冠状动脉慢血流发生的危险因素分析 及预测列线图构建.

Objective To analyze the risk factors for the occurrence of coronary slow flow (CSF) and to construct a predictive nomogram for the occurrence of CSF. Methods Totally 103 patients with CSF diagnosed by CAG examination were recorded as the CSF group. A total of 121 patients with normal blood flow in the same period were selected as the control group. The following data of the two groups were collected: age, gender, smoking history, drinking history, body mass index (BMI), blood pressure, past medical history and other basic information, blood potassium, blood sodium, blood phosphorus, blood magnesium, urea nitrogen, creatinine, uric acid, blood glucose, albumin, globulin, creatine kinase, creatine kinase isoenzyme, triglycerides, total cholesterol, free fatty acids, low-density lipoprotein (LDL), high-density lipoprotein (HDL), lipoproteins, leukocytes, monocytes, lymphocytes, neutrophils, hemoglobin, red blood cells, red blood cell volume, red blood cell width, hematocrit, platelets, platelet volume, platelet distribution width, Ddimer and other laboratory test information, end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), fractional shortening (FS) and other cardiac ultrasound examination results, the percentage of adjacent RR intervals ≥ 50 ms (PNN50), mean standard deviation of normal RR intervals per 5 min (SDNNI), standard deviation of average RR interval (SDNN), root mean square difference of consecutive normal RR intervals (RMSSD), standard deviation of the mean of all NN intervals within 5 min (SDANN), standard deviation of adjacent NN intervals (SDSD), triangular index (TI) and other indexes related to heart rate variability. The indexes with statistical differences in univariate analysis were included in least absolute shrinkage and selection operator (LASSO) regression and multivariate Logistic regression analysis using the "glmnet" package of R statistical software to screen characteristic variables and analyze the risk factors for CSF. The "rms" package of R statistical software was used to construct a prediction nomogram for CSF. The ROC curve of the nomogram was drawn to evaluate the discrimination of the nomogram. The calibration curve was used to evaluate the consistency of the nomogram. The clinical decision curve was used to evaluate the clinical value of the nomogram. Results There were statistically significant differences in the smoking history, hypertension, systolic blood pressure, diastolic blood pressure, BMI, uric acid, blood sugar, triglycerides, total cholesterol, LDL, HDL, lymphocytes, EDV, ESV, EF, FS, PNN50, RMSSD, SDSD, SDNN, SDANN, age, blood sodium, blood magnesium, neutrophils, red blood cell width, D-dimer, and TI between the CSF group and the control group (all P<0. 05). The results of LASSO regression and multivariate Logistic regression analysis showed that smoking, systolic blood pressure, triglycerides, lymphocytes, SDNN, and EF were independent risk factors for the occurrence of CSF (all P<0. 05) . Based on this, a prediction nomogram for the occurrence of CSF was constructed, and the nomogram had good discrimination, consistency, and clinical value. Conclusions Smoking, systolic blood pressure, triglycerides, lymphocytes, SDNN, and EF are independent risk factors for the occurrence of CSF. The prediction nomogram constructed based on the above risk factors has a high predictive value for the occurrence of CSF [ABSTRACT FROM AUTHOR]