[The Predictive Value of Age, D-Dimer, and FIB in Non-Thrombotic Diseases].

Abstract

Objective: To explore the predictive value of age, D-Dimer and fibrinogen (FIB) for non-thrombotic.

Methods: A retrospective analysis was conducted on a total of 1 384 coagulation test cases from January to August 2024 at Nanning No. 8 People's Hospital. Among them, the control group comprised 400 non-thrombotic cases with D-Dimer test results within the reference range. The thrombotic group comprised 57 clinically diagnosed thrombotic patients. The research group comprised 927 non-thrombotic cases with D-Dimer levels exceeding the reference range. The diagnosis treatment records, age information, plasma D-Dimer, and FIB test results of each group were collected. The changes and correlations of age, D-Dimer, and FIB indicators were compared and analyzed among the three groups. A new combination factor was generated by fitting a Logistic binary regression model. ROC curves were used to evaluate the predictive value of each index for non-thrombotic disease in both the research group and the thrombotic group.

Results: Compared with the control group, the thrombotic group and the research group had significantly higher age, D-Dimer, and FIB levels (P < 0.001). Further comparative analysis showed that the research group had significantly lower age and D-Dimer levels than the thrombotic group, the FIB level was significantly higher than that of the thrombotic group (P < 0.001). Spearman correlation analysis showed that the correlation coefficient between age and D-Dimer in the research group was higher than that in the control group and thrombotic group (P < 0.01), the thrombotic group had the highest negative correlation coefficient between FIB and D-Dimer (P < 0.01). The ROC curve analysis results showed that the AUC values of age, plasma D-dimer, and FIB independently predicted non-thromb diseases were 0.726, 0.735, and 0.611, respectively. A new combined factor was generated by fitting age, D-dimer, and FIB with a logistic binary regression model. The AUC value of the combined prediction of non-thrombotic diseases was the maximum at 0.832, which had high diagnostic value, and its sensitivity and specificity were 0.572 and 0.070.

Conclusion: Elevated D-dimer levels were associated with age, increased FIB, and a variety of non-thrombotic diseases, and combination of age, D-dimer, and FIB had a certain predictive value for non-thrombotic diseases, but the combined model had a low specificity, other information needs to be combined in the clinic to improve diagnostic accuracy.