Influence factors for the anticoagulant quality of warfarin in patients with prosthetic heart valve and construction of a prediction model
Objective To determine the influence factors for warfarin anticoagulation quality in patients with prosthetic heart valve,to construct a prediction model for warfarin anticoagulation quality,and to identify the quality of low/medium level warfarin anticoagulation.Methods We collected the data of patients with prosthetic heart valve in a hospital from January 2020 to May 2023 and evaluated the anticoagulation quality of warfarin using the time within the therapeutic range as the indicator.Logistic regression equation to analyze the influence factors of warfarin anticoagulation quality,construct a prediction model for the anticoagulation quality in patient with prosthetic valve with a nomogram,and internally validate the model.Results Totally 236 patients were included,60 of which were in the high anticoagulation quality group and 176 were in the low/medium anticoagulation quality groups.The Logistic regression analysis showed that a history of combined myocardial infarction/ischemic stroke[Exp(B)=0.14,95％CI:0.02～0.87,P=0.04],and anxiety/depression status[Exp(B)=0.06,95％CI:0.08～0.89,P=0.01]were risk factors for high anticoagulation quality of warfarin in patients with prosthetic valve;high albumin levels[Exp(B)=1.13,95％CI:1.02～1.26,P=0.02],and high INR monitoring frequency[Exp(B)=10.92,95％CI:5.11～23.32,P＜0.01]were protective factors of high anticoagulant quality of warfarin in such patients.The nomogram constructed based on these factors predicted a C-index index of 0.94 in the model.Conclusion The prediction model of warfarin anticoagulation quality nomogram for patients with prosthetic valve has good discrimination,consistency,and clinical application value.It can identify individuals with low/medium levels of warfarin anticoagulant quality,conduct targeted pharmaceutical monitoring,and improve anticoagulation quality in patients.
prosthetic heart valvewarfarinanticoagulant qualitytime within the therapeutic rangeinfluence factornomogram prediction model
NETL
NSTL
万方数据
