Research On Predictors Of Fatigue Trajectory In Patients With Acute Myocardial Infarction

Objective:Fatigue levels in patients with first-episode Acute Myocardial infarction(AMI)were studied longitudinally on day 2 after admission(Acute admission,T1),1 month(T2),2 months(T3),and4 months(T4)after discharge.The latent category growth model(LCGM)was used to fit the types and characteristics of fatigue development trajectory of AMI patients.The influencing factors of fatigue trajectory of patients with different AMI were analyzed,and the predictive indexes of patients with different fatigue subgroups were discussed.Methods:In this study,a total of 206 first-onset AMI inpatients who met the standards of inpatient intake and discharge in the cardiovascular diagnosis and treatment center of a tertiary general hospital in Huzhou were selected by means of convenience sampling method from October 2019 to January2021 with longitudinal follow-up.The General Data Questionnaire,Multidimensional Fatigue Inventory-20(MFI-20),Generalized Anxiety Disorder Scale(GAD-2),2-item Patient Health Questionnaire(PHQ-2)and Athens insomnia scale(AIS),were distributed on the 2nd day after admission,and MFI-20,GAD-2,PHQ-2 and AIS were distributed again 1,2 and 4 months after discharge for follow-up investigation.Collect and input data,using SPSS22.0 software and Mplus8.3 software,first of all,using the method of repetitive measure analysis of variance analysis in patients with AMI fatigue level of overall development trend,again with potential category growth model fitting of trajectory of fatigue in patients with AMI potential category,USES the information index such as AIC and BIC,sample correction BIC,entropy,BLRT fitting and VLMR inspection evaluation results,determine the best fitting model;The predictive factors of fatigue trajectory of AMI patients were analyzed from general demographic data,disease-related data and physiological and psychological indicators.First,univariate analysis was used to screen statistically significant independent variables,and then multi-classification Logistic regression was used to analyze the predictors of different fatigue trajectories.Results:(1)The average total scores of the MFI-20 scale of AMI patients at the four time points were 57.87±13.90 points,57.87±13.90 points,41.52±12.33 points,41.34±12.15 points,respectively.The T1 score is the highest,and the T2,T3,and T4 scores gradually decrease after discharge.After latent category growth model analysis,it is determined that the data fitting results of the three latent category models are the best.At this time,the BIC value is 5538.453,the entropy value is 0.939,and the BLRT and VLMR tests are both statistically significant(P< 0.001).According to the intercept and slope,the three types of fatigue development trajectories were named as the obvious improvement group(C1),the slow relief group(C2),and the sustained fatigue group(C3),and the proportions in the total were 15.2% and 62.8%,respectively And22.0%;(2)Through single factor analysis,general demographic factors,disease-related factors,and physiological and psychological factors will all affect the fatigue level within 4 months after discharge.The statistically significant independent variables include: gender,Marital status,number of comorbidities,Killip,T1-T4 anxiety score,T1-T4 depression score,T1 and T3-T4 sleep quality score;(3)After multi-class logistic regression analysis,it was found that compared with the patients in the continuous fatigue group(C3),the predictors of the patient’s fatigue type developing into a significantly improved group(C1)include: anxiety T1 score,depression T4 score,and sleep quality T4 score,Its effect in the significant improvement group(C1)was 0.064 times(OR=0.064,95%CI 0.013-0.324),0.362 times OR=0.362,95%CI 0.178-0.737)and the sustained fatigue group(C3)respectively.0.366 times(OR=0.366,95%CI 0.145-0.926).Compared with the continuous fatigue group(C3),the predictive factors for the fatigue type of AMI patients to develop into the slow remission group(C2)mainly include male,the number of comorbidities <5,Killip,and the sleep quality T4 score,which is in the significantly improved group(C1)The effects were 8.400 times(OR=8.400,95%CI 2.204-32.021),11.021 times(OR=11.021,95%CI 2.793-43.493),11.665 times(OR=11.665,95%CI 1.726-78.856)and 0.366times(OR=0.366,95%CI 0.145-0.926)of the slow response group(C2).Conclusions:(1)The fatigue level of AMI patients presents a non-linear change pattern at different stages of the disease.AMI patients have three different development trajectories in the fatigue level within 4 months after discharge,namely the obvious improvement group(15.2%),the slow remission group(62.8%)and the continuous fatigue group(22.0%);Although the fatigue of most patients can be improved well,there are still a small number of patients whose fatigue level has not been effectively improved under treatment and care,and there is a state of continuous fatigue;(2)Gender,marital status,number of comorbidities,Killip,T1-T4 anxiety score,T1-T4 depression score,T1 and T3-T4 sleep quality scores and other related indicators will affect the development trajectory of AMI patients’ fatigue.Gender,number of comorbidities,Killip,T1 score for anxiety,T4 score for depression and T4 score for sleep quality play an important role in distinguishing and predicting different types of fatigue development trajectories,and provide a reference for early identification and screening of high-risk fatigue patients in the future.