Establishment and evaluation of 90-day survival prediction model for the patients with acute heart failure

Zheng Liangliang; Wang Junjie; Wang Fan; Quan Jinhua; Chen Xi; Wen Wei

doi:10.3969/j.issn.1002-1949.2025.07.007

Abstract：Objective To establish a medium-and short-term survival prediction model for the patients with acute heart failure(AHF)assisted by machine learning algorithms and to analyze and verify the prognostic factors.Methods The clinical data of AHF patients admitted to the Emergency Department of Beijing Hospital from July 2021 to February 2025 were retrospectively analyzed,including demographic information,underlying diseases,vital signs,comorbidities,laboratory test indicators,diagnosis and treatment within 7 days,etc.According to the clinical outcome of 90-day follow-up,they were divided into survival group and death group.L1 regularized Logistic regression analysis,random forest method and extreme gradient boosting method were used in the training set,and clinical data and its first-order data were included.Prediction models were established based on survival outcomes.Predictive efficiency of the model was evaluated,internal verification was conducted in the test set,and the main clinical factors affecting prognosis were screened,and the differences in the factors affecting the survival status of different times were analyzed,then quantitative analysis and survival analysis were performed on the top-ranked parameters in the model to quantify their predictive efficiency.Results Based on the survival status 90 days after the treatment,the patients were divided into the survival group with 102 cases and the death group with 66 cases.There were significant differences in the univariate between the survival group and the death group.The random forest method showed the best predictive results in the 90-day prognosis model,with the area under curve(AUC)of 0.782,the accuracy was 0.667,the sensitivity was 0.838,and the specificity was 0.600.The top five factors in the importance of the model were D-dimer on 7th day,diastolic blood pressure on 3rd day,troponin I on 7th and 5th day,and N-terminal probrain natriuretic peptide(NT-proBNP)on 7th day.Further exploration found that the factors ranked the top importance in other models of 90 days and the prediction models of 30,60 and 90 days had high overlap.Urea nitrogen and D-dimer on 7th day were ranked the top two in multiple models.Then,the predictive value of urea nitrogen and D-dimer on 7th day for 90-day survival was quantified by Logistic regression analysis,and it was found that both had independent predictive value and the combined predictive value was greater.Survival analysis found that the level of two parameters had a significant impact on survival rate.Conclusions This study establishs a 90-day survival prediction model for the patients with AHF based on common clinical factors through machine learning algorithms,and further quantifies and verifies the good predictive efficacy of key indicators for survival.The model shows that urea nitrogen and D-dimer on 7th day after admission for AHF patients had high predictive value in their 90-day survival prognosis,with similar trends in the shorter-term prognosis(30-day and 60-day)predictions.