order to provide reference for the application of MBMA in drug development and clinical practice.