Acute Coronary Syndrome Death Factor Analysis And Model Construction From Nomogram

Objective To explore the analysis of risk factors for death in patients with acute coronary syndrome(ACS)and to construct a Nomogram risk prediction model to provide a basis for early identification of high-risk groups for treatment.Methods From June 2020 to March 2021,814 patients with acute coronary syndrome who met the diagnostic criteria of ACS and visited the General Hospital of Ningxia Medical University general were collected baseline data and clinical characteristics.The study and control groups were divided according to the presence or absence of all-cause mortality during the 6-month period.Comparing the two groups in terms of general baseline data,laboratory tests,ECG,cardiac function and adverse cardiovascular events and other indicators,using univariate analysis and multi-factor Logistic regression analysis analyses to screen for risk factors affecting mortality in patients with ACS,the Nomogram risk prediction model was constructed by R language.Finally,the Area under the curve(AUC),ROC curve and calibration curve were used to evaluate the model differentiation and consistency,respectively.Results1.A total of 814 patients with ACS were included in this study,the majority of patients(52.6%)with unstable angina(UA),111 patients(13.6%)with non-ST-segment elevation myocardial infarction(NSTEMI),and 275 patients(33.8%)with ST-segment elevation myocardial infarction(STEMI).All-cause mortality occurred in 33 patients(4.2%),among them,STEMI patients were the majority(75.8%),UA patients 2(6.1%)and NSTEMI patients6(18.2%).Overall ACS had a high proportion of males(68.9%)and 253 females(31.1%);among the patients who died,there was a high proportion of females(63.6%)and 12 males(36.4%).2.Compared with the control group,more patients in the study group were older,had a faster heart rate,were more female,had lower blood pressure and a higher Killip classification,and had a previous history of combined cerebrovascular disease,STEMI patients were more predominant,with all GRACE scores being high risk(P<0.05);Fasting blood glucose(FPG)and serum creatinine(Scr)levels were higher in the study group compared to the control group(P < 0.05);The values of white blood cell count(WBC),neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR)and troponin I(cTnI)were significantly higher in the study group than in the control group(P < 0.05);Left ventricular ejection fraction(LVEF)levels were lower than normal in the study group,but N-terminal B-type natriuretic peptide precursor(NT-proBNP)and left ventricular end-systolic internal diameter(LVESD)were significantly higher(P < 0.05);The study group had a higher QTC interval and QRS wave duration than the control group,and a higher proportion of ventricular arrhythmias occurred(P < 0.05);The study group had a higher prevalence of preoperative blood flow ≤ TIMI class Ⅱ and a higher incidence of postoperative slow flow and no recurrent flow,and the proportion of percutaneous coronary intervention(PCI)performed after admission was significantly lower compared to the control group(P <0.05);The incidence of acute heart failure,ventricular wall tumor,shock,cardiac arrest,cardiac rupture,septal perforation,and papillary muscle rupture was higher in the study group than in the control group(P < 0.05).3.According to the multifactorial logistic regression analysis,Scr,LVEF,ventricular fibrillation,failure to perform PCI after admission,acute heart failure,and cardiac arrest were found to be independent risk factors for ACS death(p < 0.05).4.Scr,LVEF,ventricular fibrillation,failure to perform PCI after admission,acute heart failure,and cardiac arrest six variables were included in the Nomogram model to quantitatively assess the risk of death in patients with ACS.5.The Nomogram model was constructed based on the above factors,and the area under the model curve(AUC)was 0.959(95%CI=0.930-0.988),and the calibration curve overlapped well with the ideal curve,indicating that the model had good discrimination and calibration.6.Based on the scores corresponding to each variable on the Nomogram model,the total score of the risk of death occurring in each patient was calculated and divided into low risk,intermediate risk and high risk groups by K-means clustering,and the survival curves showed that the prediction models differed in the prognosis of survival in the three risk strata(Log Rank P < 0.01).Conclusion1.Elevated Scr,reduced LVEF,ventricular fibrillation,failure to perform PCI after admission,acute heart failure,and cardiac arrest were independent risk factors for death from ACS.2.The Nomogram prediction model constructed in this paper has good predictive efficacy for the risk of death in ACS patients and can provide an individualized and effective predictive tool for early clinical decision making in ACS patients and their prognosis.