Objective:To study the independent risk factors for the short-term prognosis of patients with atrial fibrillation in the ICU from the MIMIC-Ⅲ database,established a predictive model,and veri-fied the model.Methods:In the MIMIC-Ⅲ database,6 555 research subjects were included through inclusion and exclusion screening criteria.The basic clinical data,laboratory data,time of admission and discharge,death records,and in-hospital deaths of patients were extracted from the database,and the 90-day all-cause mortality of patients was recorded.The independent risk factors for 90-day all-cause mortality in critically ill patients with atrial fibrillation were analyzed using the Cox risk re-gression model.The prediction model was established based on the Cox analysis results,and the No-mogram column chart was drawn.Meanwhile,the ROC was analyzed,and the AUC was calculated.After internal verification,the calibration curve was performed.Results:A total of 1 639 patients died(death group)and 4 916 survived(survival group)within 90 days.The baseline characteristics in the prediction model suggested that the death group was different from the survival group in age,gender,congestive heart failure,electrolyte imbalance,chronic lung diseases,and valve diseases(P<0.05).In addition,significant differences were also found in the AG,serum Cr,HCT,PLT,BUN,WBC,RDW,length of stay,and disease severity scores(SAPSⅡ,OASIS,and SOFA)(P<0.05).COX regression prediction model multivariate analysis suggested that age,congestive heart failure,valvular diseases,electrolyte disorder,and chronic lung diseases were risk factors for death in critically ill pa-tients with atrial fibrillation within 90 days.The values within 24 hours from admission,such as RDW,AG,PLT,and HCT,could be used to predict short-term death.The length of hospital stay and SAPSⅡ scores could significantly reflect the short-term risk of patients.According to the analy-sis results of the Cox regression model,the prediction model was Y=1.116×X1+1.064×X2+1.283×X3+1.199×X4+1.165×X5+1.246×X6(X1=RDW,X2=AG,X3=congestive heart failure,X4=valvular diseases,X5=chronic lung diseases,X6=electrolyte disorder and acid-base im-balance).ROC analysis of the model and other independent predictors showed that the AUC of the model was 0.753(P<0.001).Based on Cox regression analysis,a Nomogram was drawn,and its C-index=0.719.Finally,we performed internal bootstrap validation and drew calibration curves.Conclusion:The predictive model is a good predictor for 90-day all-cause mortality risk for critically ill patients with concomitant atrial fibrillation.
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